Dynamic simulation periods are specified in Time's definition. This is usually a list of numbers or labels, typically in some unit of time (days, weeks, months, etc.). Use the ÒDynamic()Ó function in your variables to perform dynamic simulation.2,450,279,476,409100020114980120120LogComposite traffic v. 1.8 - Health and costs in the Helsinki metropolitan areaThis model is a decision analysis in a poorly studied area, trip aggregation, and it studies decisions of two different stakeholders, the passenger and the society. In composite traffic, a centralised system collects the information on all trips online, aggregates the trips with the same origin and destination into public vehicles with eight or four seats, and sends the travel instructions to the passengers' mobile phones. We show here that in an urban area with one million inhabitants, this system could reduce environmental and other pressures of car traffic typically by 50-70 %, would attract about half of the car passengers, and within a broad operational range needs no public subsidies. Composite traffic gives a new level of freedom in urban decision-making towards solving the problems of urban traffic.
The model is built using Analytica 3.0(TM) program that utilises a graphical interface for creating probabilistic (Monte Carlo) models. A free browser can be downloaded from the Analytica web site http://www.lumina.com . The file format for the models is XML, and therefore the code can also be viewed with a regular web browser.
In this material, we present the main views of the graphical model and describe several modules in more detail. The model consists of two parts: a deterministic trip aggregation model that produces the output tables used in decision analysis. The calculation of the results takes several days and therefore they are stored as static tables in the module 'Static nodes'. In the second part the aggregation results are combined with cost functions, emission factors and other uncertain and/or varying variables using probabilistic (Monte Carlo) simulation. This part of the model is readily available for detailed examination, and several input values can be changed and explored using the Analytica Browser. Note, however, that the model (depending on dimensions used) easily requires more than 1 GB of RAM memory.jtue (Jouni Tuomisto)7. Novta 2002 13:32jtue22. huhta 2009 16:55 48,241,12,10,832,535,212,10,87,476,467Arial, 130,Model Composite_traffic_v_,2,2,0,1,N:\YMAL\Users\Jouni\Composite_traffic_1_8_2c.ANA81,1,1,0,2,1,4900,6400,72,40,7,450,720Timing profile1140,164,1132,121,0,0,1,0,0,0,72,0,1Timing_profileFrom0140,20,1132,121,0,0,1,0,0,0,72,0,1FromInput varIndex for variables that may affect the number of composite traffic trips.['Car fraction','Public fraction','Guarantee level','Large guarantee?','Public matrix','Public level','No-change fraction','Max size','Min direct load','Vehicle types','Drop points/area']360,240,148,122,102,90,476,320['Car fraction','Public fraction','Guarantee level','Large guarantee?','Public matrix','Public level','No-change fraction','Max size','Min direct load','Vehicle types','Drop points/area']Scenario inputInput variable values for base case scenario. If Large guarantee? is 'Yes', then it is assumed that the guarantee covers the whole area, if the origin OR the destination of the trip are in the guaranteed area. Otherwise, both O and D must be in the covered area.Table(Input_var)(
0.5,1,7,0,2,0,0,8,4,2,8)['Composite fraction','Guarantee level','Lim']360,208,148,242,102,90,476,2242,11,333,217,303,0,MIDM52425,39321,65535[Self,Scenario]Trip aggregationThis module calculates the actual trips, modes of transportation, and delays during trips and vehicle transfers. It also calculates the kilometres traveled by each type of vehicle and number of vehicles needed.
The composite traffic trips are allocated into different vehicles. The following hierarchy is used in allocation. If the criterion is fulfilled, that number of passengers is allocated, and the rest will go to the next criterion. The criteria are used for a group of trips that has the same origin, destination, and time. Time resolution is 12 min. Origin and destination are described as '129-areas' used for city authorities in Helsinki metropolitan area. The 129 areas have on average 7300 inhabitants (0, 25%, 50%, 75%, and 100% percentiles are 0, 3400, 6800, 10300, and 28300, respectively).
1) Use an 8-seat vehicle if there are enough passengers to get it full.
2) Use a 4-seat vehicle if there are enough passengers to get it full.
Divide the trips into two parts so that the passengers change vehicle in the most busy point along the route. Then,
3) Use an 8-seat vehicle if there are enough passengers to get it full.
4) Use a 4-seat vehicle if there are enough passengers to get it full.
5) Use a 4-seat vehicle for all remaining trips.
The criterion is checked at the actual arrival time at the transfer point, i.e. the model takes into account the different travel times between areas.
The following outputs are calculated:
Number of passenger trips by mode (car or composite traffic)
Number of passenger trips by vehicle type. Note that in this output, the trip that includes a transfer is calculated twice.
Vehicle kilometres driven
Parking lots needed for the vehicles that are used
Average vehicle numbers per hour for the 30 most busy links at 8.00-9.00 in the morning
Number of vehicles needed
Waiting time due to traffic jams and waiting for composite vehicle to arrive.
The outputs of each scenario are indexed (when relevant) by period (day, evening, night); zone (Helsinki downtown, other centre, suburb), length of trip (less or more than 5 km), and vehicle type (8-seat or 4-seat vehicle with of without transfer, or car).jtue6. helta 2003 18:5548,24480,136,148,241,61,49,604,539,172,102,90,476,451100,170,170,770,1,9,6798,4744,7,0Vehicle typesTable(Self,Vehicle)(
'Minibus','Minibus','Minibus','Minibus','Minibus','Minibus','Minibus','Minibus','Minibus','Minibus','Minibus','Minibus','Minibus','Minibus','Minibus','Minibus','Minibus','Minibus',
'Minibus','Minibus','Minibus','Minibus','Minibus','Minibus','Car (d)','Car (d)','Car (d)','Minibus','Minibus','Minibus','Minibus','Minibus','Car (d)','Car (d)','Car (d)','Car (d)',
'Minibus','Minibus','Minibus','Minibus','Minibus','Minibus','Car (d)','Car (d)','Car (d)','Minibus','Minibus','Minibus','Minibus','Minibus','Minibus','Car (d)','Car (d)','Car (d)'
)[1,2,3]56,224,148,242,531,17,416,475,0,MIDM2,40,50,416,413,0,MIDM52425,39321,65535[Self,Vehicle][Self,Vehicle]Delaytime unitsTravel time between two city areas. It includes the time that is spent in the composite vehicle when it drives within the origin or destination area picking up or dropping off other passengers. However, the travel times of composite vehicles and car are estimated to be so close to each other that the same value is used for both. (In any case, the resolution is 12 min anyway).ceil(Distances[mode1='Car', time_of_day1='Morning']/Traffic_speed/time_unit)56,152,148,242,262,247,476,3242,414,130,694,363,0,MIDM[From,To1]Vehicle sizepassengersSize of vehicles that is used to allocate passengers into vehicles. For cars, the average number of passengers is 1.345 (See Car occupancy). A slightly higher number is used here, because with low volumes (1-4 passengers) the need of cars is overestimated if the actual number is used. Even if the higher number overcompensates this and causes bias, it is in favour of personal cars.Table(Vehicle)(
9,8,7,6,5,4,3,2,1,9,8,7,6,5,4,3,2,1)168,184,148,241,1,1,1,1,1,0,,0,2,9,108,476,4062,88,98,416,398,0,MIDM52425,39321,65535[Hellman, 2004 54 /id]
Tripstrips/time unitThe composite traffic trips are allocated into different vehicles. The following hierarchy is used in allocation. If the criterion is fulfilled, that number of passengers is allocated, and the rest will go to the next criterion. The criteria are used for a group of trips that has the same origin, destination, and time.
1) Use an 8-seat vehicle if there are enough passengers to get it full.
2) Use a 4-seat vehicle if there are enough passengers to get it full.
Divide the trips into two parts so that the passenger changes vehicle in the most busy point along the route. Then,
3) Use an 8-seat vehicle if there are enough passengers to get it full.
4) Use a 4-seat vehicle if there are enough passengers to get it full.
5) Use a 4-seat vehicle for all the remaining trips.
The criterion is checked at the actual arrival time at a transfer point, i.e. the model takes into account the different travel times between areas.var v:= Transfer_point;
var b:= All_trips[Mode1='Composite'];
var h:= 0;
var y:= 1;
var out:= 0;
var yy:= if findintext('d',vehicle_noch)=1 then 1 else 0;
yy:= if evaluate(selecttext(vehicle_noch,2))<= Scenario_input[input_var='Max size'] and evaluate(selecttext(vehicle_noch,2))>= Scenario_input[input_var='Min direct load'] then yy else 0;
yy:= subset(yy);
while y<=size(yy) do (
var s:= evaluate(selecttext(slice(yy,y),2));
h:= mod(b,s);
out:= if ('d'&s)=Vehicle_noch then b-h else out;
b:= h;
y:= y+1);
var noch:= round(b*scenario_input[input_var='No-change fraction']);
b:= b-noch;
var changed:= b;
var a:= From&','&To1;
var j:= if v=a then b else 0;
b:= b-j;
a:= ','&To1;
{laskee alkumatkan matkasuoritteen}
var d:= for x[]:= a do (
var c:= (if findintext(From&x,v)>0 then b else 0);
c:= sum(c,To1) );
{siirtää matkasuoritetta alkumatkan viipeen verran.}
var e:= selecttext(v,6,9);
e:= for x[]:= evaluate(e) do delay[To1=x];
b:= time_shift(b,e);
{laskee loppumatkan matkasuoritteen}
a:= From&',';
b:= for x[]:= a do (
var c:= (if findintext(x&To1,v)>0 then b else 0);
c:= sum(c,From) );
b:= j+d+b;
b:= b+noch;
y:= 1;
yy:= if findintext('c',vehicle_noch)=1 then 1 else 0;
yy:= if evaluate(selecttext(vehicle_noch,2))<= Scenario_input[input_var='Max size'] then yy else 0;
yy:= subset(yy);
while y<=size(yy) do (
var s:= evaluate(selecttext(slice(yy,y),2));
h:= mod(b,s);
out:= if ('c'&s)=Vehicle_noch then b-h else out;
b:= h;
y:= y+1);
if Vehicle_noch='Noch' then noch else out280,152,148,242,460,19,476,6072,25,71,468,508,0,MIDMGraphtool:0
Distresol:10
Diststeps:1
Cdfresol:5
Cdfsteps:1
Symbolsize:6
Baroverlap:0
Linestyle:1
Frame:1
Grid:1
Ticks:1
Mesh:1
Scales:1
Rotation:45
Tilt:0
Depth:70
Frameauto:1
Showkey:1
Xminimum:0
Xmaximum:1
Yminimum:0
Ymaximum:1
Zminimum:0
Zmaximum:1
Xintervals:0
Yintervals:0
Includexzero:0
Includeyzero:0
Includezzero:0
Statsselect:[1,1,1,1,1,0,0,0]
Probindex:[0.05,0.25,0.5,0.75,0.95]
[Time,To1][Index Mista]Total vehicle needvehiclesTotal number of vehicles needed to run the system. It is assumed that cars can be used in a similar way as composite vehicles, i.e. that if a car is parked, anyone can take and use it. This is of course unrealistic, but the bias is in the favour of car travelling. In addition, this number is not used for the final car need calculations.var a:= cumulative_balance;
var driving:= -sum(a,from);
a:= a-min(a,time);
a:= sum(a,from)+driving;
a:= max(a,time);
if vehicle_type= 'Bus' then bus_need else a392,352,148,242,518,118,476,3052,15,21,372,202,0,MIDM[Vehicle_type,Time_of_day1][Index Travel_type]Areal vehicle peakvehiclesThe highest number of vehicles during the observation period in each area. This excludes vehicles that are driving through the area. This is a proxy of parking lot need in the area. For practical reasons, the numbers are aggregated into zone level.
It is assumed that cars can be used in a similar way as composite vehicles, i.e. that if a car is parked, anyone can take and use it. This is of course unrealistic, but the bias underestimates the parking lot need in favour of car travelling. It is also assumed that composite vehicles and cars use separate parking areas. In this way the beforementioned bias does not affect the estimate for composite traffic.var a:= cumulative_balance;
a:= max(a,time)-min(a,time);
aggr_zone(a)392,288,148,242,25,35,476,4602,8,6,365,180,0,MIDM[Zone,Vehicle_type][Index Region2]36,1,1,0,1,9,6798,4744,7Link intensityvehicles/hThe average number of vehicles per hour driving along a link for the 30 most busy links at 8.00-9.00 in the morning. Note that each street consists of two links going to opposite directions.var a:= if rank(-Trips_per_link_bau1,link1)<30 then 1 else 0;
sum(a*Vehicles_per_link1,link1)/sum(a)392,416,148,242,385,178,476,3842,12,422,360,163,0,MIDMTransfer intensitypassengers/dThe number of transfers (changing composite vehicle in the middle of a trip) in each area.var a:= if vehicle_noch='Noch' then 0 else Trips;
a:= sum(a,vehicle_noch);
a:= sum(a-all_trips[Mode1='Composite'],time);
var fro:= sum(a,To1);
var to:= sum(a,from);
fro+to[to1=from]168,88,148,242,109,186,476,4252,781,43,296,405,0,MIDM[To1,From]Trips per hourtrips/hTotal number of trips travelled per hour in the whole area.var a:= Trips[vehicle_noch=vehicle];
a:= sum(sum(a,From),To1)/time_unit;
a56,88,148,242,517,83,476,4102,25,49,676,547,0,MIDMGraphtool:0
Distresol:10
Diststeps:1
Cdfresol:5
Cdfsteps:1
Symbolsize:6
Baroverlap:0
Linestyle:10
Frame:1
Grid:1
Ticks:1
Mesh:1
Scales:1
Rotation:45
Tilt:0
Depth:70
Frameauto:1
Showkey:1
Xminimum:0
Xmaximum:1
Yminimum:0
Ymaximum:7.25M
Zminimum:1
Zmaximum:2
Xintervals:0
Yintervals:0
Includexzero:0
Includeyzero:0
Includezzero:0
Statsselect:[1,1,1,1,1,0,0,0]
Probindex:[0.05,0.25,0.5,0.75,0.95]
Arial, 8[Vehicle,Time]Cumulative balancevehiclesCumulative net balance of vehicles and its development in time. This could take into account the compensative gap filling, i.e. if there is shortage of composite vehicles, empty vehicles are transported into the area. However, in the current version, it is assumed that empty vehicles are not transported. Because of this, there must be enough vehicles in each area so that it will not run out of them at any time of the day.dynamic(0,Cumulative_balance[time-1]+Vehicle_balance)280,352,148,242,102,90,476,3692,178,119,756,399,0,MIDM[Time,From][Index From]Vehicle balancevehicles/time unitNumber of vehicles coming to and leaving each area, i.e. the net balance of the area for each time point. Assumes 1.5 trips per private car. Assumes that all gasoline cars are private cars.var b:= vehicle_by_type;
b:= if vehicle_type='Car (g)' then ceil(all_trips[mode1='Car']/1.5) else b;
var a:= time_shift(b,delay+1);
a:= sum(a,From);
a:= a[To1=From];
a:= -sum(b,To1)+a;
a280,288,148,242,62,35,476,6492,248,12,694,438,0,MIDM[Time,From][Index From]Vehicle kmkm/time unitNumber of vehicle kilometres driven during each time unit. Assumes 1.5 trips per private car. Assumes that all gasoline cars are private cars.var a:= vehicle_by_type;
a:= if vehicle_type='Car (g)' then ceil(all_trips[mode1='Car']/1.5) else a;
a:= aggr_period(a);
a:= a*distances[mode1='Car', time_of_day1='Morning'];
a:= aggr_zone(aggr_length(a));
a:= a[mode1='Composite'];
if length='< 5 km' and zone=1 and vehicle_type='Bus' then bus_km else a168,272,148,242,368,50,476,4452,23,341,591,469,0,MIDM[Vehicle_type,Period][Sysvar Time]88,1,1,0,2,9,4744,6798,7WaitingminCalculates the waiting time for composite traffic. First, we calculate the number of vehicles running between each points at each time. This is calculated for short (< 5 km) and long trips separately. We assume that the vehicles run at relatively regular intervals, and then the expected waiting time is half of the time difference between the vehicles. Then we sum over areas and aggregate over time, and calculate the trip-number-weighted waiting time.var siz:= if vehicle_noch='Noch' then 1 else vehicle_size[vehicle=vehicle_noch];
index a:= (4..max(drop_points*5))/5;
a:= drop_points[area1=from]/Sqrt(a);
a:= if a=inf then 0 else a;
a:= a*(1-((a-1)/a)^siz)*drop_length[area1=from];
a:= a +(if findintext('c',vehicle_noch)=1 then time_unit*60/2 else 0);
a:= if vehicle_noch='Noch' then 0 else a;
var n:= sum(vehicle_by_type,vehicle_type);
n:= drop_points[area1=from]/Sqrt(n);
n:= if n=inf then 0.8 else round(n*5)/5;
a:= a[.a=n];
var per:= if time>=6 and time<20 then ' 6.00-20.00' else
if time>=20 and time<24 then '20.00-24.00' else ' 0.00- 6.00';
var x:= 1;
var c:= 0;
while x<= size(time) do (
var b:= slice(a,time,x)*slice(trips,time,x);
c:= if slice(per,time,x)=period then c+b else c;
x:= x+1);
a:= trips_by_type(aggr_zone(aggr_length(c)));
a:= a/composite_trips;
if isnan(a) then 0 else a512,184,148,242,26,19,474,6512,2,11,470,460,0,MIDMGraphtool:0
Distresol:10
Diststeps:1
Cdfresol:5
Cdfsteps:1
Symbolsize:6
Baroverlap:0
Linestyle:1
Frame:1
Grid:1
Ticks:1
Mesh:1
Scales:1
Rotation:45
Tilt:0
Depth:70
Frameauto:1
Showkey:1
Xminimum:0
Xmaximum:1
Yminimum:0
Ymaximum:1
Zminimum:0
Zmaximum:1
Xintervals:0
Yintervals:0
Includexzero:0
Includeyzero:0
Includezzero:0
Statsselect:[1,1,1,1,1,0,0,0]
Probindex:[0.05,0.25,0.5,0.75,0.95]
[Period,Vehicle_type][Index Vehicle]11.7.2006 Jouni Tuomisto
Ennen oli tämmöinen hieno yhteenveto, mutta sitten keksin paljon yksinkertaisemman, ja muistitarve romahti.
var e:= for x:= waiting_time do (
var d:= if round(a)=x then c else 0;
d:= trips_by_type(aggr_period(d));
d:= aggr_zone(aggr_length(d)));
sum(e*waiting_time,waiting_time)/sum(e,waiting_time)
13.7.2006 Jouni Tuomisto
Silti muistia kului liikaa tällä koodilla:
{var siz:= if vehicle_noch='Noch' then 1 else vehicle_size[vehicle=vehicle_noch];
var a:= if vehicle_noch='Noch' then 0 else trips;
a:= ceil(a/siz);
var n:= drop_points[area1=from]/Sqrt(a);
a:= if a>0 then n*(1-((n-1)/n)^siz)*drop_length[area1=from] else 0;
a:= if findintext('c',vehicle_noch)=1 then ceil(time_unit*60/2)+a else a;
a:= trips_by_type(aggr_zone(aggr_length(aggr_period(a*trips))));
a/composite_trips}{missing ')'}
Yritin siis vielä viilata sujuvammaksi, mutta ei pääse mihinkään siitä, että tässä pitää pyörittää taulua jonka ulottuvuudet ovat from*to1*time*vehicle_noch. Pitämällä Vehicle_nochin mahdollisimman lyhyenä säästetään tietysti muistia.
Waiting timeA dummy index1..20512,216,148,12[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20]ZonesThe areas are classified into three categories: 1) downtown (downtown of Helsinki), 2) centre (other major centres within the Metropolitan area), and 3) suburb (all other areas).Table(Area1)(
1,1,1,1,2,2,2,2,2,2,2,2,3,3,2,3,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,2,3,3,3,3,2,2,3,3,3,3,3,3,3,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,2,3,3,3,3,3,3,3,2,3,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,0)512,32,148,242,782,11,215,614,0,MIDM52425,39321,65535All trips2tripsTotal composite and car trips classified into zones and periods. This is the number of original trips, which is divided into car and composite trips. Compare Trips.v by zone.aggr_zone(aggr_length(aggr_period(All_trips)))512,352,148,242,403,72,476,3712,253,263,718,295,0,MIDM[Zone,Period][Index Length]OutputsThe combined result of various variables using the basic assumptions. This output is copied to the module 'Static nodes' and subsequently used as the basis for cost calculations.var a:= array(zone,[total_vehicle_need,0,0]);
var b:= array(zone,[link_intensity,0,0]);
a:= array(period,[areal_vehicle_peak,b,a]);
a:= array(length,[a,0]);
b:= all_trips2;
b:= array(vehicle_type,[
slice(b,mode1,3),
slice(b,mode1,2),0,
slice(b,mode1,1)]);
array(output1, [composite_trips,b,nochange_trips,vehicle_km,a,waiting])512,272,148,242,32,126,475,3552,627,251,487,311,0,MIDM[Alias Bau_scenario_output1, Formnode Outputs2]Graphtool:0
Distresol:10
Diststeps:1
Cdfresol:5
Cdfsteps:1
Symbolsize:6
Baroverlap:0
Linestyle:1
Frame:1
Grid:1
Ticks:1
Mesh:1
Scales:1
Rotation:45
Tilt:0
Depth:70
Frameauto:1
Showkey:1
Xminimum:0
Xmaximum:1
Yminimum:0
Ymaximum:1
Zminimum:0
Zmaximum:1
Xintervals:0
Yintervals:0
Includexzero:0
Includeyzero:0
Includezzero:0
Statsselect:[1,1,1,1,1,0,0,0]
Probindex:[0.05,0.25,0.5,0.75,0.95]
[Zone,Period][Index Length][Length,0,Vehicle_type,2,Output1,6,Period,1,Zone,1]LengthThe length of the trip classified as short (< 5 km) and long.['< 5 km','>= 5 km']280,48,148,12Composite tripstripsTotal composite and car trips classified into zones and periods. This is indexed by different vehicle types based on the modelled allocation. Note that this number is greater than Trips.m by zone, because here all trips with transfer are calculated as two separate trips.var a:= aggr_period(trips);
a:= trips_by_type(a);
a:= aggr_length(a);
aggr_zone(a)392,224,148,242,102,90,476,3882,43,123,450,295,0,MIDM[Zone,Period](a)Trips by typeAggregates the vehicle index into vehicle_type index. The input parameter a must be indexed by either vehicle or vehicle_noch.var type:= vehicle_types[vehicle_types= Scenario_input[input_var= 'Vehicle types']];
type:= type[vehicle=vehicle_noch];
{a:= if size(a)=size(sum(a,vehicle_noch)) then a else a[vehicle_noch=vehicle];
a:= if a=null then 0 else a;}
a:= if vehicle_noch='Noch' then 0 else a;
for x:= vehicle_type do (
var b:= if type=x then a else 0;
sum(b,vehicle_noch))392,48,148,122,372,20,476,352a(param1)Aggr zoneAggregates geographical location based on the following rules:
If trip is within downtown, it is Downtown.
If trip is within downtown or centre, it is Centre.
If trip goes to suburb, it is Suburb. Param1 must contain To1, From, or both, and this function will aggragate those two indices.if size(param1)/size(from)/size(to1)=size(sum(sum(param1,from),to1)) then
var a:= zones[area1=to1]*zones[area1=from];
a:= if a>4 then 3 else if a=4 then 2 else a;
a:= if zone=a then param1 else 0;
a:= sum(sum(a,from),to1)
else
var b:= param1[from=to1];
var a:= zones[area1=to1];
a:= if zone=a then b else 0;
a:= sum(a,to1)392,16,148,122,16,127,476,428param1Nochange tripsWe assume here that Noch-trips are performed with the vehicle in the first row of Vehicle_noch index. Actually, it is not known, which type of vehicles are used for noch-trips, but this is not a problem unless Nochange fraction is not 0. And even then it is a minor thing.var a:= aggr_zone(aggr_length(trips_aggr_period));
a:= if findintext('c',vehicle_noch)>0 then 0 else a;
a:= a+array(vehicle_noch,[a[vehicle_noch='Noch']]);
a:= trips_by_type(a);
392,152,148,242,40,50,684,303,0,MIDM[Zone,Period](param1)Aggr lengthvar c:= array(length,[0,1]);
c:= if distances[mode1='Car', time_of_day1='Morning'] < 5 then 1-c else c;
param1*c288,16,148,12param1Drop pointsarea1*0+scenario_input[input_var='Drop points/area']512,88,148,2465535,52427,65534Drop lengtharea1*0+1392,88,148,242,496,250,476,22465535,52427,65534Vehicle by typevar type:= vehicle_types[vehicle_types= Scenario_input[input_var= 'Vehicle types']];
type:= type[vehicle=vehicle_noch];
var a:= trips/vehicle_size[vehicle=vehicle_noch];
var c:= 0;
var x:= 1;
while x< size(vehicle_noch) do (
var b:= ceil(slice(a,vehicle_noch,x));
b:= if slice(type,vehicle_noch,x)=vehicle_type then b else 0;
c:= c+b;
x:= x+1);
c280,224,148,242,530,57,476,3362,65,48,416,303,0,MIDM[To1,From]Trips aggr periodaggr_period(trips)280,88,148,24[To1,From]Vehicles per linkvehicles/hThe number of vehicles in each link. Assumes 1.5 trips per private car. Assumes that all gasoline cars are private cars.var v:= Route_matrix;
index e:= Sequence(8,8.99,time_unit);
var g:= vehicle_by_type;
g:= if vehicle_type='Car (g)' then ceil(all_trips[mode1='Car']/1.5) else g;
g:= sum(g[time=e],e);
for x[]:= link1 do (
var c:= (if findintext(x,v)>0 then g else 0);
c:= sum(sum(c,From),To1) )168,352,148,242,96,75,428,552,0,MIDM[Vehicle_type,Link1]Trips per link BAUtrips/hVehicles per link in a scenario with cars only. This is used to rank the links according to their vehicle intensities.var v:= Route_matrix;
var a:= Link1;
index e:= sequence(8,8.99,time_unit);
var f:= sum(sum(adjusted_trip_rate[time=e],e),mode1);
for x[]:= a do (
var c:= (if findintext(x,v)>0 then f else 0);
c:= sum(sum(c,From),To1) )280,416,148,242,74,10,797,552,0,MIDM[To1,From]Bus kmkmThe number of buses needed depends on the higher amount of trips to one direction. In addition, we assume that if a bus is put on its way, it will drive the whole route and come back (thus the factor 2) independently on what is the need elsewhere. The travel is assumed to happen immediately, because there is no need to have higher time resolution. One bus is assumed to take max 50 passengers.var a:= ceil(max(max(bus_trips,bus_trips.etappi), bus_trips.mp)/50);
a:= sum(a*bus_route_length*2,a.i);
a:= sum((if time_of_day_by_time= time_of_day1 then a else 0),time_of_day1);
aggr_period(a)56,272,148,242,102,90,476,4122,40,50,480,303,0,MIDM[Time_of_day1,Time][Sysvar Time]Bus need#The number of buses needed depends on the amount of trips to the more busy direction. In addition, we assume that if a bus is put on its way, it will drive the whole route and come back independently on what is the need elsewhere. The travel is assumed to happen immediately, because there is no need to have higher time resolution. One bus is assumed to take max 50 passengers and drive on average 30 km/h. The need is simplly the highest total need summed over all routes at one time point. But also other buses are taken into account, those that have started but have not returned yet. The busyest time point only is considered.
var a:= bus_trips;
a:= ceil(max(max(a,a.etappi), a.mp)/50);
a:= a*bus_route_length*2/30/time_unit;
max(sum(a,a.i),time_of_day1)512,416,148,242,615,108,605,5052,40,50,1105,409,0,MIDM[I,Time_of_day1][Index I]Vehicle by typevar type:= vehicle_types[vehicle_types= Scenario_input[input_var= 'Vehicle types']];
type:= type[vehicle=vehicle_noch];
var siz:= if vehicle_noch='Noch' then 1 else vehicle_size[vehicle=vehicle_noch];
var a:= if vehicle_noch='Noch' then 0 else ceil(trips/siz);
for x:= vehicle_type do (
var b:= if type=x then a else 0;
sum(b,vehicle_noch))64,344,148,242,65,48,416,303,0,MIDM[To1,From]CostsThis module calculates various pressures of different traffic scenarios. The estimates are based on Outputs node (which has been calculated beforehand due to slow calculations) and the numbers are stored in Static nodes). The outputs of each scenario are indexed (when relevant) by period (day, evening, night); zone (Helsinki downtown, other centre, suburb), length of trip (less or more than 5 km), and vehicle type (8-seat or 4-seat vehicle with of without transfer, or car).
Costs are separately calculated for the passenger and the society. Some costs affect these stakeholders differently, such as fine particle and carbon dioxide emissions: they are calculated as societal costs only, not as costs to a passenger.
The following endpoints are considered (see Table 1):
Fraction of composite trips without change (%)
Vehicles needed (number)
Parking places need (number)
Average vehicle flow on the 30 most busy roads (vehicles/h at 8.00-9.00 AM)
Fine particle (<2.5 µm of diameter) emissions (kg per day)
Carbon dioxide emissions (ton per day)
Driver salaries (thousand e per day)
Vehicle capital and operational costs (thousand e per day)
Time cost (thousand e per day)
Average car trip cost to passenger (e per trip)
Expected composite trip cost to passenger (e per trip)
The following costs are taken into account for passenger (P) or societal (S) costs:
Vehicle capital cost (P+S)
Driver salary cost (P+S)
Driving cost (fuel) (P+S)
Parking (parking fees for individual drivers) (P)
Parking land (opportunity cost of reserving land to parking purposes) (P+S)
Emissions (fine particles and carbon dioxide causing health and climate change effects, respectively (S)
Time for waiting composite vahicles, time spent in traffic jams (P+S)
Accidents (an option only, not used in the current model)
Ticket (profit for composite service provider) (P)
The module has a submodule Cost elements. It contains the detailed descriptions of the unit costs and other input variables that are used to calculate the pressures of each scenario. The values used are dependent on the stakeholder. For example, the car price is the price that a random new car would cost, and it has therefore large uncertainty. On the other hand, the price of a 4-seat composite vehicle is the average price a taxi-style car would cost in Finland, and the confidence intervals are narrower because there is no individual uncertainty. This is because the price of an individual car affects the costs of individual car trips, while the cost of composite trip is dependent on the total cost of vehicles.
Variation between individuals has been separately estimated for three variables: how passengers evaluate the capital costs of owning a car; how passengers are willing to pay for either the right to drive themselves or to not need to drive; and how many passengers are traveling together.jtue6. syyta 2004 13:4648,24600,136,148,241,238,69,519,619,17Scenarios output# or #/hA set of scenarios organised along two indexes:
Guar is the level of composite traffic guarantee. This means that trips within a certain area will be organised by composite travel, while areas outside this guarantee remain without the service. The point in using this index is to explore whether composite traffic can be started with low profile and expanded geographically as more people start using it.
Comp_fr is the fraction of trips that were previously performed by personal car but are performed by composite traffic in the scenario.var a:= Scenario_data;
var b:= Scenario_description;
a:= if b[input_var='Car fraction']=Car_fr then a else 0;
a:= if Car_fr=1 then a[guar=7] else a;
a:= if Car_fr=1 and output1='Waiting' then 0 else a;
a:= if size(car_fr)=1 then sum(a,car_fr) else a;
a:= if b[input_var='Public fraction']=public_fr then a else 0;
a:= if size(public_fr)=1 then sum(a,public_fr) else a;
a:= if b[input_var='Guarantee level']=guar then a else 0;
a:= if size(guar)=1 then sum(a,guar) else a;
a:= if b[input_var='Large guarantee?']=large then a else 0;
a:= if size(large)=1 then sum(a,large) else a;
a:= if b[input_var='Public level']=public_level then a else 0;
a:= if size(public_level)=1 then sum(a,public_level) else a;
a:= if b[input_var='No-change fraction']=nochange_fr then a else 0;
a:= if size(nochange_fr)=1 then sum(a,nochange_fr) else a;
a:= if b[input_var='Max size']=max_size then a else 0;
a:= if size(max_size)=1 then sum(a,max_size) else a;
a:= if b[input_var='Min direct load']=min_direct then a else 0;
a:= if size(min_direct)=1 then sum(a,min_direct) else a;
a:= if b[input_var='Vehicle types']=veh_types then a else 0;
a:= if size(veh_types)=1 then sum(a,veh_types) else a;
a:= if b[input_var='Drop points/area']=drop then a else 0;
a:= if size(drop)=1 then sum(a,drop) else a;
a:= sum(a,Scen_ind);
a:= if a=null then 0 else a;280,32,148,242,803,17,476,7582,38,42,819,419,0,MIDMGraphtool:0
Distresol:10
Diststeps:1
Cdfresol:5
Cdfsteps:1
Symbolsize:6
Baroverlap:0
Linestyle:10
Frame:1
Grid:1
Ticks:1
Mesh:1
Scales:1
Rotation:45
Tilt:0
Depth:70
Frameauto:1
Showkey:1
Xminimum:5
Xmaximum:15
Yminimum:0
Ymaximum:1M
Zminimum:1
Zmaximum:6
Xintervals:0
Yintervals:0
Includexzero:0
Includeyzero:0
Includezzero:0
Statsselect:[1, 1, 1, 1, 1, 0, 0, 0]
Probindex:[5%, 25%, 50%, 75%, 95%]
Arial, 5[Public_fr,Output1][Index Length][0,0,0,0][Length,0,Nochange_fr,1,Large,1,Public_fr,6,Max_size,1,Public_level,1,Vehicle_type,1,Zone,1,Period,1,Output1,1,Scen_ind,1]Cost structureThe various costs that are included in the model. The details of each cost are described in the respective node in the 'Detailed costs' module. Accidents are omitted, although there is a placeholder.['Vehicle','Driver','Driving','Parking','Parking land','Emissions','Time','Accidents','Ticket']280,312,152,122,102,90,476,4662,15,262,416,303,0,MIDM['Vehicle','Driver','Driving','Parking','Parking land','Emissions','Time','Accidents','Ticket']Car capital valuationThe variation of how much an individual values the capital costs of the personal car when estimating the costs of a single trip. If the person needs the car only for trips within the composite traffic area, the valuation might be 1. However, the car is often needed for other purposes also such as longer trips (value: <1), and some people like to own a car in any case (value: 0).ktluser24. lokta 2004 12:48ktluser28. lokta 2004 23:3148,2456,400,148,241,1,1,1,1,1,0,0,0,01,40,59,-432,294,17Arial, 13Cap variabfractionEach row represents one possibility for the distribution of individual valuations in the population. Probability distributions are used to represent this variation within population.Table(Self)(
Uniform(0,1),Triangular(0,0,1),Bernoulli(0.2))[1,2,3]56,32,148,242,376,89,476,2802,98,96,288,157,0,MIDM2,280,290,465,303,0,MIDM65535,52427,65534[0,0,0,0]Based on author judgement, as there is no data available.Cap uncertThe uncertainty between several valuation distributions on the population level.Probtable(Self)(
(1/3),(1/3),(1/3))56,96,148,242,97,295,351,170,0,MIDM2,248,258,416,303,0,SAMP52425,39321,65535[1,2,3]Based on author judgement, as there is no data available.CapfractionThe aggregate of the car capital variation and uncertainty.Cap_variab_2[Cap_variab=Cap_uncert]168,96,148,242,247,96,476,4202,83,220,416,303,0,MIDM[0,0,0,0]Cap variationfractileThe fractile of the sample within the population.average(sample(Cap_variab_2),cap_variab)168,32,148,242,199,80,476,2802,510,212,416,303,1,CDFP[Run,Cap_variab][0,0,0,0]9.5.2005 Jouni Tuomisto
Vanha syntaksi, ennen kuin keksin miten epävarmuus ja vaihtelu erotetaan:
var a:= rank(cap_variab,run)/samplesize;
a[Cap_variab=Cap_uncert]Cap variab 2Vanha Cap:
Cap_variab_2[Cap_variab=Cap_uncert]
Vanha Cap_variation:
average(sample(cap_variab_2),cap_variab)
var a:= cap_variab[run=sortindex(cap_variab,run)];
var b:= uniform(0,1);
a[run=sortindex(b,run)]168,168,148,242,252,400,476,2242,576,48,377,441,0,SAMPGraphtool:0
Distresol:10
Diststeps:1
Cdfresol:5
Cdfsteps:1
Symbolsize:6
Baroverlap:0
Linestyle:10
Frame:1
Grid:1
Ticks:1
Mesh:1
Scales:1
Rotation:45
Tilt:0
Depth:70
Frameauto:1
Showkey:1
Xminimum:0
Xmaximum:100
Yminimum:0
Ymaximum:1
Zminimum:1
Zmaximum:3
Xintervals:0
Yintervals:0
Includexzero:0
Includeyzero:0
Includezzero:0
Statsselect:[1,1,1,1,1,0,0,0]
Probindex:[0.05,0.25,0.5,0.75,0.95]
Arial, 8[Cap_variab,Run][0,0,0,0]Willingness to driveThe price that the passenger is willing to pay to be able to drive the vehicle him/herself compared with the situation where the composite driver drives the vehicle. Note that for car passengers, the question is not about driving but being a passenger in a car or in a composite vehicle.
There are also cases where the car driver is not traveling but only chauffeuring passengers that do not have driver's license. The willingness to drive is probably low in these cases, but we were very modest in these estimates.
There exists no data about this variable, because it is about a comparison between the current and a hypothetical situation. Author judgement is therefore used.ktluser24. lokta 2004 12:4848,2456,456,148,241,1,1,1,1,1,0,0,0,01,444,64,-318,333,17Arial, 13Drive variabfractionWillingness to drive. This is expressed as fraction of composite driver's salary. Each row represents one possibility for the distribution of individual valuations in the population. Probability distributions are used to represent this variation within population.Table(Self)(
Uniform(-0.3,0),Triangular(-0.1,0,0.3),Uniform(-0.2,0.2))[1,2,3]64,40,148,242,102,90,476,4052,79,219,457,274,0,MIDM2,132,57,335,303,0,SAMP65535,52427,65534[Self,Run][0,0,0,0]Based on author judgement, as there is no data available.Drive uncertThe uncertainty between several valuation distributions on the population level.Probtable(Self)(
(1/3),(1/3),(1/3))64,104,148,242,488,60,247,303,0,SAMP52425,39321,65535[1,2,3]Based on author judgement, as there is no data available.DrivefractionThe aggregate of the willingness to drive variation and uncertainty. It is expressed as a fraction of composite driver's salary.Drive_variab_2[Drive_variab=Drive_uncert]176,104,148,242,786,88,416,303,0,SAMP[0,0,0,0]Drive variationfractileThe fractile of the sample within the population.average(sample(drive_variab_2),drive_variab)176,40,148,242,104,69,476,2242,120,130,416,303,1,CDFP[Run,Cap_variab][0,0,0,0]Drive variab 2Vanha Drive:
Drive_variab_2[Drive_variab=Drive_uncert]
Vanha Drive_variation:
average(sample(drive_variab_2),drive_variab)
var a:= drive_variab[run=sortindex(drive_variab,run)];
var b:= uniform(0,1);
a[run=sortindex(b,run)]176,176,148,242,767,125,476,3622,576,48,377,441,0,SAMPGraphtool:0
Distresol:10
Diststeps:1
Cdfresol:5
Cdfsteps:1
Symbolsize:6
Baroverlap:0
Linestyle:10
Frame:1
Grid:1
Ticks:1
Mesh:1
Scales:1
Rotation:45
Tilt:0
Depth:70
Frameauto:1
Showkey:1
Xminimum:0
Xmaximum:100
Yminimum:0
Ymaximum:1
Zminimum:1
Zmaximum:3
Xintervals:0
Yintervals:0
Includexzero:0
Includeyzero:0
Includezzero:0
Statsselect:[1,1,1,1,1,0,0,0]
Probindex:[0.05,0.25,0.5,0.75,0.95]
Arial, 8[Drive_variab,Run][0,0,0,0]Trips per modetrips/periodNumber of trips per periodvar a:= scenarios_output[output1='All trips'];
a:= array(mode1,[
slice(a,vehicle_type,4),
slice(a,vehicle_type,2),
slice(a,vehicle_type,1)]);
sum(sum(a[period=choose_period],zone),length)400,208,148,162,102,90,476,2572,17,71,622,270,0,MIDM[Car_fr,Mode1][Index Length][0,0,0,1]StakeholderThere are three different stakeholders:
'Passenger' is a random sample of passengers who have chosen the personal car in the business-as-usual scenario, and may choose between car and composite traffic in other scenarios.
'Society' the community that is responsible for the well-being of citizens in the metropolitan area. It also has the ability to pay subsidies to public transportation. Societal costs include other costs than passenger costs, such as health effects of air pollution, and opportunity costs of parking space.
'Bus company', the composite traffic service provider, is a simple stakeholder and does not therefore show up in the stakeholder index. Its only interest (in the model) is to get a reasonable profit ('Ticket' cost) from each composite trip (in addition to covering direct costs).['Passenger','Society']168,456,148,12['Passenger','Society']Cost elementsThis module contains the detailed descriptions of the unit costs and other input variables that are used to calculate the pressures of each scenario. The values used are dependent on the context. For example, the car price is the price that a random new car would cost, and it has therefore large uncertainty. On the other hand, the price of a 4-seat composite vehicle is the average price a taxi-style car would cost in Finland, and the confidence intervals are narrower because there is no individual uncertainty. This is because the price of an individual car affects the costs of individual car trips, while the cost of a composite trip is dependent on the total cost of vehicles to the service provider.ktluser3. marta 2004 16:3748,24168,360,148,241,89,140,612,484,17[Alias Cost_elements1]Emission factorg/kmFine particle and carbon dioxide unit emissions for average vehicles. Fine particle emissions are taken from the Lipasto model using average (mixed gasoline and diesel) values for personal car and diesel EURO3 (applied since 2000) values for composite vahicles. For CO2, typical emissions of a new car were used based on the Finnish Vehicle Administration AKE. The following vehicles are used as typical examples of the class:
8-seat vehicle: Toyota Hiace 2.5 D4D 100 4 door long DX bus
4-seat vehicle: Toyota Corolla 2.0 90 D4D Linea Terra 5 door Hatchback (diesel)
Car: Toyota Corolla 1.6 VVT-i Linea Terra 5ov Hatchback (gasoline)Table(Vehicle_type,Pollutant)(
(0.1*Triangular(0.3,1,1.7)),(232*Triangular(0.9,1,1.1)),
(0.1*Triangular(0.3,1,1.7)),(232*Triangular(0.9,1,1.1)),
(0.1*Triangular(0.3,1,1.7)),(153*Triangular(0.9,1,1.1)),
(0.047*Triangular(0,1,2)),(168*Triangular(0.9,1,1.1))
)472,368,148,242,45,51,618,6232,231,148,416,303,0,MIDM2,56,66,416,303,0,MEAN65535,52427,65534[Pollutant,Vehicle_type][Pollutant,Vehicle_type][1,0,0,0]http://lipasto.vtt.fi/yksikkopaastot/henkiloautotkeskimaarin.htm
Pääkaupunkiseudun julkaisusarja B1999: 5. Vaihtoehtoisten polttoaineiden käyttömahdollisuudet joukkoliikentessä Pääkaupunkiseudulla. Taulukko 3, Keskusta ja esikaupunki.
Autorekisterikeskus AKE: Uuden auton kulutustiedot. EKOAKE, huhtikuu 2003.Pollutant['PM','CO2']472,400,148,12['PM','CO2']Vehicle pricee/vehiclePrice of a new vehicle. Note that the interpretation is slightly different with different vehicles.
The car price is the price that a random new car would cost, and it has therefore large uncertainty. The price of a composite vehicle is the average price of a taxi-style car in Finland, and the confidence intervals are narrower because there is no individual uncertainty. This is because the price of an individual car affects the costs of individual car trips, while the cost of a composite trip is dependent on the total cost of vehicles to the service provider.var d:= 160K*Triangular(0.75,1,1.25);
var a:= 39.52K*Triangular(0.75,1,1.25);
var b:= 22.6K*Triangular(0.75,1,1.25);
var c:= lognormal(19.1K,1.5);
a:= array(Vehicle_type,[d,a,b,c]);
{a[vehicle_type=vehicle_types]}56,24,148,242,102,90,476,3752,22,50,416,303,0,MIDM2,68,58,839,549,0,MIDM65535,52427,65534Graphtool:0
Distresol:10
Diststeps:1
Cdfresol:5
Cdfsteps:1
Symbolsize:6
Baroverlap:0
Linestyle:1
Frame:1
Grid:1
Ticks:1
Mesh:1
Scales:1
Rotation:45
Tilt:0
Depth:70
Frameauto:1
Showkey:1
Xminimum:-20K
Xmaximum:80K
Yminimum:-1u
Ymaximum:1u
Zminimum:1
Zmaximum:6
Xintervals:0
Yintervals:0
Includexzero:0
Includeyzero:0
Includezzero:0
Statsselect:[1,1,1,1,1,0,0,0]
Probindex:[0.05,0.25,0.5,0.75,0.95]
[0,0,0,0]Vehicle lifetimeaExpected operation time of a new vehicle.var d:= 12*Triangular(0.75,1,1.25);
var a:= 7*Triangular(0.75,1,1.25);
var b:= 5*Triangular(0.75,1,1.25);
var c:= 9*Triangular(0.7,1,1.3);
a:= array(Vehicle_type,[d,a,b,c]);
{a[vehicle_type=vehicle_types]}184,88,148,242,102,90,476,4842,14,383,416,303,0,MIDM65535,52427,65534[0,0,0,0]Fuel consumptionl/kmFuel consumption of a vehicle. It is assumed that composite vehicles use diesel fuel and cars use gasoline. The values are based on standardised European consumption values of a new car.var a:= (8.7/100)*Triangular(0.75,1,1.25);
var b:= (5.7/100)*Triangular(0.75,1,1.25);
var c:= (8/100)*Triangular(0.5,1,1.5);
a:= array(Vehicle_type,[a,a,b,c]);
{a[vehicle_type=vehicle_types]}56,168,148,242,454,28,476,4552,425,410,416,303,0,MIDM2,152,162,416,303,0,MIDM65535,52427,65534[0,0,0,0]Fuel pricee/lDiesel price for composite vehicles; gasoline price for cars. The values are based on rough follow-up of retail prices in Finland in fall 2004 - summer 2005.var a:= 0.95*triangular(0.8,1,1.2);
var b:= 1.22*triangular(0.8,1,1.2);
array(Vehicle_type,[a,a,a,b])56,216,148,242,102,90,476,5292,481,182,416,246,0,MIDM2,264,175,697,402,0,MIDM65535,52427,65534[0,0,0,0]St1 gas station, Kuopio keskusta, 6.9.2004.Driver salarye/hMonthly salary and social security costs (35 %), and scaled to one hour assuming 160 hours of work per month. The salary is based on that of bus drivers in municipality-owned bus companies.var a:= 2313/160*1.35;
normal(a,a*0.18)192,32,148,242,102,90,476,4682,411,332,416,303,0,CONF65535,52427,65534[0,0,0,0]Statistics Finland 2005 <a href= "http://statfin.stat.fi/StatWeb/start.asp?LA=en&lp=home&DM=SLEN" >Click</a>Parking spacee/d/parking spaceCost of a parking space to the society due to the opportunity loss of the land, and maintenance costs.var va1:= 1.05^30;
var a:= 20*3000;
a:= (a-a/va1)*va1;
a:= a/30/365;
a/2*lognormal(1,1.3)192,272,148,242,102,90,476,3282,40,50,416,303,0,MIDM65535,52427,65534[0,0,0,0]Costs of unit emissions of air pollutantse/kgAssumptions: Primary fine particle emissions of 24290 kg/a caused 12.5 deaths in a risk assessment study in Helsinki (Tainio et al, 2005). We here use the distribution of deaths per emission derived from that study. The value of a statistical life is 0.98-2 Me (Watkiss et al., 2005). The official value for road economy calculations is 201.879 e/kg (LVM, 2003). This value is within the range derived from Tainio, but clearly lower than the mean.
CO2 emission price comes from the emission trade market. According to Helsingin Sanomat (7 May, 2005), it was 18 e/ton in 5 Apr, 2005, although it had been lower during previous months. In July, it was approaching 30 e/ton according to Taloussanomat. The official value for road economy calculations is 32 e/ton (LVM, 2003), which is within the range used here.var a:= Pm_unit_lethality;
array(Pollutant, [a*uniform(0.98M,2M), uniform(5,40)/1000])192,216,148,292,73,7,548,6452,466,127,416,303,0,MIDM2,54,148,672,472,0,MIDM65535,52427,65534Graphtool:0
Distresol:10
Diststeps:1
Cdfresol:5
Cdfsteps:1
Symbolsize:6
Baroverlap:0
Linestyle:1
Frame:1
Grid:1
Ticks:1
Mesh:1
Scales:1
Rotation:45
Tilt:0
Depth:70
Frameauto:1
Showkey:1
Xminimum:0
Xmaximum:1
Yminimum:0
Ymaximum:1
Zminimum:0
Zmaximum:1
Xintervals:0
Yintervals:0
Includexzero:0
Includeyzero:0
Includezzero:0
Statsselect:[1,1,1,1,1,0,0,0]
Probindex:[0.05,0.25,0.5,0.75,0.95]
[1,0,0,0]Tainio, M., Tuomisto, J.T., Hänninen, O., Aarnio, P., Koistinen, K.J., Jantunen, M.J., and Pekkanen J. Health effects caused by primary particulate matter (PM2.5) emitted from buses in the Helsinki Metropolitan Area, Finland. Risk Analysis, Vol. 25, No.1, 2005. pp. 151-160.
{[Tainio, 2005 96 /id]}
<a href="http://www.blackwell-synergy.com/links/doi/10.1111/j.0272-4332.2005.00574.x/abs/">Link to publisher</a>
{[Watkiss, 2005]} <a href="http://europa.eu.int/comm/environment/air/cafe/activities/cba_baseline_results2000_2020.pdf">Click</a>
{[LVM, 2003]} <a href="http://www.mintc.fi/www/sivut/dokumentit/julkaisu/mietinnot/2003/b292003.pdf">Click</a>Trips per cartrips/d/carNumber of trips per car per day, i.e. the cumulative number of passenger that use the car during the day. This value is used to calculate the need of cars.uniform(4,10)312,88,148,2465535,52427,65534Tickete/tripThe income the service provider wants to get from composite traffic users in addition to the price of the direct costs (vehicle, fuel, driver, and parking costs).Uniform( 0.2, 0.6 )312,160,148,2465535,52427,65534[0,0,0,0]Group sizepassengersSize of group traveling together for a random passenger.var a:= Car_occupancy*occupancy;
a:= a/sum(a,occupancy);
chancedist(a,occupancy,occupancy)192,328,148,242,355,136,476,3442,445,95,416,473,0,MEAN65535,52427,65534[0,0,0,1]OccupancyAn index for the number of passengers in a personal car.1..556,360,148,12[1,2,3,4,5]Rush delayh, fractionDelay that is caused by increased link intensity. The node contains two values. Delay is the average time of delay due to traffic jams during daytime. Reduction is the relative reduction to 'Link intensity' (average vehicle flow on the 30 most busy roads at 8.00-9.00 AM) that is needed to reduce the delay to 0 min.Table(Self)(
(Triangular(0,0,10)/60),0.3)['Delay','Reduction']312,32,148,242,102,90,476,3862,592,87,416,303,0,MIDM2,40,50,416,303,0,SAMP65535,52427,65534[Self,Run][0,0,0,0]Parking pricee/tripThe cost of 30 min parking in zones 1, 2, 3 in Helsinki. It is assumed that each car trip involves 30 min of parking during daytime, while during evening and night, the parking is free. Also daytime parking at home is included in these estimates, although it is difficult to valuate. In any case, it is common to pay at least 5-10 euro per month for a parking place (or more for a garage), which is 15-30 cents per day. Due to the uncertainties, the confidence intervals are large.Table(Period,Zone)(
((2.4*0.5)*Triangular(0,1,2)),((1.2*0.5)*Triangular(0,1,2)),((0.6*0.5)*Triangular(0,1,2)),
0,0,0,
0,0,0
)312,272,148,242,102,90,476,3922,336,267,416,303,0,MIDM2,232,242,416,303,0,MIDM65535,52427,65534[Period,Zone][Period,Zone][0,0,0,0]Accidentscases/aThe number of injuries and deaths in traffic accidents in Vantaa, Espoo, and Helsinki, respectively. It is assumed that the number of 2002 or 2003 statistics is the expectation. Poisson distribution is used to describe the uncertainty.
Taulukko 1-1 Liikenneonnettomuudet Vantaalla v. 2002
Yhteensä Hvo Ovo Loukkaantui Kuoli
Auto-onnettomuus 570 100 470 155 5
Moottoripyöräonnettomuus 23 15 8 13 2
Mopo-onnettomuus 14 6 8 7 0
Polkupyöräonnettomuus 47 37 10 40 0
Jalankulkijaonnettomuus 33 29 4 31 0
Yhteensä tieliikenne 687 187 500 246 7
Raideliikenne (jk) 8 8 - 1 7
Hvo= henkilövahinkoon johtanut onn.
Ovo= omaisuusvahinkoon johtanut onn.
LIIKENNEONNETTOMUUDET VUONNA 2003
Pelti rytisi Espoon alueella viime vuonna yhteensä 434 kertaa. Henkilövahinko-onnettomuuksia oli 135, niissä kuoli 3 ja loukkaantui 159 henkilöä. Edelliseen vuoteen verrattuna liikenneonnettomuuksien määrä kääntyi hienoiseen laskuun. Vuonna 2002 tilastoitiin 538 onnettomuutta. Liikenneonnettomuustiedot on koottu poliisille ilmoitetuista onnettomuustapauksista.
Onnettomuuskustannukset
Liikenneonnettomuudet aiheuttivat Helsingissä vuonna
2003 yhteensä 244 miljoonan euron yhteiskunnalliset
kustannukset. Henkilövahinkoihin johtaneiden onnettomuuksien
osuus oli 213 miljoonaa euroa. Laskelma perustuu
liikenne- ja viestintäministeriön hyväksymiin liikenneonnettomuuksien
yksikkökustannuksiin vuodelta
2000. Kustannuksissa ovat mukana onnettomuuksien
aiheuttamat reaalitaloudelliset menetykset ja ns. hyvinvoinnin
menetys. Taloudellisia kustannuksia ovat sairaanhoitokulut,
uhrin työn menetys, ajoneuvovahingot
sekä muut aineelliset vahingot.Table(Self)(
Poisson(((246+159)+724)),Poisson(((7+3)+16)))['Injuries','Deaths']312,408,148,242,82,80,500,5002,578,153,416,303,0,MIDM2,136,146,416,303,0,MIDM65535,52427,65534Graphtool:0
Distresol:10
Diststeps:1
Cdfresol:5
Cdfsteps:1
Symbolsize:6
Baroverlap:0
Linestyle:1
Frame:1
Grid:1
Ticks:1
Mesh:1
Scales:1
Rotation:45
Tilt:0
Depth:70
Frameauto:1
Showkey:1
Xminimum:0
Xmaximum:1
Yminimum:0
Ymaximum:1
Zminimum:0
Zmaximum:1
Xintervals:0
Yintervals:0
Includexzero:0
Includeyzero:0
Includezzero:0
Statsselect:[1,1,1,1,1,0,0,0]
Probindex:[0.05,0.25,0.5,0.75,0.95]
[Self,Undefined,Undefined,2][1,1,0,1]Liikenneonnettomuudet Vantaalla 2002. C21:2003. Vantaan kaupunki, Vantaa 2003. <a href="http://www.vantaa.fi/i_liitetiedosto.asp?path=1;135;137;221;1761;1827;7348;7349">Internet PDF</a>
Liikenneonnettomuudet Helsingissä vuonna 2003. <a href="http://www.hel.fi/ksv/hela/Kaupunkisuunnittelulautakunta/Esityslistat/liitteet/041670240.pdf">Internet file</a>
Espoon kaupunki, liikenneturvallisuus. <a href="http://www.espoo.fi/xsl_taso2_alasivuilla.asp?path=1;606;607;4214;7808">Internet page</a>
http://www.tieh.fi/liikenneturvallisuus/lion04.pdf
Accident costse/dThe societal costs of traffic accidents were 227 million euro in Helsinki in 2004. For the whole metropolitan area, this is more than 1 million euro per day. The numbers are scaled up from Helsinki to the metropolitan area based on the numbers of injured people in accidents. The uncertainty is based on the standard deviation of the variable Accidents (deaths), which is ca. 20% of the mean.
The accident cost number for Helsinki is scaled up by the number of injuries in the whole Helsinki Metropolitan Area (for data and references, see Accidents).
"Onnettomuuskustannukset
Liikenneonnettomuudet aiheuttivat Helsingissä vuonna
2003 yhteensä 244 miljoonan euron yhteiskunnalliset
kustannukset. Henkilövahinkoihin johtaneiden onnettomuuksien
osuus oli 213 miljoonaa euroa. Laskelma perustuu
liikenne- ja viestintäministeriön hyväksymiin liikenneonnettomuuksien
yksikkökustannuksiin vuodelta
2000. Kustannuksissa ovat mukana onnettomuuksien
aiheuttamat reaalitaloudelliset menetykset ja ns. hyvinvoinnin
menetys. Taloudellisia kustannuksia ovat sairaanhoitokulut,
uhrin työn menetys, ajoneuvovahingot
sekä muut aineelliset vahingot."var a:= 227M*((246+159+724)/724)/365;
normal(a,a/5)312,328,148,242,511,78,500,5442,26,124,416,303,0,MIDM65535,52427,65534[0,0,0,0]Liikenneonnettomuudet Helsingissä vuonna 2003. <a href="http://www.hel.fi/ksv/hela/Kaupunkisuunnittelulautakunta/Esityslistat/liitteet/041670240.pdf">Internet file</a>
Liikenneonnettomuudet Helsingissä vuonna 2004. <a href="http://www.hel.fi/ksv/Mita_suunnitellaan/Liikenne/tilastoja/liikenneonnettomuudet2004.pdf"> Internet file </a>
http://www.ytv.fi/FIN/seutu_ymparistotietoja/liikkuminen/onnettomuudet/etusivu.htmCars should also have variationThe costs of car have large individual variation. This might be an important factor in the comparison of car and composite traffic. This is not currently done but could be considered in the future versions of the model.Fuel_consumption;
Vehicle_lifetime;
Vehicle_price;
0464,48,148,29The costs are calculated for a passenger who has a car in the household and is trying to decide between the car and composite trafficThe costs are calculated for a passenger who has a car in the household and is trying to decide between the car and composite traffic.vehicle_price464,160,168,721,1,1,1,1,1,0,,1,2,102,90,476,427Time unit coste/hThe cost of time spent waiting for a composite vehicle or in traffic jam.Triangular( 0, 5.9, 11.8 )['Delay','Reduction','Cost']312,216,148,242,102,90,476,3012,199,277,416,303,0,MIDM65535,52427,65534[0,0,0,0]Group subventione/tripThis subvention is given to passengers that travel in groups with more than one person. The idea is that the subsidy is an amount (uncertain to the decision-maker) which is given to everyone in the group except the first one. In this way, the total group subsidy increases with the size of the group (just like the efficiency of car travelling increases with more passengers). We assume here that the groups are identical in both car and composite modes.var a:= uniform(0,2);
a:= a*(sample(Group_size)-1)/sample(Group_size);
if subsidise_groups_='Yes' then a else 0192,408,148,242,144,229,512,3262,136,146,416,303,0,MIDM65535,52427,65534[Run,Subsidise_groups_][0,0,0,1]Subsidise groups?Personal car becomes more efficient if there are several passengers. To attract groups to use the composite traffic, it is possible to subsidise groups so that there is a certain reduction in the ticket price. This node determines whether group subsidies are considered in the model or not. In the default model, this variable is set to No.Choice(Self,2)56,408,148,242,102,90,476,342[Formnode Subsidise_groups_1]['Yes','No']Car occupancyfractionProportion of cars with different number of passengers. The last number is divided into occupancy '4' and '5' based on author judgement. The original data is from streets entering downtown Helsinki during a weekday (from 6.00 to 21.00) in May.
driver 72.0 %
driver+1 passenger 23.3 %
driver+2 passengers 3.3 %
driver+ at least 3 passenger 1.4 %var a:= array(occupancy,[0.72,0.233,0.033,0.01,0.004]);
a56,328,148,242,102,90,476,3452,445,95,416,473,0,MIDM65535,52427,65534Car maintenancee/kmMaintenance costs (service, tyres, oil etc.). This is based on Autoliitto's report 'Costs of car 2004'. Insurance and use tax are excluded, as like capital costs, there may be other reasons to own the car, and then these would be sunken costs.
Original values assuming an old car with the original price 20000 e, 20000 km/a of driving (e/a):
Maintenance 844
Tyres 320
total 1164/20000 = 0.0582 e/kmTriangular( 0.03, 0.058, 0.086 )56,272,148,242,210,329,416,303,0,MEAN65535,52427,65534[0,0,0,0]PM unit lethalitydeaths/kgAssumptions: Primary fine particle emissions of 24290 kg/a caused 12.5 deaths in a risk assessment study in Helsinki (Tainio et al, 2005). We use the distribution of deaths per emission derived from that study.var a:= fractiles([
-7.223e-004,
5.640e-006,
4.228e-005,
5.987e-005,
8.013e-005,
1.150e-004,
2.037e-004,
2.939e-004,
3.598e-004,
4.132e-004,
4.640e-004,
5.139e-004,
5.662e-004,
6.233e-004,
6.854e-004,
7.577e-004,
8.441e-004,
9.519e-004,
1.093e-003,
1.314e-003,
2.805e-003]);
a192,160,148,242,102,90,476,4282,466,127,416,303,0,MIDM2,54,148,672,472,1,PDFP65535,52427,65534Graphtool:0
Distresol:10
Diststeps:1
Cdfresol:5
Cdfsteps:1
Symbolsize:6
Baroverlap:0
Linestyle:1
Frame:1
Grid:1
Ticks:1
Mesh:1
Scales:1
Rotation:45
Tilt:0
Depth:70
Frameauto:1
Showkey:1
Xminimum:0
Xmaximum:1
Yminimum:0
Ymaximum:1
Zminimum:0
Zmaximum:1
Xintervals:0
Yintervals:0
Includexzero:0
Includeyzero:0
Includezzero:0
Statsselect:[1,1,1,1,1,0,0,0]
Probindex:[0.05,0.25,0.5,0.75,0.95]
[0,0,0,0]Tainio, M., Tuomisto, J.T., Hänninen, O., Aarnio, P., Koistinen, K.J., Jantunen, M.J., and Pekkanen J. Health effects caused by primary particulate matter (PM2.5) emitted from buses in the Helsinki Metropolitan Area, Finland. Risk Analysis, Vol. 25, No.1, 2005. pp. 151-160.
{[Tainio, 2005 96 /id]}
<a href="http://www.blackwell-synergy.com/links/doi/10.1111/j.0272-4332.2005.00574.x/abs/">Link to publisher</a>Bus ticket priceARVO
Henkilökohtaiset ja haltijakohtaiset matkakortit
Kaikilla arvolipuilla voi vaihtaa lipun voimassaoloaikana. Liput ovat voimassa
¥ Helsingin sisäisillä matkoilla 60 minuuttia
¥ seutumatkoilla sekä Espoon, Kauniaisten ja Vantaan sisäisillä matkoilla 80 minuuttia.
SEUTU
Aikuinen Lapsi
¥ arvolippu 2,90 1,45
¥ päiväarvolippu ma-pe 9-14 2,70
¥ yöarvolippu ma-su 2-4.30 4,00
HELSINGIN SIS€INEN
Aikuinen Lapsi
¥ arvolippu 1,70 0,70
¥ päiväarvolippu ma-pe 9-14 1,40
¥ yöarvolippu ma-su 2-4.30 2,50
¥ arvolippu, raitiovaunu 1,28
________________________________
Matkakorttiyksikön toimintamenot vuonna 2005
ovat noin 4,2 milj. euroa, mikä on hieman
vähemmän kuin edellisvuonna.
__________________________________
YTV:n matkakorttijärjestelmän piirissä on 800.000 matkakortin käyttäjää pääkaupunkiseudulla. Päivittäin järjestelmää käytetään yli miljoonan matkan tekemiseen.
4.2M/1M/365448,272,148,242,325,106,476,38465535,52427,65534<a href="http://www.ytv.fi/matkakortti/mitamaksaa.html">Ticket prices (in Finnish)</a>
<a href="http://www.ytv.fi/yleis/asiak/poutakirjat/04015347.HTM"> Total costs of the travel card system (matkakortti)</a>
<a href="http://www.ytv.fi/liikenne/ajank/uutinen.php?id=2774">Total trip volumes using travel card</a>Unit cost of drivinge/kmfuel_price*fuel_consumption+car_maintenance56,104,148,24[1,0,0,0]Car frThe fraction of trips that were previously performed by personal car but are performed by composite traffic in the scenario.index b:= copyindex(Scenario_description[input_var='Car fraction']);
unique(b,b)280,64,144,122,460,148,476,4162,236,315,416,303,0,MIDM[0,0,0,0][0.6]GuarThe level of composite traffic guarantee. This means that trips within certain areas will be organised by composite travel, while areas outside this guarantee remain without the service. The point in using this index is to explore whether composite traffic can be started with low profile and expanded geographically as more people start using it.index b:= copyindex(Scenario_description[input_var='Guarantee level']);
unique(b,b)192,64,148,122,102,90,476,3532,623,192,416,303,0,MIDM[0,0,0,1][7]Detailed costsDetailed costs and pressures. See each individual node for a full description.ktluser24. marta 2004 0:0048,24280,208,148,241,578,150,619,455,17Emissionkg/dTotal emissions based on kilometres driven. The unit emissions are based on standard values.vehicle_km_s*Emission_factor/1000176,224,148,162,218,232,476,2242,43,56,717,317,0,MIDM[Car_fr,Period][Index Travel_type][0,0,0,0]Driver needpersonsThe number of full-time drivers needed in the composite traffic. This is based on the kilometres driven and an 8-hour working day. It is assumed that there is no waiting for drivers. This assumption probably causes underestimation of the true number.sum(sum(vehicle_km_s/traffic_speed/8,zone),length)
{var a:= Scenarios_output[output1='Vehicle km'];
a:= slice(a,region,1);
ceil(a/traffic_speed/8)}296,96,148,162,102,90,476,2862,19,38,868,392,0,MIDM[Car_fr,Period][Index Travel_type]Cars neededvehiclesFor composite vehicles, this comes directly from traffic optimising; for cars, it is simply the number of trips divided by the average number of trips per car per day. For cars, the amount needed is difficult to estimate, because most cars are needed also for trips beyond the area modelled here. Therefore, even if some trips are performed by composite traffic, it is possible that the number of cars needed remains the same but the number of trips per car decreases.var a:= Trips_per_mode[mode1='Car']/trips_per_car;
a:= sum(sum(sum(a,length),zone),period);
{if vehicle_type='Car (g)' then a else vehicles_needed_s}
{var a:= Trips_per_period[mode1='Car']/Trips_per_car;
var b:= trips_per_period[Mode1='Composite'];
b:= b/sum(b,length);
b:= b*sum(sum(scenarios_output[output1='Vehicles'],zone),length);
b:= if Vehicle_type='Car (g)' then a else b;
if periods=1 then b else 0}504,136,148,162,470,127,477,4942,25,485,438,264,0,MIDM[Car_fr,Vehicle_type][Index Length][0,0,0,0]Car parking coste/dIt is assumed that each car trip involves parking. However, composite traffic does not pay anything in parking meters. Instead, they have to pay for the land. This cost is calculated as Parking land cost.var a:= scenarios_output[output1='All trips', vehicle_type='Car (g)'];
a:= a*parking_price296,160,148,162,77,296,476,4022,48,139,602,242,0,MIDM[Period,Zone][Variable Zone][0,0,0,0]Emission coste/dFine particles are assumed to cause 10 deaths per 17 ton emission, a result from buses in Helsinki (1). CO2 costs are based on the estimated costs of CO2 in the greenhouse gas emission market. The true health and environmental costs are probably clearly higher than the price of the emission market.emission1*Costs_of_unit_emissi296,224,148,162,102,90,476,3922,64,54,639,305,0,MIDM[Car_fr,Vehicle_type][Index Length][0,0,0,0]Parking land coste/dCost of parking land. It is assumed that for composite vehicles, there is a fixed amount of reserved parking places. The cost is equal to the societal cost of the land use. This cost is allocated to short and long trips based on the number of trips.parking_s*parking_space
{var a:= scenarios_output;
var b:= a[output1='All trips'];
b:= b/sum(b,length);
a:= sum(slice(a[output1='Park rush veh'],period,2),length);
a*Parking_space*b}296,192,148,162,541,90,476,2852,40,16,555,416,0,MIDM[Zone,Vehicle_type][Variable Zone][0,0,0,0]Taxi accident rate"The accident risk of taxies (related to kilometres driven) is 40 percent lower than that of regular drivers. However, the accident density is 10.4 accidents per year per 100 cars, is double the number for private drivers."
.6504,40,148,2465535,52427,65534Ammattiliikenteen turvallisuuden kehittäminen. LINTU-projektin osaraportti 12. Research report 566/2000. VTT 2000, Espoo. <a href="http://www.vtt.fi/rte/projects/srs/raportit/lintu_osa12_ammattiliik.pdf">Internet PDF</a>
Acc costse/dWe assume that half of the accidents are attributable to personal car traffic, while the other half is attributable to other traffic modes (walking, cycling, public transportation). In addition, the accident risk is proportional to the change in traffic volume, but there is uncertainty about the slope. The expected value is that when traffic volume decreases 10%, accident risk decreases 5%; but it could vary between 0% and 10%.
It is likely that these two assumptions underestimate rather than overestimate the benefit of composite traffic, but we were careful not to exaggerate the benefits. The guidelines for road projects #REF# assume that accidents are proportional to the traffic volume.var a:= Vehicle_km_s;
var b:= sum(sum(sum(sum(a,zone),vehicle_type),length),period);
b:= (1-b/b[Car_fr=1])*triangular(0,0.5,1);
b:= (1-b)*accident_costs*0.5;
a:= a/sum(sum(sum(sum(a,zone),vehicle_type),length),period);
b*a296,288,148,162,523,131,476,4332,27,48,714,303,0,MIDM[Period,Vehicle_type][Index Length][0,0,0,0]Acc numA draft node. Not used in the model.var a:= sum(scenarios_output,zone);
a:= a[output1='Vehicle km'];
var b:= sum(sum(sum(a,vehicle),length),period);
b:= (1-b/b[Car_fr=0])*triangular(0,0.5,1);
(1-b)*accidents*0.5504,88,148,242,60,131,476,4522,15,97,354,363,0,MIDM[Accidents,Car_fr][0,0,0,0]Rush BAUvehicles/hThe average number of vehicles per hour driving along a link for the 30 most busy links at 8.00-9.00 in the morning. These numbers are for business-as-usual scenario where there is no composite traffic.rush_s[car_fr=1, public_fr=1, guar=7]
{var c:= Scenario_description[input_var='Car fraction'];
var g:= Scenario_description[input_var='Guarantee level'];
a:= if c=1 and g=7 then a else 0;
sum(sum(sum(a,Scen_ind),zone),length)}176,320,148,162,403,80,476,5272,26,211,355,321,0,MIDMGraphtool:0
Distresol:10
Diststeps:1
Cdfresol:5
Cdfsteps:1
Symbolsize:6
Baroverlap:0
Linestyle:10
Frame:1
Grid:1
Ticks:1
Mesh:1
Scales:1
Rotation:45
Tilt:0
Depth:70
Frameauto:1
Showkey:1
Xminimum:5
Xmaximum:15
Yminimum:0
Ymaximum:1M
Zminimum:1
Zmaximum:6
Xintervals:0
Yintervals:0
Includexzero:0
Includeyzero:0
Includezzero:0
Statsselect:[1, 1, 1, 1, 1, 0, 0, 0]
Probindex:[5%, 25%, 50%, 75%, 95%]
Arial, 5[Vehicle_type,Public_fr][0,0,0,1]Vehicle coste/dCapital costs of the vehicle. It is assumed here that each vehicle is bought new and driven until the end of the vehicle's lifetime. In reality, of course many cars change owners during their lifetime, and this causes variation between individual car-owners about how much their way of owning a car really causes capital costs. However, this source of variation was excluded for simpilicity. This choice can be defended with an argument that those car-owners who spend most on the capital costs, i.e. buy the most expensive cars or sell them when they are still rather new, are likely to count a smaller fraction of the capital cost of the car when comparing different modes of transport.Vehicles_needed_s*vehicle_price/vehicle_lifetime/365296,32,148,162,40,50,416,303,0,MIDM[Public_fr,Vehicle_type][0,0,0,0]Time coste/dTime costs has two parts: the cost of delays due to traffic jams; and the cost of waiting for composite vehicles. The traffic jam cost includes only the direct costs of actual delays. However, a likely much bigger cost is the need to reserve extra time because of the risk of a traffic jam. If this was included, the costs for both car and composite passengers would be smaller especially with high volumes of composite traffic.
Although this is indexed by vehicle type, it actually is indexed by mode.var a:= waiting_s/60*composite_trips_s*time_unit_cost;
a:= array(mode1,[0,sum(a,vehicle_type),0]);
{a:= if vehicle_type='Car (g)' then 0 else a;}
var c:= sum(rush_s,vehicle_type)/sum(rush_bau,vehicle_type);
c:= 1-min([(1-c)/rush_delay[rush_delay='Reduction'],1]);
c:= if period=' 6.00-20.00' then c else 0;
c:= c*Trips_per_mode*rush_delay[rush_delay='Delay']*time_unit_cost;
c:= array(Delay_cause,[c,a]);
c:= if isnan(c) then 0 else c;
array(vehicle_type,[slice(c,mode1,3),slice(c,mode1,2),0,slice(c,mode1,1)])296,320,148,162,30,65,476,5262,63,79,634,303,0,MIDM[Car_fr,Vehicle_type][Index Length][0,0,0,0]20.7.2005 Jouni Tuomisto: Vanha koodi
{index i:= ['Passengers in traffic jam','Waiting a composite vehicle'];
var a:= sum(scenarios_output,zone);
var b:= a[output1='Waiting']/60*a[output1='Trips'];
b:= if Vehicle='Car' then 0 else b*time_unit_cost;
var c:= a[output1='Link intensity',length='< 5 km'];
c:= sum(c,Vehicle)/rush_bau;
c:= 1-min([(1-c)/rush_delay[rush_delay='Reduction'],1]);
var d:= a[output1='Trips'];
d:= if periods=1 then d else 0;
d:= d*rush_delay[rush_delay='Delay']*c*time_unit_cost;
d:= array(i,[d,b]);
sum(d,d.i)}Driver coste/dSalary and social security costs of the composite vehicle drivers. We assume that the drivers are paid only when driving, not when waiting for passengers. Although this might slightly underestimate the costs, this is a common practice among hired taxi drivers, who don't own the vehicle.vehicle_km_s*driver_salary/traffic_speed296,64,148,162,12,43,433,355,0,MIDM[Period,Vehicle_type][Index Length][0,0,0,0]Driving coste/dCosts due to fuel and maintenance.vehicle_km_s*(fuel_price*fuel_consumption+car_maintenance)296,128,148,16[Period,Vehicle_type][Index Length][0,0,0,0][Length,0,Public_fr,1,Zone,1,Vehicle_type,1,Period,1]PM lethalitye/dFine particles are assumed to cause 10 deaths per 17 ton emission, a result from buses in Helsinki (1). CO2 costs are based on the estimated costs of CO2 in the greenhouse gas emission market. The true health and environmental costs are probably clearly higher than the price of the emission market.emission1[Pollutant='PM']*Pm_unit_lethality296,256,148,162,431,209,476,2242,301,50,639,305,0,MIDM[Car_fr,Length]Emission coste/dThis version calculates emission costs per drive for a 10-km drive.
Fine particles are assumed to cause 10 deaths per 17 ton emission, a result from buses in Helsinki (1). CO2 costs are based on the estimated costs of CO2 in the greenhouse gas emission market. The true health and environmental costs are probably clearly higher than the price of the emission market.var a:= 10*Emission_factor/1000*Costs_of_unit_emissi;
sum(a,Pollutant)504,248,148,162,301,50,262,305,0,MIDM[Vehicle,Pollutant]Vehicle coste/driveThis version calculates the capital costs per trip assuming that each car takes 15 drives per day.
Capital costs of the vehicle. It is assumed here that each vehicle is bought new and driven until the end of the vehicle's lifetime. In reality, of course many cars change owners during their lifetime, and this causes variation between individual car-owners about how much their way of owning a car really causes capital costs. However, this source of variation was excluded for simpilicity. This choice can be defended with an argument that those car-owners who spend most on the capital costs, i.e. buy the most expensive cars or sell them when they are still rather new, are likely to count a smaller fraction of the capital cost of the car when comparing different modes of transport.vehicle_price/vehicle_lifetime/365/15504,184,148,162,340,200,476,3122,82,146,654,409,0,MIDM[Vehicle,Car_fr]Driving coste/driveCosts due to fuel and maintenance for a 10-km drive.10*(fuel_price*fuel_consumption+car_maintenance)504,216,148,16[Period,Vehicle]Four-passenger drivee/tripCost per trip of vehicle-dependent costs (=vehicle price, driving, emissions). The numbers are compared with the largest vehicle type.var a:= array(cost_structure,[Vehicle_cost1,0,Driving_cost1,0,0,Emission_cost1,0,0,0]);
a:= sum(a,cost_structure)/4;
a/a[vehicle='Bus no change']504,296,148,242,674,6,336,558,0,MEAN[Vehicle_type,Vehicle][Index Cost_structure][0,0,1,0](a)Fillindexvar b:= indexnames(a);
a:= if sum(findintext('Length',b))>0 then a else if length='< 5 km' then a else 0;
a:= if sum(findintext('Vehicle_type',b))>0 then a else if Vehicle_type='Minibus' then a else 0;
a:= if sum(findintext('Zone',b))>0 then a else if zone=1 then a else 0;
a:= if sum(findintext('Period',b))>0 then a else if Period=' 6.00-20.00' then a else 0;
a504,384,148,122,102,90,476,326aDelay cause['Passengers in traffic jam','Waiting a composite vehicle']296,344,148,12Composite trips sslice(Scenarios_output,output1,1)64,256,148,162,102,90,476,367[Car_fr,Length][0,0,0,1]Nochange trips sslice(Scenarios_output,output1,3)64,160,148,16[Public_fr,Car_fr]Vehicle km sslice(Scenarios_output,output1,4)64,96,148,16[Car_fr,Length][0,0,0,0]Vehicles needed ssum(sum(slice(slice(Scenarios_output,output1,5),period,3),length),zone)64,32,148,162,40,50,416,303,0,MIDM[Car_fr,Vehicle_type][0,0,0,0]Parking ssum(slice(slice(Scenarios_output,output1,5),period,1),length)64,192,148,162,120,130,416,303,0,MIDM[Car_fr,Vehicle_type][0,0,0,1]Rush ssum(sum(slice(slice(Scenarios_output,output1,5),period,2),length),zone)64,320,148,162,40,50,456,304,0,MIDM[Car_fr,Vehicle_type][Index Vehicle_type][0,0,0,1]Waiting sslice(Scenarios_output,output1,6)64,288,148,16[Car_fr,Length][0,0,0,0]Cost strengthThe stakeholder-specific weights that are given to different cost types. The weight is 1 always with the following exceptions:
- Car capital costs may be <1 because the owner may need the car for other purposes than the trips considered here.
- Willingness to drive (Driver costs for car drivers) may be positive or negative depending on how the the driving is valuated.
- 'Parking' is zero for composite traffic and society, because 'Parking land' cost is then calculated.
- 'Parking land ' is zero for car passengers, because 'Parking' is then calculated.
- 'Emission costs' and 'Accidents' are not calculated for passengers because they harm people in general, not any individual specifically.
- 'Ticket' cost is calculated only for composite traffic passengers. It is not relevant for cars; and from the societal point of view, it is only a money transfer from the passenger to the service provider.Table(Cost_structure,Mode1,Stakeholder)(
Cap,Cap,
1,1,
1,1,
Drive,Drive,
1,1,
1,1,
1,1,
1,1,
1,1,
1,0,
0,0,
0,0,
0,1,
1,1,
1,1,
0,1,
0,1,
0,1,
1,1,
1,1,
1,1,
0,1,
0,1,
0,1,
0,0,
1,0,
1,0
)168,424,148,242,102,90,476,3552,387,248,416,303,0,MIDM2,134,368,725,281,0,MEANGraphtool:0
Distresol:10
Diststeps:1
Cdfresol:5
Cdfsteps:1
Symbolsize:6
Baroverlap:0
Linestyle:1
Frame:1
Grid:1
Ticks:1
Mesh:1
Scales:1
Rotation:45
Tilt:0
Depth:70
Frameauto:1
Showkey:1
Xminimum:0
Xmaximum:1
Yminimum:0
Ymaximum:1
Zminimum:0
Zmaximum:1
Xintervals:0
Yintervals:0
Includexzero:0
Includeyzero:0
Includezzero:0
Statsselect:[1,1,1,1,1,0,0,0]
Probindex:[0.05,0.25,0.5,0.75,0.95]
[Mode1,Cost_structure][Mode1,Cost_structure][0,0,0,0]Nochange frindex b:= copyindex(Scenario_description[input_var='No-change fraction']);
unique(b,b)192,88,148,12[0]index b:= copyindex(Scenario_description[input_var='Large guarantee?']);
unique(b,b)192,112,048,121,1,1,1,1,1,0,0,0,0[0]Public frThe fraction of trips that were previously performed by personal car but are performed by composite traffic in the scenario.index b:= copyindex(Scenario_description[input_var='Public fraction']);
unique(b,b)280,88,144,122,236,315,416,303,0,MIDM[1,0.9,0.8,0.7,0.6,0.5,0.4,0.3,0.2,0.1]Max sizeThe fraction of trips that were previously performed by personal car but are performed by composite traffic in the scenario.index b:= copyindex(Scenario_description[input_var='Max size']);
unique(b,b)280,136,144,122,236,315,416,303,0,MIDM[8]Min directThe fraction of trips that were previously performed by personal car but are performed by composite traffic in the scenario.index b:= copyindex(Scenario_description[input_var='Min direct load']);
unique(b,b)280,160,144,122,236,315,416,303,0,MIDM[4]Veh typesThe fraction of trips that were previously performed by personal car but are performed by composite traffic in the scenario.index b:= copyindex(Scenario_description[input_var='Vehicle types']);
unique(b,b)192,136,148,122,236,315,416,303,0,MIDM[2]DropThe fraction of trips that were previously performed by personal car but are performed by composite traffic in the scenario.index b:= copyindex(Scenario_description[input_var='Drop points/area']);
unique(b,b)280,112,144,122,236,315,416,303,0,MIDM[8]Public levelThe fraction of trips that were previously performed by personal car but are performed by composite traffic in the scenario.index b:= copyindex(Scenario_description[input_var='Public level']);
unique(b,b)192,160,148,122,236,315,416,303,0,MIDM[0]Transport costThe total cost (per day) of various cost elements calculated for each vehicle type separately.var a:= array(Cost_structure,[
if period= ' 6.00-20.00' then Vehicle_cost else 0,
sum(sum(Driver_cost,zone),length),
sum(sum(Driving_cost,zone),length),
sum(sum((If (Vehicle_type='Car (g)') Then Car_parking_cost Else 0),zone),length),
sum((if period= ' 6.00-20.00' then Parking_land_cost else 0),zone),
sum(sum(Sum(Emission_cost,Pollutant),zone),length),
sum(sum(Sum(Time_cost,Delay_cause),zone),length),
sum(sum(acc_costs,zone),length),
0]);
a280,280,148,242,644,531,476,3462,775,456,416,303,0,MIDM2,24,460,883,345,0,MIDM[Public_fr,Cost_structure][Variable Zone][0,0,0,0][Vehicle_type,3,Period,2,Cost_structure,1,Public_fr,1]Cost per tripvar a:= Transport_cost;
a:= array(mode1,[
slice(a,vehicle_type,4),
slice(a,vehicle_type,2)+slice(a,vehicle_type,3),
slice(a,vehicle_type,1)]);
a:= a[period=choose_period];
var b:= Trips_per_mode;
a:= if cost_structure='Vehicle' or cost_structure='Parking land' then sum(a,period)/sum(b,period) else a/b;
a:= if cost_structure='Ticket' and Mode1<>'Car' then ticket-group_subvention else a;
a:= if isnan(a) then 0 else a;
280,360,148,242,104,215,764,318,0,MIDM[Public_fr,Cost_structure][Index Cost_structure][0,0,0,0]Cost to stakeholderThe cost per trip for a random individual passenger. These values have been weighted by the stakeholder-specific weights (Cost strength).
The costs are first calculated for an average trip from total costs and total numbers of trips. The costs of individual car trips depend on the number of passengers. Therefore, the average cost is multiplied by the average number of passengers and divided by the number of passengers in the particular case we are looking at.var a:= mean(Group_size)/sample(Group_size);
a:= if cost_structure <>'Time' and Mode1='Car' then a else 1;
a:= a*Cost_per_trip;
a:= if isnan(a) then 0 else a;
a:= if a=inf then 0 else a;
a:= a*cost_strength;
{sum(a,cost_structure)}280,424,148,242,162,-2,792,372,0,MEANGraphtool:0
Distresol:10
Diststeps:1
Cdfresol:5
Cdfsteps:1
Symbolsize:6
Baroverlap:0
Linestyle:1
Frame:1
Grid:1
Ticks:1
Mesh:1
Scales:1
Rotation:45
Tilt:0
Depth:70
Frameauto:1
Showkey:1
Xminimum:0
Xmaximum:1
Yminimum:0
Ymaximum:1
Zminimum:0
Zmaximum:1
Xintervals:0
Yintervals:0
Includexzero:0
Includeyzero:0
Includezzero:0
Statsselect:[1,1,1,1,1,0,0,0]
Probindex:[0.05,0.25,0.5,0.75,0.95]
[Public_fr,Cost_structure][Index Public_fr][0,0,1,0]Choose periodChoice(Period,0,True)400,360,148,162,61,457,476,22452425,39321,65535['item 1']Ei toimi WTD probabilistisestidrive56,528,148,29Scenarios output# or #/hA set of scenarios organised along two indexes:
Guar is the level of composite traffic guarantee. This means that trips within a certain area will be organised by composite travel, while areas outside this guarantee remain without the service. The point in using this index is to explore whether composite traffic can be started with low profile and expanded geographically as more people start using it.
Comp_fr is the fraction of trips that were previously performed by personal car but are performed by composite traffic in the scenario.var a:= Scenario_data;
var b:= Scenario_description;
a:= if b[input_var='Car fraction']=Car_fr then a else 0;
a:= if Car_fr=1 then a[guar=7] else a;
a:= if Car_fr=1 and output1='Waiting' then 0 else a;
a:= if b[input_var='Public fraction']=public_fr then a else 0;
a:= if b[input_var='Guarantee level']=guar then a else 0;
a:= if b[input_var='Large guarantee?']=large then a else 0;
a:= if b[input_var='Public level']=public_level then a else 0;
a:= if b[input_var='No-change fraction']=nochange_fr then a else 0;
a:= if b[input_var='Max size']=max_size then a else 0;
{a:= if b[input_var='Min direct load']=min_direct then a else 0;
a:= if b[input_var='Vehicle types']=veh_types then a else 0;
a:= if b[input_var='Drop points/area']=drop then a else 0;
a:= sum(a,Scen_ind);
a:= if a=null then 0 else a;
a:= if size(car_fr)=1 then sum(a,car_fr) else a;
a:= if size(guar)=1 then sum(a,guar) else a;
a:= if size(public_fr)=1 then sum(a,public_fr) else a;
a:= if size(large)=1 then sum(a,large) else a;
a:= if size(public_level)=1 then sum(a,public_level) else a;
a:= if size(nochange_fr)=1 then sum(a,nochange_fr) else a;
a:= if size(max_size)=1 then sum(a,max_size) else a;
a:= if size(min_direct)=1 then sum(a,min_direct) else a;
a:= if size(veh_types)=1 then sum(a,veh_types) else a;
a:= if size(drop)=1 then sum(a,drop) else a;}88,32,148,242,803,17,476,7582,38,42,766,419,0,MIDMGraphtool:0
Distresol:10
Diststeps:1
Cdfresol:5
Cdfsteps:1
Symbolsize:6
Baroverlap:0
Linestyle:10
Frame:1
Grid:1
Ticks:1
Mesh:1
Scales:1
Rotation:45
Tilt:0
Depth:70
Frameauto:1
Showkey:1
Xminimum:5
Xmaximum:15
Yminimum:0
Ymaximum:1M
Zminimum:1
Zmaximum:6
Xintervals:0
Yintervals:0
Includexzero:0
Includeyzero:0
Includezzero:0
Statsselect:[1, 1, 1, 1, 1, 0, 0, 0]
Probindex:[5%, 25%, 50%, 75%, 95%]
Arial, 5[Scen_ind,Output1][Index Length][0,0,0,0]VOI and importance analysisValue of information analyses, studies on variation in the population, and other analyses on the results.jtuomistTue, Mar 27, 2001 11:26jtue12. Aprta 2005 16:3548,24720,136,048,291,1,1,1,1,1,0,0,0,01,426,18,635,491,1794,1,1,0,2,9,4744,6798,7Fig 2 Tripstrips/hFig 1 in the main text. Trips by vehicle type as a function of time when the fraction of composite trips is 50% of the current personal car trips. In this graph, you can also view other composite fractions than 0.5 when guar is set to 7, and other other levels of guarantee when composite fraction is set to 0.5.FunctionOf(var a:= Trips1_0;
var b:= Scenario_description;
a:= if b[input_var='Composite fraction']=Car_fr then a else 0;
a:= if b[input_var='Guarantee level']=guar then a else 0;
a:= if Car_fr=0 then a[guar=7] else a;
a:= a[guar=Expr];
a:= a[Car_fr=choose_comp];
a:= if b[input_var='Flexible fraction']=choose_flexible then a else 0;
a:= if b[input_var='No-change fraction']=choose_nochange then a else 0;
a:= if b[input_var='Large guarantee?']='Yes' then
(if large='Yes' then a else 0) else (if large='No' then a else 0);
a:= a[large=choose_large];
a:= sum(a,Scen_ind);
a*array(vehicle,[1,0.5,1,0.5,0.5,1]))432,96,148,242,493,4,476,5902,161,13,835,589,1,MIDM[Formnode Figure_3]Graphtool:0
Distresol:10
Diststeps:1
Cdfresol:5
Cdfsteps:1
Symbolsize:6
Baroverlap:0
Linestyle:10
Frame:1
Grid:1
Ticks:1
Mesh:1
Scales:1
Rotation:45
Tilt:0
Depth:70
Frameauto:1
Showkey:1
Xminimum:5
Xmaximum:15
Yminimum:0
Ymaximum:1M
Zminimum:1
Zmaximum:6
Xintervals:0
Yintervals:0
Includexzero:0
Includeyzero:0
Includezzero:0
Statsselect:[1, 1, 1, 1, 1, 0, 0, 0]
Probindex:[5%, 25%, 50%, 75%, 95%]
Arial, 5[Time_stat,Vehicle]EndpointEndpoints or pressures estimated.['Fraction of composite trips without change (%)','Vehicles needed (number)','Parking places needed (number)','Average vehicle flow on the 30 most busy roads (vehicles/h at 8.00-9.00 AM)','Injuries due to accidents (cases per year)','Deaths due to accidents (cases per year)','Deaths due to fine particles (cases per year)','Fine particle (<2.5 µm of diameter) emissions (kg per day)','Carbon dioxide emissions (ton per day)','Driver salaries (thousand e per day)','Vehicle capital and operational costs (thousand e per day)','Time cost (thousand e per day)','Average car trip cost to passenger (e per trip)','Average composite trip cost to passenger (e per trip)']552,128,148,122,17,81,625,3722,-3,30,512,303,0,MIDMTable 1 PressuresTable 1 in the Main text of the article. To retrieve the same table, 'Choose guar' should be set to 7, 'Choose comp' to All, and 'Choose period' to All.
Footnotes:
Mean (90% confidence interval when applicable).
If a passenger requests a trip without a transfer, the additional price to him/her will be 3 - 6 euro/trip during daytime. This cost is due to reduced efficiency in trip aggregation.
The number of vehicles and parking places is theoretical and involves the modelled trips only; a car owner may need the car for trips outside Helsinki even if he/she uses composite traffic. The true number of cars in the area was 346 400 in 2001. (1)
The current ticket prices for buses, metro, and trams are 1.70 e per trip in Helsinki and 2.90 e per trip between communities in the Helsinki metropolitan area. Note that the car trip and composite trip costs include time costs.var d:= sum(sum(sum(scenarios_output,zone),period),length);
d:= d[Car_fr=i];
var a:= d[output1='Trips by vehicle'];
a:= (slice(a,vehicle,1)+slice(a,vehicle,3)) +sum(sum(sum(no_change_trips[Car_fr=i],period),length),zone);
a:= a/d[output1='Trips',vehicle='Bus no change']*100;
var b:= sum(d[output1='Vehicles'],vehicle);
var c:= sum(d[output1='Parking lot'],vehicle);
d:= sum(d[output1='Link intensity'],vehicle);
a:= rounding(a,3);
b:= rounding(b,3);
c:= rounding(c,3);
d:= rounding(d,3);
var e:= tm(sample(acc_num[accidents='Injuries']));
var f:= tm(sample(acc_num[accidents='Deaths']));
var g:= tm(sample(pm_lethality)*365);
var h:= tm(sample(emission1[Pollutant='PM']));
var i:= tm(sample(emission1[Pollutant='CO2'])/1000);
var j:= tm((if Vehicle='Car' then 0 else sample(driver_cost))/1k);
var k:= tm(sample(vehicle_cost[guar=7])/1k+sample(driving_cost)/1k);
var l:= tm(sample(time_cost)/1k);
var x:= tm(sample(Cost_passenger));
var m:= (x[Mode1='Car']);
var n:= (x[Mode1='Composite']);
array(endpoint,[a,b,c,d,e,f,g,h,i,j,k,l,m,n])552,96,148,242,439,7,545,6212,357,353,682,303,0,MIDM2,5,2,990,352,0,MIDM[Formnode Table_4]Graphtool:0
Distresol:10
Diststeps:1
Cdfresol:5
Cdfsteps:1
Symbolsize:6
Baroverlap:0
Linestyle:1
Frame:1
Grid:1
Ticks:1
Mesh:1
Scales:1
Rotation:45
Tilt:0
Depth:70
Frameauto:1
Showkey:1
Xminimum:0
Xmaximum:1
Yminimum:0
Ymaximum:1
Zminimum:0
Zmaximum:1
Xintervals:0
Yintervals:0
Includexzero:0
Includeyzero:0
Includezzero:0
Statsselect:[1,1,1,1,1,0,0,0]
Probindex:[0.05,0.25,0.5,0.75,0.95]
[I,Endpoint]1,D,4,2,0,085,1,1,0,2,9,4744,6798,7YTV: Liikkumisen nykytila (The Present-day Traffic Situation) PJS B 2001:10 <a hfref="http://www.ytv.fi/liikenne/julk/nykytila.pdf">PDF file</a>Uncertain inputsA list of uncertain variables used in the model. This list is used to analyse the role of each variable by e.g. value-of-information analysis or importance analysis. The variables with 'V:' are not uncertain but describe variability within the population. Note that the last variable 'Blank' is NOT included in the model and therefore whatever significance is attached to this variable, is just a random effect.Table(Uncertain_var)(
Vehicle_price[Vehicle='Car'],Vehicle_lifetime[Vehicle='Car'],Fuel_price[Vehicle='Car'],Car_maintenance,Driver_salary,Rush_delay[Rush_delay='Delay'],Time_unit_cost,Trips_per_car,Emission_factor[Vehicle='Car', Pollutant='PM'],Costs_of_unit_emissi[Pollutant='PM'],Sum(Sum(Sum(Accident_costs,Period),Vehicle),Length),Cap_uncert,Drive_uncert,Group_subvention,Group_size,Cap_variation,Drive_variation,Uniform(0,1))['Pollutant levels in fish feed after lower limits (S+P)','Salmon consumption after feed limits (S+P)','Does omega-3 help CHD patients or everyone? (S)','Dose-response of health benefit (S)','Highest omega-3 dose with health benefit (S)','Current average consumption of salmon (S)','Fraction of farmed from total salmon use (S)','Omega3 content in salmon (S)','Consider pollutant or net health effect? (P)','Dieldrin concentration in farmed salmon (S)','Toxaphene concentration in farmed salmon (S)','PCB concentration in farmed salmon (S)','Farmed salmon use after recommendation (S)','Lower limits for pollutants in fish feed? (P)','Recommend restricted farmed salmon consumption? (P)']296,56,148,241,1,1,1,1,1,0,0,0,02,541,193,476,2752,525,42,465,461,0,MIDM2,148,242,582,361,0,MIDM52425,39321,65535[Self,Self]Uncertain varA list of uncertain variables used in the model.['Car price','Car lifetime','Fuel price','Vehicle maintenance','Driver salary','Delay due to rush','Unit cost of time','Trips per car','Car fine particle emission','Fine particle unit cost','Accident costs','Car capital','Willingness to drive','Group subvention','V: Car occupancy','V: Car capital','V: Willingness to drive','Blank']296,88,148,121,1,1,1,1,1,0,0,0,02,123,124,476,4692,351,356,688,342,0,MIDM2,168,178,582,361,0,MIDM[Self,Self]['Car price','Car lifetime','Fuel price','Vehicle maintenance','Driver salary','Delay due to rush','Unit cost of time','Trips per car','Car fine particle emission','Fine particle unit cost','Accident costs','Car capital','Willingness to drive','Group subvention','V: Car occupancy','V: Car capital','V: Willingness to drive','Blank']Subventione/dDirect costs occurring to the society if it subsidises the composite traffic ticket prices so much that the target level of composite fraction is reached, i.e. that that fraction of population thinks that composite traffic is equally or more economic for them than car traffic.var a:= Expected_total_varia[stakeholder='Passenger'];
a:= Linearinterp(a.i,a, car_fr {Exprchoose_comp},a.i);
(a+mean(group_subvention))*Trips_per_mode[{period=choose_period,} Mode1='Composite']176,208,148,242,102,90,476,4752,336,56,550,289,0,MIDMGraphtool:0
Distresol:10
Diststeps:1
Cdfresol:5
Cdfsteps:1
Symbolsize:6
Baroverlap:0
Linestyle:10
Frame:1
Grid:1
Ticks:1
Mesh:1
Scales:1
Rotation:45
Tilt:0
Depth:70
Frameauto:1
Showkey:1
Xminimum:1
Xmaximum:14
Yminimum:-100K
Ymaximum:909.4K
Zminimum:1
Zmaximum:2
Xintervals:0
Yintervals:0
Includexzero:0
Includeyzero:0
Includezzero:0
Statsselect:[1,1,1,1,1,0,0,0]
Probindex:[0.05,0.25,0.5,0.75,0.95]
Arial, 8[Car_fr,Length][Index Length]Cost variatione/tripThis node is a combination of variables that represent variation, not uncertainty. In other words, all variation between the Monte Carlo iterations are due to variation within the population. (However, there are actually two variables, namely Cap_uncert and Drive_uncert that represent uncertainty of capital cost of car and willingness to drive, respectively. It would be tricky to separate these from variation, and therefore this discrepancy is allowed.)var a:= mean(Group_size)/sample(Group_size);
a:= if cost_structure <>'Time' and Mode1='Car' then a else 1;
a:= a*mid(Cost_per_trip);
a:= if isnan(a) then 0 else a;
a:= a*cost_strength_variability;
a:= sum(a,cost_structure);
{a:= a[stakeholder='Passenger',length='>= 5 km'];
a[Mode1='Composite']-a[Mode1='Car']}56,136,148,242,424,37,476,5842,0,8,394,483,0,SAMP[Mode1,Run][0,0,0,0]Expected total variatione/tripCost difference of composite and car trips shown as the expectation. The X axis shows the fractiles of the total variation within the population. See also 'Expected variations'. These lines are used in Figure 2 of the main text. See 'Figure 2'.var a:= cost_variation[Mode1='Composite']-cost_variation[Mode1='Car'];
a:= variation1(a,Cost,9);
var b:= a[.varia=1/9]+(a[.varia=1/9]-a[.varia=2/9])/2;
var c:= a[.varia=9/9]+(a[.varia=9/9]-a[.varia=8/9])/2;
index i:= [0,1/18,3/18,5/18,7/18,9/18,11/18,13/18,15/18,17/18,1];
array(i,[b,a[.varia=1/9],a[.varia=2/9],a[.varia=3/9],a[.varia=4/9],a[.varia=5/9],a[.varia=6/9],a[.varia=7/9],a[.varia=8/9],a[.varia=9/9],c])176,136,148,242,102,90,476,3402,94,158,860,436,0,MIDMGraphtool:0
Distresol:10
Diststeps:1
Cdfresol:5
Cdfsteps:1
Symbolsize:6
Baroverlap:0
Linestyle:1
Frame:1
Grid:1
Ticks:1
Mesh:1
Scales:1
Rotation:45
Tilt:0
Depth:70
Frameauto:1
Showkey:1
Xminimum:0
Xmaximum:1
Yminimum:-4
Ymaximum:4
Zminimum:0.1111
Zmaximum:1
Xintervals:0
Yintervals:0
Includexzero:0
Includeyzero:0
Includezzero:0
Statsselect:[1,1,1,1,1,0,0,0]
Probindex:[0.05,0.25,0.5,0.75,0.95]
[0,0,0,0]ClassesThe number of classes in the value-of-information analysis. This is a technical parametre only, and it should be large enough. However, the samplesize should be at least 100 times larger than this to avoid random noise.17416,280,148,122,102,90,476,40552425,39321,65535VariationfractileTotal variation expressed as fractiles. See 'Cost variation'.var a:= sample(cost_variation);
a:= a[Mode1='Composite']-a[Mode1='Car'];
a:= rank(a,run)/samplesize;
slice(a[guar=7,Car_fr=0.5, stakeholder='Passenger'], period,1)56,88,148,122,41,49,476,3352,142,191,670,314,1,SAMP[Run,Length]1,D,4,2,0,0Passenger VOIe/tripValue of information analysis for the input variables with the passenger decision between composite and car traffic. The analysis calculates the expected benefit for the passenger when the uncertainty of a variable is resolved.var a:= sample(cost__variation[stakeholder='Passenger']);
a:= sum(a*sum(trip_fraction,mode1),length);
a{Voi(a,Mode1,uncertain_inputs,uncertain_var,Classes)}296,208,148,242,68,266,476,2842,28,44,735,480,0,MIDMGraphtool:0
Distresol:10
Diststeps:1
Cdfresol:5
Cdfsteps:1
Symbolsize:6
Baroverlap:0
Linestyle:1
Frame:1
Grid:1
Ticks:1
Mesh:1
Scales:1
Rotation:45
Tilt:0
Depth:70
Frameauto:1
Showkey:1
Xminimum:1
Xmaximum:20
Yminimum:-0.3
Ymaximum:0
Zminimum:1
Zmaximum:1
Xintervals:0
Yintervals:0
Includexzero:0
Includeyzero:0
Includezzero:0
Statsselect:[1,1,1,1,1,0,0,0]
Probindex:[0.05,0.25,0.5,0.75,0.95]
[Run,Car_fr]1,F,4,3,0,0[Index Comp_fr]Societal coste/dTotal societal costs including subsidies.var a:= Cost_to_stakeholder[stakeholder='Society'];
a:= a*Trips_per_mode{[period=choose_period]};
if mode1='Composite' then a+subvention else a176,280,148,242,104,11,736,486,0,MEAN[Car_fr,Mode1][Index Length][0,0,0,0]Societal VOI 0-100e/dValue of information analysis for the input variables with the societal decision about the target level of composite fraction. Each level involves a particular amount of subsidies to composite traffic to reach the target. The analysis calculates the expected benefit for the society when the uncertainty of a variable is resolved.var a:= sum(sum(sample(Societal_cost__varia),length),mode1);
a:= if Car_fr=1 then a[Car_fr=0.9] else a;
Voi(a,Car_fr,uncertain_inputs,uncertain_var,classes)296,408,148,242,506,97,476,3102,18,41,377,506,0,MIDMGraphtool:0
Distresol:10
Diststeps:1
Cdfresol:5
Cdfsteps:1
Symbolsize:6
Baroverlap:0
Linestyle:9
Frame:1
Grid:1
Ticks:1
Mesh:1
Scales:1
Rotation:45
Tilt:0
Depth:70
Frameauto:1
Showkey:1
Xminimum:1
Xmaximum:3
Yminimum:-70K
Ymaximum:0
Zminimum:1
Zmaximum:12
Xintervals:0
Yintervals:0
Includexzero:0
Includeyzero:0
Includezzero:0
Statsselect:[1,1,1,1,1,0,0,0]
Probindex:[0.05,0.25,0.5,0.75,0.95]
Arial, 6Fig 5A Societal costse/dFigure 3 top panel of the main text.
Marginal societal costs of traffic (composite+car) as a function of the fraction that composite replaces personal car trips (composite fraction). Top: Societal costs (excluding subsidies for composite traffic) during different periods of day.
To reproduce the figure in the article, set
Choose comp = All
Choose guar = 7
Choose period = All
Subsidise groups? = No
Choose large = No
Choose_nochange = 0
Uncertainty options: Sample size 5000, random seed = 98, Median Latin Hypercube
Warning: This will require > 1 MB of system memoryvar a:= sum(sum(Societal_cost,mode1)-subvention,length);
a-a[Car_fr=0]176,352,148,292,521,109,476,2712,277,24,326,548,0,MEAN[Formnode Figure_3_top2][Car_fr,Period][0,0,0,0]Fig 5B Subsidiese/dFigure 3 middle panel of the main text.
Marginal societal costs of traffic (composite+car) as a function of the fraction that composite replaces personal car trips (composite fraction). Middle: Subsidies to ticket prices needed to reach the target fraction of composite traffic (i.e., to make that fraction of current car passengers to favour composite traffic). For comparison, the current subsidies to public transportation in Helsinki area are on the range of 380 000 e per day.
The public transport subsidies in Helsinki, Espoo (incl Kauniainen), and Vantaa were 93.30, 25.95, and 19.49 million euro in 2003, which is approximately 380 000 euro per day for the whole area.
To reproduce the figure in the article, set
Choose comp = All
Choose guar = 7
Choose period = All
Subsidise groups? = No
Choose large = No
Choose_nochange = 0
Uncertainty options: Sample size 5000, random seed = 98, Median Latin Hypercube
Warning: This will require > 1 MB of system memoryvar a:= sum(subvention,length);
a56,208,148,242,120,77,476,2242,62,10,324,463,0,MIDM[Formnode Figure_3_middle2]Graphtool:0
Distresol:10
Diststeps:1
Cdfresol:5
Cdfsteps:1
Symbolsize:6
Baroverlap:0
Linestyle:10
Frame:1
Grid:1
Ticks:1
Mesh:1
Scales:1
Rotation:45
Tilt:0
Depth:70
Frameauto:1
Showkey:1
Xminimum:0
Xmaximum:1
Yminimum:-100K
Ymaximum:100K
Zminimum:1
Zmaximum:7
Xintervals:0
Yintervals:0
Includexzero:0
Includeyzero:0
Includezzero:0
Statsselect:[1,1,1,1,1,0,0,0]
Probindex:[0.05,0.25,0.5,0.75,0.95]
Arial, 6[Car_fr,Period][Index Period][Rosenberg, 2005 55 /id]
<a href="http://www.mintc.fi/oliver/upl471-Julkaisuja_2_2005.pdf">PDF file</a>Fig 5C Expandinge/dFigure 3 bottom panel of the main text.
Marginal societal costs of traffic (composite+car) as a function of the fraction that composite replaces personal car trips (composite fraction). Bottom: Societal costs (including subsidies) during daytime with increasing areal coverage of composite traffic (starting from the most densely populated areas). Both origin and destination must be in the covered area. The legend shows the number of inhabitants living in the covered area.
(To see the legend, calculate Population_guaranteed.)
To reproduce the figure in the article, set
Choose comp = All
Choose guar = All
Choose period = 6.00-20.00
Subsidise groups? = No
Choose large = No
Choose_nochange = 0
Uncertainty options: Sample size 5000, random seed = 98, Median Latin Hypercube
Warning: This will require > 1 MB of system memoryvar a:= Societal_cost;
a:= a-a[Car_fr=0];
sum(sum(a,length),mode1);56,352,148,242,411,30,476,3572,558,40,290,520,1,MEAN[Formnode Figure_3_bottom2]Graphtool:0
Distresol:10
Diststeps:1
Cdfresol:5
Cdfsteps:1
Symbolsize:6
Baroverlap:0
Linestyle:10
Frame:1
Grid:1
Ticks:1
Mesh:1
Scales:1
Rotation:45
Tilt:0
Depth:70
Frameauto:1
Showkey:1
Xminimum:0
Xmaximum:1
Yminimum:-1.2M
Ymaximum:200K
Zminimum:1
Zmaximum:1
Xintervals:0
Yintervals:0
Includexzero:0
Includeyzero:0
Includezzero:0
Statsselect:[1,1,1,1,1,0,0,0]
Probindex:[0.05,0.25,0.5,0.75,0.95]
Arial, 2[Car_fr,Guar][0,0,0,0]Fig 4 Cost variatione/tripFigure 2 in the main text. Individual variation in the cost of a composite trip compared with a personal car trip for an individual passenger. The estimates include daytime trips with 50% composite fraction scenario. The trips are divided into two groups based on length. The variation between individuals is shown on X axis, with people most in favour of composite traffic on left. The expected values across individuals are shown as lines, and the dots represent the uncertainty of the value.
Note that the lines of expectations are shown in another node, 'Expected total variation'.
To reproduce the figure in the article, set
Choose comp = 0.5
Choose guar = 7
Choose period = 6.00-20.00
Subsidise groups? = No
Choose large = No
Choose_nochange = 0
Uncertainty options: Sample size 1000, random seed = 98, Median Latin Hypercubeslice(Cost[guar=7,Car_fr=0.5,stakeholder='Passenger'],period,1)56,56,148,242,102,90,476,3852,159,34,670,538,1,SAMP[Formnode Figure_6]Graphtool:0
Distresol:10
Diststeps:1
Cdfresol:5
Cdfsteps:1
Symbolsize:4
Baroverlap:0
Linestyle:4
Frame:1
Grid:1
Ticks:1
Mesh:1
Scales:1
Rotation:45
Tilt:0
Depth:70
Frameauto:1
Showkey:1
Xminimum:0
Xmaximum:1
Yminimum:-4
Ymaximum:3
Zminimum:1
Zmaximum:2
Xintervals:0
Yintervals:0
Includexzero:0
Includeyzero:0
Includezzero:0
Statsselect:[1,1,1,1,1,0,0,0]
Probindex:[0.05,0.25,0.5,0.75,0.95]
Arial, 2[Run,Length]Variation[0,0,0,0]Coste/trip The cost difference of the composite and car trips for the passenger (negative values: composite traffic is more beneficial).var a:= sum(Cost_to_stakeholder{[stakeholder='Passenger']},cost_structure);
a:= a{[Mode1='Composite']}-a[Mode1='Car'];
a176,56,148,242,77,76,476,3252,8,10,828,422,0,MEANGraphtool:0
Distresol:10
Diststeps:1
Cdfresol:5
Cdfsteps:1
Symbolsize:6
Baroverlap:0
Linestyle:10
Frame:1
Grid:1
Ticks:1
Mesh:1
Scales:1
Rotation:45
Tilt:0
Depth:70
Frameauto:1
Showkey:1
Xminimum:0
Xmaximum:1
Yminimum:-4
Ymaximum:10
Zminimum:1
Zmaximum:7
Xintervals:0
Yintervals:0
Includexzero:0
Includeyzero:0
Includezzero:0
Statsselect:[1, 1, 1, 1, 1, 0, 0, 0]
Probindex:[5%, 25%, 50%, 75%, 95%]
Arial, 5[Drop,Mode1][0,0,0,0]Single passenger VOIe/tripSame as 'Passenger VOI' except that the value of information is estimated for the subgroup that travels alone.var a:= sample(cost__variation[stakeholder='Passenger']);
a:= sum(a*sum(trip_fraction,mode1),length);
a:= if sample(Group_size)=1 then a else 0;
Voi(a,Mode1,uncertain_inputs,uncertain_var,Classes)296,280,152,242,15,127,476,2242,393,95,352,473,0,MIDMGraphtool:0
Distresol:10
Diststeps:1
Cdfresol:5
Cdfsteps:1
Symbolsize:6
Baroverlap:0
Linestyle:1
Frame:1
Grid:1
Ticks:1
Mesh:1
Scales:1
Rotation:45
Tilt:0
Depth:70
Frameauto:1
Showkey:1
Xminimum:0
Xmaximum:1
Yminimum:0
Ymaximum:1
Zminimum:0
Zmaximum:1
Xintervals:0
Yintervals:0
Includexzero:0
Includeyzero:0
Includezzero:0
Statsselect:[1,1,1,1,1,0,0,0]
Probindex:[0.05,0.25,0.5,0.75,0.95]
1,F,4,3,0,0[Index Comp_fr]Cost \variationCost_to_stakeholder-(Cost_variation-mean(cost_variation))296,136,148,242,122,153,476,5672,257,61,680,471,1,SAMPGraphtool:0
Distresol:10
Diststeps:1
Cdfresol:5
Cdfsteps:1
Symbolsize:4
Baroverlap:0
Linestyle:4
Frame:1
Grid:1
Ticks:1
Mesh:1
Scales:1
Rotation:45
Tilt:0
Depth:70
Frameauto:1
Showkey:1
Xminimum:0
Xmaximum:1000
Yminimum:-3
Ymaximum:3
Zminimum:1
Zmaximum:2
Xintervals:0
Yintervals:0
Includexzero:0
Includeyzero:0
Includezzero:0
Statsselect:[1, 1, 1, 1, 1, 0, 0, 0]
Probindex:[5%, 25%, 50%, 75%, 95%]
Arial, 8[Run,Mode1]Variation[0,0,0,0]Societal cost \variatione/dTotal societal costs including subsidies. Here we exclude the variation so that the VOI is calculated based on uncertainty only.var a:= Cost_variation-mean(cost_variation);
a:= Cost_to_stakeholder-a;
a:= a[stakeholder='Society'];
a:= a*Trips_per_mode{[period=choose_period]};
if mode1='Composite' then a+subvention else a296,344,148,242,542,125,476,2242,154,69,736,486,1,MEANGraphtool:0
Distresol:10
Diststeps:1
Cdfresol:5
Cdfsteps:1
Symbolsize:6
Baroverlap:0
Linestyle:10
Frame:1
Grid:1
Ticks:1
Mesh:1
Scales:1
Rotation:45
Tilt:0
Depth:70
Frameauto:1
Showkey:1
Xminimum:1
Xmaximum:14
Yminimum:0
Ymaximum:600K
Zminimum:1
Zmaximum:1
Xintervals:0
Yintervals:0
Includexzero:0
Includeyzero:0
Includezzero:0
Statsselect:[1,1,1,1,1,0,0,0]
Probindex:[0.05,0.25,0.5,0.75,0.95]
Arial, 8[Car_fr,Undefined][Index Length][0,0,0,0]Other partsktluser10. touta 2005 21:2848,24552,32,148,241,0,0,1,1,1,0,,0,1,483,26,571,564,17Trips by vehicle typetrips/dNumber of trips per day by vehicle type. Set guar to 7 to view the trips as a function of composite fraction. Set comp fr to 0.5 to view the trips as a function of guarantee level.sum(Fig_2_trips,time_stat)*time_unit312,400,148,242,102,90,476,3452,13,28,811,629,1,MIDMGraphtool:0
Distresol:10
Diststeps:1
Cdfresol:5
Cdfsteps:1
Symbolsize:6
Baroverlap:0
Linestyle:10
Frame:1
Grid:1
Ticks:1
Mesh:1
Scales:1
Rotation:45
Tilt:0
Depth:70
Frameauto:1
Showkey:1
Xminimum:1
Xmaximum:7
Yminimum:0
Ymaximum:100K
Zminimum:1
Zmaximum:6
Xintervals:0
Yintervals:0
Includexzero:0
Includeyzero:0
Includezzero:0
Statsselect:[1,1,1,1,1,0,0,0]
Probindex:[0.05,0.25,0.5,0.75,0.95]
Arial, 5Fig1 flexiblevar a:= slice(time_stat,time_stat,ceil(rank(time_stat)/2)*2-1);
index tim:= sequence(0,max(time_stat),time_unit*2);
sum((if tim=a then Fig_2_trips else 0),time_stat)/2312,344,148,242,320,289,476,2242,7,15,469,587,1,MIDMCost.passengere/tripCosts per trip to the passenger.var b:= cost__variation;
var a:= (sum(sum(Trips_per_mode,length),period));
a:= Trips_per_mode/a;
a:= sum(sum(a*b,length),period);
a[stakeholder='Passenger']176,360,148,242,641,24,476,5622,132,15,788,516,1,MEAN[Car_fr,Mode1][Index Cost_structure][0,0,0,0]Fig 3 Cost by sourcee/tripThe cost per trip for a random individual passenger. These values have been weighted by the stakeholder-specific weights (Cost strength).
The costs are first calculated for an average trip from total costs and total numbers of trips. The costs of individual car trips depend on the number of passengers. Therefore, the average cost is multiplied by the average number of passengers and divided by the number of passengers in the particular case we are looking at.var a:= mean(Group_size)/sample(Group_size);
a:= if cost_structure <>'Time' and Mode1='Car' then a else 1;
a:= a*Cost_per_trip[Car_fr=0.5,guar=7];
a:= if isnan(a) then 0 else a;
a:= a*cost_strength;
var b:= Trips_per_mode[Car_fr=0.5, guar=7];
b:= b/(sum(sum(b,length),period));
sum(sum(a*b,length),period);176,480,152,242,589,137,476,4562,61,3,833,348,1,MEAN[Formnode Cost_by_type_to_sta1]Graphtool:0
Distresol:10
Diststeps:1
Cdfresol:5
Cdfsteps:1
Symbolsize:6
Baroverlap:0
Linestyle:9
Frame:1
Grid:1
Ticks:1
Mesh:1
Scales:1
Rotation:45
Tilt:0
Depth:70
Frameauto:1
Showkey:1
Xminimum:0
Xmaximum:10
Yminimum:0
Ymaximum:0.6
Zminimum:1
Zmaximum:2
Xintervals:0
Yintervals:0
Includexzero:0
Includeyzero:0
Includezzero:0
Statsselect:[1,1,1,1,1,0,0,0]
Probindex:[0.05,0.95]
Arial, 2[Cost_structure,Mode1][Index Cost_structure][0,0,0,0]Trip fractionvar a:= Trips_per_mode/sum(sum(Trips_per_mode,length),mode1);
a[period=choose_period]176,208,148,242,18,307,416,303,0,MIDM[Car_fr,Length][Index Length]No-change trips# or #/hA set of scenarios organised along two indexes:
Guar is the level of composite traffic guarantee. This means that trips within a certain area will be organised by composite travel, while areas outside this guarantee remain without the service. The point in using this index is to explore whether composite traffic can be started with low profile and expanded geographically as more people start using it.
Comp_fr is the fraction of trips that were previously performed by personal car but are performed by composite traffic in the scenario.scenarios_output[output1='Nochange trips']
{var a:= scen1_0;
a:= a[vehicle_noch='No-change',output1='Trips by vehicle'];
var b:= Scenarios1_0;
a:= if b[input_var='Flexible fraction']=flexible_fr then a else 0;
a:= a[flexible_fr=choose_flexible];
a:= if b[input_var='No-change fraction']=nochange_fr then a else 0;
a:= a[nochange_fr=choose_nochange];
a:= if b[input_var='Large guarantee?']='Yes' then
(if large='Yes' then a else 0) else (if large='No' then a else 0);
a:= a[large=choose_large];
a:= if b[input_var='Composite fraction']=Car_fr then a else 0;
a:= if b[input_var='Guarantee level']=guar then a else 0;
a:= sum(a,scenario1_0);
a:= if Car_fr=0 then a[guar=7] else a;
a:= a[Car_fr=choose_comp];
a:= a[guar=choose_guar];
a[period=choose_period]}64,424,148,242,462,53,476,5172,69,383,591,222,0,MIDMGraphtool:0
Distresol:10
Diststeps:1
Cdfresol:5
Cdfsteps:1
Symbolsize:6
Baroverlap:0
Linestyle:10
Frame:1
Grid:1
Ticks:1
Mesh:1
Scales:1
Rotation:45
Tilt:0
Depth:70
Frameauto:1
Showkey:1
Xminimum:5
Xmaximum:15
Yminimum:0
Ymaximum:1M
Zminimum:1
Zmaximum:6
Xintervals:0
Yintervals:0
Includexzero:0
Includeyzero:0
Includezzero:0
Statsselect:[1, 1, 1, 1, 1, 0, 0, 0]
Probindex:[5%, 25%, 50%, 75%, 95%]
Arial, 5[Length,Zone]No-change coste/tripCalculates the additional cost to those passengers that want a direct trip even if there is not a full vehicle available. First, the additional cost per trip of having these trips in the system is calculated. This is multiplied by the total number of trips to get the total additional cost per day. This is divided by the number of these special-service trips. Taken together, everyone must pay the price shown with No-change fraction=0, and the No-change cost is added to this price to cover the additional costs.var a:= cost_passenger-cost_passenger[nochange_fr=0];
a:= a[mode1='Composite'];
a:= a*sum(Trips_per_mode[{period=choose_period,}mode1='Composite'],length);
var b:= sum(sum(no_change_trips,length),zone);
a/b176,424,148,242,102,90,485,4302,281,258,643,324,0,MEAN[Car_fr,Period][Index Cost_structure]Cost strength variabilityThe same as Cost strength, except that this node only contains the variability, not uncertainty.
The stakeholder-specific weights that are given to different cost types. The weight is 1 always with the following exceptions:
- Car capital costs may be <1 because the owner may need the car for other purposes than the trips considered here.
- Willingness to drive (Driver costs for car drivers) may be positive or negative depending on how the the driving is valuated.
- 'Parking' is zero for composite traffic and society, because 'Parking land' cost is then calculated.
- 'Parking land ' is zero for car passengers, because 'Parking' is then calculated.
- 'Emission costs' and 'Accidents' are not calculated for passengers because they harm people in general, not any individual specifically.
- 'Ticket' cost is calculated only for composite traffic passengers. It is not relevant for cars; and from the societal point of view, it is only a money transfer from the passenger to the service provider.Table(Cost_structure,Mode1,Stakeholder)(
Cap_variation,Cap_variation,
1,1,
1,1,
(-Drive_variation),Drive_variation,
1,1,
1,1,
1,1,
1,1,
1,1,
1,0,
0,0,
0,0,
0,1,
1,1,
1,1,
0,1,
0,1,
0,1,
1,1,
1,1,
1,1,
0,1,
0,1,
0,1,
0,0,
1,0,
1,0
)312,456,148,242,102,90,476,4222,826,255,416,303,0,MIDM2,564,215,350,281,0,MEAN[Mode1,Cost_structure][Mode1,Cost_structure]Most of the VOI (esp. car occupancy) is actually in variables known to the passengerMost of the VOI (esp. car occupancy) is actually in variables known to the passenger.passenger_voi_and_voc456,208,148,6365535,65532,19661Most of the VOI is actually VOC=value of consensusMost of the VOI is actually VOC=value of consensus. This means that VOI is calculated for an input variable that is not actually unknown, but it reflects true variability in the population. Therefore the reduction of the spread of this variable does not mean that uncertainty is decreased. It means that the variability is decreased, i.e. that the population is approaching consensus.Societal_voi_and_voc336,128,148,4665535,65532,19661Outcome ImportanceSpearman rImportance analysis of the uncertain input variables. It is a Spearman rank correlation between the input variables and the outcome ('Cost').Abs( RankCorrel( Uncertain_inputs,Cost) )64,120,148,241,1,1,1,1,1,0,0,0,02,127,41,402,453,0,MIDM[Length,Uncertain_var]UncertaintiesfractileUncertain input variables standardised as fractiles.rank(uncertain_inputs,run)/samplesize64,72,148,122,97,189,665,420,0,SAMP[Run,Uncertain_var]Expected variationse/tripCost difference of composite and car trips shown as the expectation. The X axis shows the fractiles of one uncertain variable. If there is a trend, this varible has a large impact on the cost difference. See also 'Cost by uncertainty'.variation1(uncertain_inputs,Cost,9)64,240,148,242,116,222,476,2242,12,12,607,474,1,MIDM[Car_fr,Uncertain_var]Costs \car occupancye/tripAn alternative way of calculating costs given a certain input variable ('Car occupancy' in this case).var classes:= 100;
index varia:= 1..classes;
var c:= getfract(Group_size,varia/classes);
average(for x[]:= c do (whatif(Costs__cap,Group_size,x)),varia)176,304,148,242,45,0,833,212,1,SAMPGraphtool:0
Distresol:10
Diststeps:1
Cdfresol:5
Cdfsteps:1
Symbolsize:6
Baroverlap:0
Linestyle:10
Frame:1
Grid:1
Ticks:1
Mesh:1
Scales:1
Rotation:45
Tilt:0
Depth:70
Frameauto:1
Showkey:1
Xminimum:0
Xmaximum:100
Yminimum:-4
Ymaximum:4
Zminimum:1
Zmaximum:1
Xintervals:0
Yintervals:0
Includexzero:0
Includeyzero:0
Includezzero:0
Statsselect:[1, 1, 1, 1, 1, 0, 0, 0]
Probindex:[5%, 25%, 50%, 75%, 95%]
Arial, 6Costs \cape/tripAn alternative way of calculating costs given a certain input variable ('Cap' in this case).var classes:= 100;
index varia:= 1..classes;
var c:= getfract(cap,varia/classes);
average(for x[]:= c do (whatif(Cost,cap,x)),varia)64,304,148,242,47,207,833,237,1,SAMPGraphtool:0
Distresol:10
Diststeps:1
Cdfresol:5
Cdfsteps:1
Symbolsize:6
Baroverlap:0
Linestyle:10
Frame:1
Grid:1
Ticks:1
Mesh:1
Scales:1
Rotation:45
Tilt:0
Depth:70
Frameauto:1
Showkey:1
Xminimum:0
Xmaximum:100
Yminimum:-4
Ymaximum:4
Zminimum:1
Zmaximum:1
Xintervals:0
Yintervals:0
Includexzero:0
Includeyzero:0
Includezzero:0
Statsselect:[1, 1, 1, 1, 1, 0, 0, 0]
Probindex:[5%, 25%, 50%, 75%, 95%]
Arial, 6Cost by uncertaintye/tripCost difference of composite and car trips shown as a scatter plot. The X axis shows the fractiles of one uncertain variable. If there is a trend, this varible has a large impact on the cost difference.Cost64,40,148,242,82,65,830,529,1,SAMPGraphtool:0
Distresol:10
Diststeps:1
Cdfresol:5
Cdfsteps:1
Symbolsize:4
Baroverlap:0
Linestyle:4
Frame:1
Grid:1
Ticks:1
Mesh:1
Scales:1
Rotation:45
Tilt:0
Depth:70
Frameauto:1
Showkey:1
Xminimum:0
Xmaximum:1
Yminimum:-2.5
Ymaximum:2.5
Zminimum:1
Zmaximum:1
Xintervals:0
Yintervals:0
Includexzero:0
Includeyzero:0
Includezzero:0
Statsselect:[1, 1, 1, 1, 1, 0, 0, 0]
Probindex:[5%, 25%, 50%, 75%, 95%]
Arial, 6[Run,Undefined]UncertaintiesS Costs \car occupancye/tripAn alternative way of calculating costs given a certain input variable ('Car occupancy' in this case).var classes:= 100;
index varia:= 1..classes;
var c:= getfract(Group_size,varia/classes);
average(for x[]:= c do (whatif(societal_cost,Group_size,x)),varia)64,184,148,242,45,0,833,212,1,SAMPGraphtool:0
Distresol:10
Diststeps:1
Cdfresol:5
Cdfsteps:1
Symbolsize:6
Baroverlap:0
Linestyle:10
Frame:1
Grid:1
Ticks:1
Mesh:1
Scales:1
Rotation:45
Tilt:0
Depth:70
Frameauto:1
Showkey:1
Xminimum:0
Xmaximum:100
Yminimum:-4
Ymaximum:4
Zminimum:1
Zmaximum:1
Xintervals:0
Yintervals:0
Includexzero:0
Includeyzero:0
Includezzero:0
Statsselect:[1, 1, 1, 1, 1, 0, 0, 0]
Probindex:[5%, 25%, 50%, 75%, 95%]
Arial, 6Cost classifiedvar x:= 9;
var a:= sample(cost_variation);
a:= a[stakeholder='Passenger'];
a:= a[mode1='Composite']-a[mode1='Car'];
index vari:= sequence(1/x,1,1/x);
var in:= ceil(rank(a,run)*x/samplesize)/x;
a:= if in=Vari then a else 0;
176,40,148,24[Run,Car_fr]Classified passenger VOIThe iterations are classified into 9 groups based on variability, and these groups are calculated separately. This is reasonable, because there is no point in calculating a common VOI for two individuals, who are on opposite extremes of the variation according to favourness of composite traffic. However, both Passenger VOI and Single passenger VOI are doing this (except that the latter matches for the most important variating variable).var a:= array(Mode1,[sample(Cost_classified)*9,0]);
a:= sum(a*sum(trip_fraction,mode1),length);
Voi(a,Mode1,uncertain_inputs,uncertain_var,Classes)336,40,152,242,389,72,366,497,0,MIDMSocietal VOI and VOCe/dValue of information analysis for the input variables with the societal decision about the target level of composite fraction. Each level involves a particular amount of subsidies to composite traffic to reach the target. The analysis calculates the expected benefit for the society when the uncertainty of a variable is resolved.var a:= sum(sum(sample(Societal_cost),length),mode1);
a:= if Car_fr=1 then a[Car_fr=0.9] else a;
Voi(a,Car_fr,uncertain_inputs,uncertain_var,classes)176,128,148,242,581,43,377,506,0,MIDMGraphtool:0
Distresol:10
Diststeps:1
Cdfresol:5
Cdfsteps:1
Symbolsize:6
Baroverlap:0
Linestyle:9
Frame:1
Grid:1
Ticks:1
Mesh:1
Scales:1
Rotation:45
Tilt:0
Depth:70
Frameauto:1
Showkey:1
Xminimum:1
Xmaximum:17
Yminimum:-225K
Ymaximum:0
Zminimum:1
Zmaximum:2
Xintervals:0
Yintervals:0
Includexzero:0
Includeyzero:0
Includezzero:0
Statsselect:[1,1,1,1,1,0,0,0]
Probindex:[0.05,0.25,0.5,0.75,0.95]
Arial, 6Passenger VOI and VOCe/tripValue of information analysis for the input variables with the passenger decision between composite and car traffic. The analysis calculates the expected benefit for the passenger when the uncertainty of a variable is resolved.var a:= sample(cost[stakeholder='Passenger']);
a:= sum(a*sum(trip_fraction,mode1),length);
a:= array(mode1,[a,0]);
Voi(a,Mode1,uncertain_inputs,uncertain_var,Classes){missing ')'}312,208,148,242,482,80,398,480,0,MIDMGraphtool:0
Distresol:10
Diststeps:1
Cdfresol:5
Cdfsteps:1
Symbolsize:6
Baroverlap:0
Linestyle:1
Frame:1
Grid:1
Ticks:1
Mesh:1
Scales:1
Rotation:45
Tilt:0
Depth:70
Frameauto:1
Showkey:1
Xminimum:0
Xmaximum:1
Yminimum:0
Ymaximum:1
Zminimum:0
Zmaximum:1
Xintervals:0
Yintervals:0
Includexzero:0
Includeyzero:0
Includezzero:0
Statsselect:[1,1,1,1,1,0,0,0]
Probindex:[0.05,0.25,0.5,0.75,0.95]
1,F,4,3,0,0Cost \variationvar a:= cost_variation[Mode1='Composite']-cost_variation[Mode1='Car'];
a:= rank(a,run)/samplesize;
var b:= expected_total_varia;
a:= Linearinterp(b.i,b, a,b.i);
cost-a424,344,148,242,102,90,476,3752,257,61,680,471,1,SAMPGraphtool:0
Distresol:10
Diststeps:1
Cdfresol:5
Cdfsteps:1
Symbolsize:4
Baroverlap:0
Linestyle:4
Frame:1
Grid:1
Ticks:1
Mesh:1
Scales:1
Rotation:45
Tilt:0
Depth:70
Frameauto:1
Showkey:1
Xminimum:0
Xmaximum:1000
Yminimum:-3
Ymaximum:3
Zminimum:1
Zmaximum:2
Xintervals:0
Yintervals:0
Includexzero:0
Includeyzero:0
Includezzero:0
Statsselect:[1, 1, 1, 1, 1, 0, 0, 0]
Probindex:[5%, 25%, 50%, 75%, 95%]
Arial, 8[Run,Length]Fig 6A Passenger VOIpassenger_voi416,208,148,292,17,59,799,413,0,MIDM[Formnode Fig_6a_passenger_vo1]Graphtool:0
Distresol:10
Diststeps:1
Cdfresol:5
Cdfsteps:1
Symbolsize:6
Baroverlap:0
Linestyle:9
Frame:1
Grid:1
Ticks:1
Mesh:1
Scales:1
Rotation:45
Tilt:0
Depth:70
Frameauto:1
Showkey:1
Xminimum:1
Xmaximum:3
Yminimum:-70K
Ymaximum:0
Zminimum:1
Zmaximum:12
Xintervals:0
Yintervals:0
Includexzero:0
Includeyzero:0
Includezzero:0
Statsselect:[1,1,1,1,1,0,0,0]
Probindex:[0.05,0.25,0.5,0.75,0.95]
Fig 6B Societal VOISocietal_voi_0_100176,408,148,242,563,94,416,435,0,MIDM[Formnode Fig_6b_societal_voi1]Societal VOI 0 or 50e/dValue of information analysis for the input variables with the societal decision about the target level of composite fraction. Each level involves a particular amount of subsidies to composite traffic to reach the target. The analysis calculates the expected benefit for the society when the uncertainty of a variable is resolved.var a:= sum(sum(sample(Societal_cost__varia),length),mode1);
index comp:= [0,0.5];
a:= a[Car_fr=comp];
Voi(a,comp,uncertain_inputs,uncertain_var,classes)416,408,148,242,18,41,377,506,0,MIDMGraphtool:0
Distresol:10
Diststeps:1
Cdfresol:5
Cdfsteps:1
Symbolsize:6
Baroverlap:0
Linestyle:9
Frame:1
Grid:1
Ticks:1
Mesh:1
Scales:1
Rotation:45
Tilt:0
Depth:70
Frameauto:1
Showkey:1
Xminimum:1
Xmaximum:3
Yminimum:-70K
Ymaximum:0
Zminimum:1
Zmaximum:12
Xintervals:0
Yintervals:0
Includexzero:0
Includeyzero:0
Includezzero:0
Statsselect:[1,1,1,1,1,0,0,0]
Probindex:[0.05,0.25,0.5,0.75,0.95]
Arial, 6(a:probtype)Tma:= if size(a)=size(sum(a,length)) then a else sum(a,length);
a:= if size(a)=size(sum(a,vehicle)) then a else sum(a,vehicle);
a:= if size(a)=size(sum(a,period)) then a else sum(a,period);
a:= rounding(mean(a),3)&' ('& rounding(Getfract(a,0.05),3)&'-'& rounding(getfract(a,0.95),3)&')';
a:= if a='NAN (NAN-NAN)' then '' else a;
a:= a;
a[Car_fr=i]552,176,148,122,7,86,476,512ai[0,0.25,0.5,0.75,1]552,152,148,12Trip dataThis module calculates the trip rate for each origin-destination pair (129^2 pairs) and for each time point (12 min intervals resulting in 120 time points) based on trip data from three separate hours (morning rush, midday, afternoon rush) and time activity (based on diaries) in traffic along 24 hours.
The total number of trips equals the number of car trips in Helsinki area on a working day in 2000. All scenarios have the same street strucure and number of trips with a particular origin, destination, and time. The trips are divided into car trips and composite trips differently in each scenario based on two variables. Composite fraction is the percentage of the trips that are handled by composite traffic; the remaining trips are handled by personal cars. Guaranteed area defines the area where composite traffic is provided (i.e. the area where you are guaranteed to get a composite vehicle if you want one). The default assumption is that both the origin AND the destination must be in the guaranteed area, but it is also easy to evaluate scenarios where the guarantee covers all trips in the Helsinki area as long as either the origin OR the destination is in the guaranteed area.
The model calculates the expected number of trips for each origin-destination-time cell, and picks one random number from Poisson distributioin based on the expectation. After that, the model is deterministic all the way to Outputs node.jtue26. Junta 2003 12:49jtue18. elota 2004 18:1248,24360,136,148,241,1,1,1,1,1,0,0,0,01,387,62,605,390,172,244,212,476,362Arial, 13Adjusted trip ratetrips/time unitCalculates the traffic volume for each time point of the day. Adjusting is taken into account to yield results where the population in an area is not much different after the day.var g:= unadjusted_trip_rate;
{index x:= copyindex(From);
var b:= 0;
var c:= 0;
var e:= 0;
var a:= sum(Unadjusted_trip_rate,time);
b:= sum(a,From);
b:= b[To1=From];
c:= sum(a,To1);
c:= (b-c)*a/sum(a,To1);
e:= if c<0 then -c else 0;
c:= if c<0 then 0 else c;
e:= e[From=x,To1=From];
e:= e[x=To1];
a:= c+e;
var g:= if time>7 and time<19 then 1 else 0;
g:= g/sum(g,time);
g:= Unadjusted_trip_rate+a*g;}
g:= g/sum(sum(sum(g,from),to1),time)*total_trips;
{var h:= if rank(time)/2 = floor(rank(time)/2) then g *scenario_input[input_var='Flexible fraction'] else 0;
var i:= h[time=time+time_unit] ;
i:= if i=null then 0 else i;
g:= g+i-h;
if isnan(g) then 0 else g}168,176,148,242,320,0,476,6082,14,239,993,392,0,MIDMGraphtool:0
Distresol:10
Diststeps:1
Cdfresol:5
Cdfsteps:1
Symbolsize:6
Baroverlap:0
Linestyle:1
Frame:1
Grid:1
Ticks:1
Mesh:1
Scales:1
Rotation:45
Tilt:0
Depth:70
Frameauto:1
Showkey:1
Xminimum:0
Xmaximum:1
Yminimum:0
Ymaximum:1
Zminimum:0
Zmaximum:1
Xintervals:0
Yintervals:0
Includexzero:0
Includeyzero:0
Includezzero:0
Statsselect:[1,1,1,1,1,0,0,0]
Probindex:[0.05,0.25,0.5,0.75,0.95]
[Time,From][Time,From]13.7.2006 Jouni Tuomisto
Poistin Felxible fractionin toiminnasta, koska sitä en ole koskaan tarvinnut, ja toteutustapa tuntuu nyt huonolta. Ehkä koodinkin voisi poistaa kokonaan, mutten sitä vielä tee.(param1, param2; suurind, pienind:indextype;indtieto)Si_piA function used to divide aggragate data into its disaggregate units based on weighting factors.var a:=sum((if indtieto=Suurind then param2 else 0), pienind);
a:= sum((if indtieto=suurind then a else 0), suurind);
a:= param2/a;
a:= if indtieto=suurind then param1*a else 0;
sum(a, suurind)512,24,148,122,36,83,476,372param1,param2,suurind,pienind,indtietoPlace1The place where the trip ends.copyindex(Place)512,112,148,122,120,130,416,303,0,MIDM['Home','Workplace','Business','School','Other']PlaceThe place where the trip origines/ends. Workplace is a trip to/from the workplace; business is a work-related trip outside the workplace.['Home','Workplace','Business','School','Other']512,88,148,122,704,209,476,464['Home','Workplace','Business','School','Other']Unadjusted trip ratetrips/time unitCalculates the traffic volume for each time point of the day. First, the matrix is selected based on the Base_time Name column, and then the numbers are scaled as the proportion of the traffic activity per each hour and the peak hour for which the matrix was calculated.var c:= Select_trip_matrix;
c:= c[area1=From,area2=To1];
c:= if c=null then 0 else c;
c:= cubicinterp(hour,c,time,hour)168,96,148,242,454,141,476,3582,10,136,678,514,0,MIDMGraphtool:0
Distresol:10
Diststeps:1
Cdfresol:5
Cdfsteps:1
Symbolsize:6
Baroverlap:0
Linestyle:10
Frame:1
Grid:1
Ticks:1
Mesh:1
Scales:1
Rotation:45
Tilt:0
Depth:70
Frameauto:1
Showkey:1
Xminimum:0
Xmaximum:25
Yminimum:0
Ymaximum:180
Zminimum:1001
Zmaximum:1012
Xintervals:0
Yintervals:0
Includexzero:0
Includeyzero:0
Includezzero:0
Statsselect:[1, 1, 1, 1, 1, 0, 0, 0]
Probindex:[5%, 25%, 50%, 75%, 95%]
Arial, 6[Time,From][To1,From]All tripstrips/time unitCalculates number of individuals in the composite traffic and in car traffic for each route and time. Composite traffic may be restricted by area or by the fraction of trips that switch from car traffic to composite traffic.var a:= adjusted_trip_rate;
a:= if isnan(a) then 100u else a;
a:= if a=0 then 100u else a;
a:= slice(sample(Poisson(a)),run,1);
var g:= Scenario_input[input_var='Guarantee level'];
var car:= Scenario_input[input_var='Car fraction'];
var pub:= Scenario_input[input_var='Public fraction'] *Bus_existence;
var b:= guaranteed_areas;
var c:= From&'';
b:= if findintext(c,Regions) then b else 0;
b:= sum(b,Region);
b:= b[guarantee=g];
b:= if Scenario_input[input_var='Large guarantee?']='Yes' then b+b[From=To1] else b*b[From=To1];
b:= if b>0 then 1 else 0;
car:= slice(sample(binomial(a[mode1='Car'],b*car)),run,1);
pub:= slice(sample(binomial(a[mode1='Public'],b*pub)),run,1);
array(Mode1,[car,sum(a,mode1)-car-pub,pub])288,176,148,242,544,169,476,4932,19,36,435,468,0,MIDM[To1,From][Index Mista]Flowpassengers/time unitPassenger flow at each point. This is a sum of people who start, continue or end their trip from or to here.var a:= From&'';
var c:= sum(All_trips[Mode1='Composite'],time);
for x[]:= a do (
var b:= (if findintext(x,Route_matrix)>0 then c else 0);
sum(sum(b,From),To1) )400,176,148,242,17,204,476,3162,142,149,654,249,0,MIDM[To1,From]Transfer pointThe most busy point along the trip. In a case where there is no direct composite vehicle driving from the origin to the destination, the passenger is dropped at this point, and the latter part of the trip is organised separately.index etappi:= 1..max(max((textlength(route_matrix)+1)/5,From),To1);
var a:= sum(Flow,To1);
var b:= '0*'&Route_matrix&'*0';
b:= for x[]:= b do slice(splittext(x,','),etappi);
var c:= a[From=evaluate(b)];
var d:= if istext(c) or isnumber(c) then c else 0;
c:= argmax(d,etappi);
c:= if max(d,etappi)=0 or c=1 then '' else b[etappi=c]&',';
From&','&c&To1400,240,148,242,504,112,476,5132,43,10,972,486,0,MIDM[To1,From]GuaranteeA dummy index.[1,2,3,4,5,6,7]288,272,148,122,102,90,476,5332,104,114,416,494,0,MIDM[1,2,3,4,5,6,7]Guaranteed areasGuarantee means that any trip within the specified region is organised by the composite traffic, if wanted. 1=guarantee, 0=no guarantee. The default assumption is that both the origin AND the destination must be in the guaranteed area, but it is also easy to evaluate scenarios where the guarantee covers all trips in the Helsinki area as long as either the origin OR the destination is in the guaranteed area.Table(Guarantee,Region)(
0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,
0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,
0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,
0,0,1,1,0,0,0,0,1,1,1,1,1,1,1,
0,0,1,1,0,1,0,1,1,1,1,1,1,1,1,
1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
)288,240,148,242,102,90,476,4342,354,113,582,448,0,MIDM2,49,233,589,346,0,MIDM52425,39321,65535[Guarantee,Region][Guarantee,Region]Total tripstripsTotal number of trips travelled in a personal car in Helsinki Metropolitan area during a working day. The total number of trips is 2.9 million, and 44% of them are by personal cars.
Trips by traffic mode on weekday in the Helsinki metropolitan area in 2000.
Total trips 2.9 million
22 % Walking
7 % Cycling
16 % Bus
3 % Tram
3 % Train
4 % Metro
34 % Personal car (driver)
10 % Personal car (passenger) and taxi2.9M*array(mode1,[0.44,0,0.16+0.03+0.04])56,176,148,242,102,90,476,4782,40,50,416,303,0,MIDM65535,52427,65534YTV: Helsingin seudun nykytila (The Current State of Helsinki Region) PJS B 2002:1 <a hfref="http://www.ytv.fi/seutukeh/pks/pks2025/nykytila.pdf">PDF file</a>PopulationinhabitantsPopulation of the Helsinki Metropolitan Area by area in 2003.Table(Area1)(
389,10.248K,8215,882,6768,4157,11.62K,761,2407,3401,13.137K,14.569K,8705,6832,4746,0,3542,2284,15.89K,7028,11.8K,6825,3344,5755,10.28K,19K,9940,7288,12.956K,12.983K,10.358K,4523,8375,12.656K,5284,8470,13.653K,6422,8695,3549,8782,4169,11.435K,10.766K,2122,5480,7962,11.615K,10.91K,7636,5795,3710,16.146K,9493,8819,8331,11.226K,4023,8631,28.283K,5951,8259,16.458K,13.495K,12,829,9,3235,9228,6191,3145,7835,8819,16.405K,14.91K,6105,8003,15.762K,14.608K,2209,2888,12.29K,7692,3475,8069,2237,5239,8905,9199,8253,15.238K,5847,5934,1845,4671,549,3999,572,3579,9299,6466,18.695K,14.052K,2140,4118,2619,112,3145,3465,215,47,1807,10.396K,4301,11.36K,4840,2895,1346,3723,8338,2620,5403,3375,9873,12.478K,3167,4698,14.244K,9899,0)56,240,148,242,388,82,476,4592,415,198,416,481,0,MIDM2,510,11,258,615,0,MIDM65535,52427,65534[Index Area1]Seutu-CD '03. YTV (The Helsinki Metropolitan Area Council), Helsinki, 2004.Population guaranteedinhabitantsNumber of inhabitants in the area in which the composite traffic operates.var b:= guaranteed_areas;
var c:= From&'';
b:= if findintext(c,Regions) then b else 0;
{b:= sum(b,Region);}
b:= if b>0 then population[area1=from] else 0;
sum(b,from)168,240,148,242,480,131,476,4402,202,71,609,369,0,MIDM[Guarantee,Region][Index Region]Areal surfacearbitraryThe areal surface of each area. (A rough classification).Table(Region)(
7,4,3,2.5,5,2,3,1,1,1,1,1,1,2,3)56,304,148,242,541,153,416,352,0,MIDM2,526,136,416,386,0,MIDM65535,52427,65534Based on rough estimates with a map on scale 1:40000.Population densityarbitraryPopulation density in each area. (A rough classification.)var c:= From&'';
var b:= if findintext(c,Regions) then 1 else 0;
b:= if b>0 then population[area1=from] else 0;
sum(b,from)/areal_surface168,304,148,242,481,162,476,4002,93,219,954,423,0,MIDM[From,Region]Modelled trip rateTrip matrix is sthe same as in Tuomisto and Tainio, 2005.jtue13. Febta 2003 16:03ktluser25. touta 2005 12:3048,24168,32,148,241,1,1,1,1,1,0,0,0,01,46,136,-382,408,17Arial, 13Trips municipality1000 tips/dOne-way trips from one municipality to another.Table(Municipality,Municipality1)(
223,(365/2),(130/2),(95/2),
(365/2),332,(103/2),(117/2),
(130/2),(103/2),320,(49/2),
(95/2),(117/2),(49/2),179
)56,64,148,242,422,91,476,5131,77,139,758,383,0,MIDM2,52,332,708,188,0,MIDM65535,52427,65534[Self,Municipality1][Municipality,Municipality1][Index Suuralue]YTV: Liikkumisen nykytila. Pääkaupunkiseudun julkaisusarja B 2001:10. Fig 6. <a href="http://www.ytv.fi/NR/rdonlyres/F6B8A4F8-C394-4972-A1DE-C64E2B69EE6D/0/nykytila_B2001_10.pdf>PDF file</a>Trips place1000 trips/dOne-way trips from one place to another (such as home, work etc).Table(Place,Place1)(
29,(642/2),(67/2),(283/2),(1315/2),
(642/2),4,(71/2),(9/2),(184/2),
(67/2),(71/2),21,(1/2),(21/2),
(283/2),(9/2),(1/2),2,(50/2),
(1315/2),(184/2),(21/2),(50/2),193
)56,176,148,242,402,104,476,6032,44,37,504,196,0,MIDM65535,52427,65534[Place,Place1][Place,Place1][Index Kohde]YTV: Liikkumisen nykytila. Pääkaupunkiseudun julkaisusarja B 2001:10. Fig 7. <a href="http://www.ytv.fi/NR/rdonlyres/F6B8A4F8-C394-4972-A1DE-C64E2B69EE6D/0/nykytila_B2001_10.pdf>PDF file</a>Trips place&modefractionThe distribution of trips among transportation modes.Table(Place,Place1,Mode2)(
0.34,0.19,0.46,0.01,
0.15,0.39,0.46,0,
0.34,0.19,0.46,0.01,
0.42,0.42,0.15,0.01,
0.34,0.19,0.46,0.01,
0.15,0.39,0.46,0,
0.25,0.24,0.5,0.01,
0.25,0.24,0.5,0.01,
0.25,0.24,0.5,0.01,
0.25,0.24,0.5,0.01,
0.34,0.19,0.46,0.01,
0.25,0.24,0.5,0.01,
0.25,0.24,0.5,0.01,
0.25,0.24,0.5,0.01,
0.25,0.24,0.5,0.01,
0.42,0.42,0.15,0.01,
0.25,0.24,0.5,0.01,
0.25,0.24,0.5,0.01,
0.25,0.24,0.5,0.01,
0.25,0.24,0.5,0.01,
0.34,0.19,0.46,0.01,
0.25,0.24,0.5,0.01,
0.25,0.24,0.5,0.01,
0.25,0.24,0.5,0.01,
0.25,0.24,0.5,0.01
)64,288,148,242,377,111,476,4411,494,125,416,303,0,MIDM2,27,185,456,199,0,MIDM65535,52427,65534[Mode2,Place1][Place,Place1][Index Kohde]YTV: Liikkumisen nykytila. Pääkaupunkiseudun julkaisusarja B 2001:10. Fig 8. <a href="http://www.ytv.fi/NR/rdonlyres/F6B8A4F8-C394-4972-A1DE-C64E2B69EE6D/0/nykytila_B2001_10.pdf>PDF file</a>Trips munic&modetrips/d/inhNumber of trips per inhabitant of each transportation mode in different municipalities. These data are not used in the model.Table(Municipality,Mode2)(
1.31,1.1,0.93,0.03,
0.89,1.01,1.34,0.03,
0.92,0.72,2.03,0.03,
0.92,0.73,1.67,0.05
)64,368,148,242,491,162,476,5511,136,146,595,314,0,MIDM2,30,208,649,187,0,MIDM65535,52427,65534[Mode2,Self][Municipality,Mode2][Index Suuralue]YTV: Liikkumisen nykytila. Pääkaupunkiseudun julkaisusarja B 2001:10. Fig 9. <a href="http://www.ytv.fi/NR/rdonlyres/F6B8A4F8-C394-4972-A1DE-C64E2B69EE6D/0/nykytila_B2001_10.pdf>PDF file</a>Fraction pub tr municfractionThe fraction of public transportation in municipalities. These data are not used in the model.Table(Municipality,Municipality1)(
0.64,0.59,0.5,0.57,
0.59,0.33,0.24,0.21,
0.5,0.24,0.22,0.14,
0.57,0.21,0.14,0.23
)64,424,148,242,102,90,476,4711,200,210,666,291,0,MIDM1,200,210,752,301,1,MIDM65535,52427,65534[Self,Municipality1][Self,Municipality1]YTV: Liikkumisen nykytila. Pääkaupunkiseudun julkaisusarja B 2001:10. Fig 6. <a href="http://www.ytv.fi/NR/rdonlyres/F6B8A4F8-C394-4972-A1DE-C64E2B69EE6D/0/nykytila_B2001_10.pdf>PDF file</a>Place weight by hourA rough weighting of different trips along the day. The purpose of this node is to take into account the fact that residences and workplaces are located differently in the area, and therefore the different trips occur unevenly in time and space.var a:= table(Time_of_day)(0.1,0.3,1,0.1,0.1);
var c:= table(Time_of_day)(1,0.3,0.2,0.1,0.1);
a:= (if Place='Workplace' or Place='Business' then a else if Place1='Workplace' or Place1='Business' then c else 1);
a:= a[Time_of_day=Time_of_day_by_hour];
a/sum(a,hour)512,96,148,242,534,55,476,5702,400,26,509,574,0,MIDM52425,39321,65535[Place1,Hour][Index Tunti]MunicipalityMunicipalities in the Helsinki metropolitan area. Helsinki is divided into two parts; Kauniainen is together with Espoo.['Helsinki, downtown','Helsinki, suburbs','Espoo, Kauniainen','Vantaa']56,96,148,122,243,104,476,4372,17,221,416,303,0,MIDM['Helsinki, downtown','Helsinki, suburbs','Espoo, Kauniainen','Vantaa']Municipality1The same as Municipality; this index is used as the destination.copyindex(Municipality)56,120,148,122,451,144,476,4212,72,82,416,303,0,MIDM['Helsinki, downtown','Helsinki, suburbs','Espoo, Kauniainen','Vantaa']ModeThe modes of transportation.['Kevyt liikenne','Joukkoliikenne','Henkilöauto','Muu']64,320,148,122,102,90,476,446['Kevyt liikenne','Joukkoliikenne','Henkilöauto','Muu']Time of dayTime of day['Morning','Day','Afternoon','Evening','Night']512,128,148,122,183,445,242,306Time in trafficmin/hTime spent in personal car traffic in Helsinki. Based on personal diaries of adult subjects in Expolis study in 1996-97.Table(hour)(
0.5434,0.3511,0.2547,0.2885,0.1949,0.4356,1.521,4.747,5.118,2.106,1.892,1.663,1.966,1.91,2.608,3.477,6.161,5.567,3.811,2.833,2.158,1.254,0.7295,0.5768)400,240,148,242,161,264,476,4282,136,28,416,569,0,MIDM2,13,59,490,544,1,MIDM65535,52427,65534[Index Tunti]Anu Kousa, Expolis database 12.11.2002.Car tripstrips/dCar trips per day.var a:= Trips_place*Trips_place_mode*1000;
a[Mode2='Henkilöauto']176,176,148,242,108,133,476,4622,13,254,489,204,0,MIDM[Place,Place1]Time of day by hourTime of day by hourTable(Hour)(
'Night','Night','Night','Night','Night','Night','Morning','Morning','Morning','Day','Day','Day','Day','Day','Day','Afternoon','Afternoon','Afternoon','Evening','Evening','Evening','Evening','Night','Night')512,32,148,242,18,279,476,2242,488,78,416,538,0,MIDM2,2,17,203,701,0,MIDM52425,39321,65535Inhabitants#Number of inhabitants by district in Jan 1st, 2001.Table(Area1)(
389,10.25K,8215,882,6768,4157,11.62K,761,2407,3401,13.14K,14.57K,8705,6832,4746,10,3542,2284,15.89K,7028,11.8K,6825,3344,5755,10.28K,19K,9940,7288,12.96K,12.98K,10.36K,4523,8375,12.66K,5284,8470,13.65K,6422,8695,3549,8782,4169,11.44K,10.77K,2122,5480,7962,11.62K,10.91K,7636,5795,3710,16.15K,9493,8819,8331,11.23K,4023,8631,28.28K,5951,8259,16.46K,13.5K,12,829,9,3235,9228,6191,3145,7835,8819,16.41K,14.91K,6105,8003,15.76K,14.61K,2209,2888,12.29K,7692,3475,8069,2237,5239,8905,9199,8253,15.24K,5847,5934,1845,4671,549,3999,572,3579,9299,6466,18.7K,14.05K,2140,4118,2619,112,3145,3465,215,47,1807,10.4K,4301,11.36K,4840,2895,1346,3723,8338,2620,5403,3375,9873,12.48K,3167,4698,14.24K,9899,0)400,32,148,242,102,90,476,4922,0,0,184,753,0,MIDM2,489,294,416,303,0,MIDM65535,52427,65534Helsingin kaupungin tietokeskus: Helsingin seudun aluesarjat
www.aluesarjat.fiWorkplaces#The number of workplaces by districtTable(Area1)(
23.89K,28.84K,6227,11.46K,9798,6390,4771,3018,1284,6659,8195,8960,17.77K,4184,12.67K,4232,8797,5226,8561,11.63K,3571,17.04K,2849,3602,3469,9525,2861,2476,3305,5571,17.35K,5016,1728,4239,1053,3709,5964,1673,849,1308,1604,2162,1287,8431,2242,975,720,1853,1668,2334,538,699,1596,1333,7414,1828,1070,7452,1394,3051,893,849,1463,1481,443,1723,4068,9201,6916,2818,6321,3340,1389,2487,7270,1709,690,2794,2389,1237,3399,3463,3694,1581,7038,3254,519,832,1336,1927,2510,4198,4122,309,1681,79,2301,478,1629,3254,2826,7822,5587,2206,1529,504,3285,1814,4254,3928,9509,2633,7034,275,1063,1958,1856,2519,232,1023,346,1808,478,1358,1605,308,2012,3644,794,0)288,32,148,242,102,90,476,5481,248,258,713,303,0,MIDM2,583,35,416,303,1,MIDM65535,52427,65534SeutuCD 02, a CD ROM database about the Helsinki area.Municipality infoThe municipality to which each district belongs.Table(Area1)(
'Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa',0)184,32,148,242,102,90,476,2242,41,173,416,303,0,MIDM52425,39321,65535Trips place munictrips/dCar trips per day by municipality and place. Several weighting factors are used to derive the numbers from the original data.var ap:= array(Place,[Inhabitants, Workplaces, Workplaces, Inhabitants, Inhabitants]);
ap:= sum((if Municipality=Municipality_info then ap else 0),area1);
ap:= ap/sum(ap,Municipality);
var a:= ap*Car_trips;
ap:= ap[Municipality=Municipality1, Place=Place1];
a:= ap*a;
a:= a/sum(sum(a,Place),Place1);
a:= a*Trips_municipality;
a:= a/sum(sum(sum(sum(a, Municipality), Municipality1), Place), Place1);
a*sum(sum(Car_trips,Place),Place1)288,176,148,242,16,104,498,5911,339,342,644,303,0,MIDM2,15,44,784,245,0,MIDM[Place,Place1][Municipality1,Municipality][Index Suuralue]Trips by hourtrips/hTrips by hour from one district to another district.var ap:= array(Place,[Inhabitants, Workplaces, Workplaces, Inhabitants, Workplaces]);
ap:= ap/sum(ap,area1);
var a:= si_pi(Trips_place_munic,ap,Municipality,area1,Municipality_info);
a:= si_pi(a,ap[area1=area2],Municipality1,area2,Municipality_info[area1=area2]);
var va4:= Place_weight_by_hour*Time_in_traffic;
va4:= va4/sum(va4,hour);
a:= a*va4;
a:= a/sum(sum(a,Place),Place1) *sum(sum(sum(a,Place),Place1),hour) *Time_in_traffic/sum(Time_in_traffic,hour);
a:= sum(sum(a,Place),Place1);
array(mode1,[a,0,0])400,176,148,242,38,32,562,6882,554,42,540,493,0,MIDM[Area1,Area2][Variable Select_trip_matrix]HLT2005hpax29. Mayta 2006 15:06vkoe31. Mayta 2006 9:4748,2456,32,148,241,1,1,1,1,1,0,0,0,01,40,17,-441,361,17Arial, 12Municipality info HLTDefines the municipality each district belongs to.Table(Area1)(
91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,49,49,49,49,49,49,49,49,49,49,49,49,49,49,49,49,49,49,49,49,49,49,49,235,49,49,49,49,49,49,49,49,49,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,0)272,208,148,242,102,90,476,2242,473,196,416,303,0,MIDM52425,39321,65535Mista[49,91,92,235]56,176,148,122,40,50,416,303,0,MIDMMihincopyindex(Mista)56,200,148,122,459,182,476,2242,207,465,416,303,0,MIDM[49,91,92,235]Kulkutapa['henkiloauto','Joukkoliikenne','Muu']56,248,148,12HLT2004-05Table(Cols,Rows)(
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168,5169,5170,5171,5172,5173,5174,5175,5176,5177,5178,5179,5180,5181,5182,5183,5184,5185,5186,5187,5188,5189,5190,5191,5192,5193,5194,5195,5196,5197,5198,5199,5200,5201,5202,5203,5204,5205,5206,5207,5208,5209,5210,5211,5212,5213,5214,5215,5216,5217,5218,5219,5220,5221,5222,5223,5224,5225,5226,5227,5228,5229,5230,5231,5232,5233,5234,5235,5236,5237,5238,5239,5240,5241,5242,5243,5244,5245,5246,5247,5248,5249,5250,5251,5252,5253,5254,5255,5256,5257,5258,5259,5260,5261,5262,5263,5264,5265,5266,5267,5268,5269,5270,5271,5272,5273,5274,5275,5276,5277,5278,5279,5280,5281,5282,5283,5284,5285,5286,5287,5288,5289,5290,5291,5292,5293,5294,5295,5296,5297,5298,5299,5300,5301,5302,5303,5304,5305,5306,5307,5308,5309,5310,5311,5312,5313,5314,5315,5316,5317,5318,5319,5320,5321,5322,5323,5324,5325,5326,5327,5328,5329,5330,5331,5332,5333,5334,5335,5336,5337,5338,5339,5340,5341,5342,5343,5344,5345,5346,5347,5348,5349,5350,5351,5352,5353,5354,5355,5356,5357,5358,5359,5360,5361,5362,5363,5364,5365,5366,5367,5368,5369,5370,5371,5372,5373,5374,5375,5376,5377,5378,5379,5380,5381,5382,5383,5384,5385,5386,5387,5388,5389,5390,5391,5392,5393,5394,5395,5396,5397,5398,5399,5400,5401,5402,5403,5404,5405,5406,5407,5408,5409,5410,5411,5412,5413,5414,5415,5416,5417,5418,5419,5420,5421,5422,5423,5424,5425,5426,5427,5428,5429,5430,5431,5432,5433,5434,5435,5436,5437,5438,5439,5440,5441,5442,5443,5444,5445,5446,5447,5448,5449,5450,5451,5452,5453,5454,5455,5456,5457,5458,5459,5460,5461,5462,5463,5464,5465,5466,5467,5468,5469,5470,5471,5472,5473,5474,5475,5476,5477,5478,5479,5480,5481,5482,5483,5484,5485,5486,5487,5488,5489,5490,5491,5492,5493,5494,5495,5496,5497,5498,5499,5500,5501,5502,5503,5504,5505,5506,5507,5508,5509,5510,5511,5512,5513,5514,5515,5516,5517,5518,5519,5520,5521,5522,5523,5524,5525,5526,5527,5528,5529,5530,5531,5532,5533,5534,5535,5536,5537,5538,5539,5540,5541,5542,5543,5544,5545,5546,5547,5548,5549,5550,5551,5552,5553,5554,5555,5556,5557,5558,5559,5560,5561,5562,5563,5564,5565,5566,5567,5568,5569,5570,5571,5572,5573,5574,5575,5576,5577,5578,5579,5580,5581,5582,5583,5584,5585,5586,5587,5588,5589,5590,5591,5592,5593,5594,5595,5596,5597,5598,5599,5600,5601,5602,5603,5604,5605,5606,5607,5608,5609,5610,5611,5612,5613,5614,5615,5616,5617,5618,5619,5620,5621,5622,5623,5624,5625,5626,5627,5628,5629,5630,5631,5632,5633,5634,5635,5636,5637,5638,5639,5640,5641,5642,5643,5644,5645,5646,5647,5648,5649,5650,5651,5652,5653,5654,5655,5656,5657,5658,5659,5660,5661,5662,5663,5664,5665,5666,5667,5668,5669,5670,5671,5672,5673,5674,5675,5676,5677,5678,5679,5680,5681,5682,5683,5684,5685,5686,5687,5688,5689,5690,5691,5692,5693,5694,5695,5696,5697]Klo[0,0.2,0.4,0.6,0.8,1,1.2,1.4,1.6,1.8,2,2.2,2.4,2.6,2.8,3,3.2,3.4,3.6,3.8,4,4.2,4.4,4.6,4.8,5,5.2,5.4,5.6,5.8,6,6.2,6.4,6.6,6.8,7,7.2,7.4,7.6,7.8,8,8.2,8.4,8.6,8.8,9,9.2,9.4,9.6,9.8,10,10.2,10.4,10.6,10.8,11,11.2,11.4,11.6,11.8,12,12.2,12.4,12.6,12.8,13,13.2,13.4,13.6,13.8,14,14.2,14.4,14.6,14.8,15,15.2,15.4,15.6,15.8,16,16.2,16.4,16.6,16.8,17,17.2,17.4,17.6,17.8,18,18.2,18.4,18.6,18.8,19,19.2,19.4,19.6,19.8,20,20.2,20.4,20.6,20.8,21,21.2,21.4,21.6,21.8,22,22.2,22.4,22.6,22.8,23,23.2,23.4,23.6,23.8]56,224,148,122,40,50,416,303,0,MIDMHLT tripsmdtable(Hlt2004_05,rows,cols,[Klo,Mista,Mihin,Kulkutapa])56,144,148,242,30,460,512,3542,11,193,1143,303,0,MIDM[Kulkutapa,Mista]Trip activityvar a:= sum(sum(Hlt_trips,mista),mihin);
a:= if hour = round(klo-0.5) then a else 0;
a:= sum(a,klo);
if a= 0 then 1 else a168,208,148,242,142,53,872,562,0,MIDMGraphtool:0
Distresol:10
Diststeps:1
Cdfresol:5
Cdfsteps:1
Symbolsize:6
Baroverlap:0
Linestyle:10
Frame:1
Grid:1
Ticks:1
Mesh:1
Scales:1
Rotation:45
Tilt:0
Depth:70
Frameauto:3
Showkey:1
Xminimum:0
Xmaximum:24
Yminimum:0
Ymaximum:120
Zminimum:1
Zmaximum:3
Xintervals:0
Yintervals:0
Includexzero:0
Includeyzero:0
Includezzero:0
Statsselect:[1, 1, 1, 1, 1, 0, 0, 0]
Probindex:[5%, 25%, 50%, 75%, 95%]
Arial, 8[Hour,Kulkutapa]HLT trips by hourvar ap:= array(Place,[Inhabitants1, Workplaces1, Workplaces1, Inhabitants1, Workplaces1]);
ap:= ap/sum(ap,area1);{ap on painokerroin}
var a:= si_pi(sum(Hlt_trips,klo),ap,Mista,area1,Municipality_info_hl);
a:= si_pi(a,ap[area1=area2],Mihin,area2,Municipality_info_hl[area1=area2]);
a:= a/sum(sum(a,Place),Place1) *sum(sum(sum(a,Place),Place1),hour) *Trip_activity/sum(Trip_activity,hour);
a:= sum(sum(a,Place),Place1);
array(mode1,[a[kulkutapa='henkiloauto'],0,a[kulkutapa='Joukkoliikenne']])272,144,148,242,527,15,488,8322,45,68,434,442,0,MIDMGraphtool:0
Distresol:10
Diststeps:1
Cdfresol:5
Cdfsteps:1
Symbolsize:6
Baroverlap:0
Linestyle:10
Frame:1
Grid:1
Ticks:1
Mesh:1
Scales:1
Rotation:45
Tilt:0
Depth:70
Frameauto:1
Showkey:1
Xminimum:0
Xmaximum:25
Yminimum:0
Ymaximum:0.0225
Zminimum:1001
Zmaximum:1012
Xintervals:0
Yintervals:0
Includexzero:0
Includeyzero:0
Includezzero:0
Statsselect:[1, 1, 1, 1, 1, 0, 0, 0]
Probindex:[5%, 25%, 50%, 75%, 95%]
Arial, 4[Area2,Area1][Index Reg][Variable Select_trip_matrix]4.7.2006 ovh
Kopioitiin trips by hour solmun lähdekoodi tähän. Vaihdettiin tähän solmun seuraavat:
sipi-funktion parametrit:
trips_place_munic = tpm
ap = inhabitants
municapility = mista
area1 = are1
municipality_info = municipality_2006
municipality1 = mihin
place_weight_by_hour*time_in_traffic = va5
Neljä viimeistä riviä jätettiin koodista huomiotta! Toimii!?!
Inhabitants#Number of inhabitants by district in Jan 1st, 2006.Table(Area1)(
389,10.359K,9265,863,6684,4085,10.615K,737,2312,3318,12.916K,14.361K,8483,6736,4381,0,3957,2276,22.81K,6951,11.499K,7173,3489,7768,10.665K,19.295K,9961,7043,12.886K,12.538K,10.192K,4775,8294,12.488K,5325,8550,13.62K,6535,8690,3562,8422,7262,9898,11.8K,2578,5506,8034,11.33K,8478,9898,5500,3777,16.377K,9663,8305,8200,6705,8559,9109,28.318K,5905,7937,17.298K,15.658K,0,848,9,3496,8991,6035,3291,7704,8602,18.143K,15.035K,6043,8159,15.73K,15.057K,3270,2946,12.26K,9069,3512,6902,2673,5133,9313,9204,8457,17.947K,6353,6462,1966,5161,520,5365,606,3577,8545,6294,18.406K,13.634K,2115,4456,2548,105,4014,8113,183,5,1830,10.399K,4266,11.217K,4533,2784,1342,3947,7752,2717,5626,3546,9813,13.455K,3638,4734,13.821K,9499,0)272,72,148,242,0,0,184,753,0,MIDM2,489,294,416,303,0,MIDM65535,52427,65534Helsingin kaupungin tietokeskus: Helsingin seudun aluesarjat
www.aluesarjat.fiWorkplaces#The number of workplaces by districtTable(Area1)(
23.894K,28.844K,6227,11.46K,9798,6390,4771,3018,1284,6659,8195,8960,17.766K,4184,12.672K,4232,8797,5226,8561,11.629K,3571,17.037K,2849,3602,3469,9525,2861,2476,3305,5571,17.35K,5016,1728,4239,1053,3709,5964,1673,849,1308,1604,2162,1287,8431,2242,975,720,1853,1668,2334,538,699,1596,1333,7414,1828,1070,7452,1394,3051,893,849,1463,1481,443,1723,4068,9201,6916,2818,6321,3340,1389,2487,7270,1709,690,2794,2389,1237,3399,3463,3694,1581,7038,3254,519,832,1336,1927,2510,4198,4122,309,1681,79,2301,478,1629,3254,2826,7822,5587,2206,1529,504,3285,1814,4254,3928,9509,2633,7034,275,1063,1958,1856,2519,232,1023,346,1808,478,1358,1605,308,2012,3644,794,0)168,72,148,241,248,258,713,303,0,MIDM2,583,35,416,303,1,MIDM65535,52427,65534SeutuCD 02, a CD ROM database about the Helsinki area.Select trip matrixChoice(Self,1)56,96,148,24[Formnode Select_trip_matrix1]52425,39321,65535[Hlt_trips_by_hour,Trips_by_hour][Variable Trips_by_hour, Variable Hlt_trips_by_hour]Public matrixktluser4. elota 2006 0:0348,24288,96,148,241,40,0,678,544,17Bus matrixvar e:= slice(Active_routes,Active_routes.i,route_(Active_routes));
index j:= [1];
e:= if j=1 then e else '';
e:= if e=null then '' else e;
e:=Mirror(e,j)&',';
var a:= findintext(from&'',e);
var b:= findintext(to1&'',e);
b:= if a=b then b+1 else b;
e:= selecttext(e,a,b+3);
slice(e,e.a,1)&slice(e,e.a,2)392,240,148,242,102,90,476,4202,113,84,850,413,0,MIDM[To1,From]Bus linksvar a:= etapit(Active_routes);
a:= a&','&a[.etappi=a.etappi+1];
index row:= 1..size(sum(a,time_of_day1));
index col:= ['.Etappi','.I','Link'];
a:= mdarraytotable(a,row,col);
a:= a[col='Link'];
index b:= unique(a,a.row);
a:= a[.row=b];
if a=null then 'xxxx' else a392,304,148,242,102,90,476,2752,445,144,767,694,0,MIDMTime of day by timeif time<2 then 'Night1' else
if time<6 then 'Night2' else
if time<9 then 'Morning' else
if time<15 then 'Day' else
if time<18 then 'Afternoon' else
if time<21 then 'Evening1' else
if time<23 then 'Evening2' else 'Night1'168,216,148,242,0,-23,1025,6662,7,10,215,666,0,MIDM52425,39321,65535Timely routesvar a:= Choose_pub_matrix{scenario_input[input_var='Public matrix']};
a:= if a=1 then routes else
if a=2 then all_bus_routes[r_t='Route'] else
if a=3 then (if route_coverage[route_quality=all_bus_routes[r_t='Time']]=1 then all_bus_routes[r_t='Route'] else '')
else 0280,112,148,242,12,69,429,530,0,MIDMAll bus routesindex i:= 1..(size(bus_route_ends)+size(bus_routes_special))/2;
var a:= Bus_route_ends[r_t='Route'];
a:= Route_matrix[from=evaluate(selecttext(a,1,4)), to1=evaluate(selecttext(a,6,9))];
a:= if r_t='Route' then a else bus_route_ends;
a:= concat(a,Bus_routes_special{[r_t='Route']},bus_route_ends,bus_routes_special,i);
a:= if a=null then '' else a;
a[.i=sortindex(textlength(a[r_t='Route']),a.i)]168,112,148,242,25,100,1039,484,0,MIDM[R_t,I]Bus route endsThis is a list of bus route endstops.Table(Self,R_t)(
'1005,1025','A',
'1001,1036','A',
'1055,1056','B',
'1058,1064','B',
'1002,1026','B',
'1002,1033','B',
'1009,1001','C',
'1001,1040','C',
'1009,1010','C',
'1026,1037','C',
'1044,1049','C',
'1002,1024','C',
'1010,1022','C',
'1007,1029','C',
'1055,1053','C',
'1058,1059','C',
'1058,1060','C',
'1058,1062','C',
'1001,1039','C',
'1001,1044','D',
'1063,1064','D',
'1010,1025','D',
'1001,1039','D',
'1006,1027','D',
'1001,1120','B',
'1001,1090','B',
'1001,1092','C',
'1119,1124','C',
'1002,1126','C',
'1058,1083','C',
'1001,1123','C',
'1002,1074','C',
'1002,1099','D',
'1001,1125','D',
'1002,1068','D',
'1002,1077','D',
'1002,1079','D',
'1002,1073','D',
'1001,1097','D',
'1001,1099','D',
'1001,1102','D',
'1001,1103','D',
'1001,1111','D',
'1001,1124','D',
'1001,1127','D',
'1001,1106','D',
'1002,1075','D',
'1002,1078','D',
'1002,1106','D',
'1069,1079','C',
'1069,1075','C',
'1082,1085','C',
'1069,1075','C',
'1083,1088','C',
'1083,1073','C',
'1091,1099','C',
'1069,1078','D',
'1102,1106','B',
'1128,1127','B',
'1126,1124','B',
'1003,1025','E',
'1002,1035','E',
'1058,1031','E',
'1058,1013','E',
'1001,1047','E',
'1042,1070','E',
'1026,1067','E',
'1111,1092','E',
'1091,1094','E',
'1092,1094','E',
'1103,1105','E'
)['item 1','item 2','item 3','item 4','item 5','item 6','item 7','item 8','item 9','item 10','item 11','item 12','item 13','item 14','item 15','item 16','item 17','item 18','item 19','item 20','item 21','item 22','item 23','item 24','item 25','item 26','item 27','item 28','item 29','item 30','item 31','item 32','item 33','item 34','item 35','item 36','item 37','item 38','item 39','item 40','item 41','item 42','item 43','item 44','item 45','item 46','item 47','item 48','item 49','item 50','item 51','item 52','item 53','item 54','item 55','item 56','item 57','item 58','item 59','item 60','item 61','item 62','item 63','item 64','item 65','item 66','item 67','item 68','item 69','item 70','item 71']56,48,148,242,0,-23,1024,6652,0,0,257,665,0,MIDM65535,52427,65534[R_t,Self][R_t,Self]Bus routes specialThis is a list of whole bus routes, i.e. full chains of areas that are covered by the route. This includes also other forms of public transportation, such as trains and trams.Table(Self,R_t)(
'1026,1010,1002,1001,1005,1018,1019,1020,1023,1017,1025,1037,1036,1038,1039','A',
'1055,1052,1020,1023,1021,1017,1015','A',
'1058,1054,1056,1055,1052,1020,1023,1021,1017,1015,1012,1013,1027,1028','A',
'1018,1019,1021,1022,1017,1025,1036,1037,1030,1031,1032','B',
'1002,1011,1012,1019,1021,1023,1024,1042,1043,1044,1047,1048','B',
'1001,1005,1018,1019,1020,1023,1017,1025,1036,1037,1038,1039,1040,1048','B',
'1058,1054,1056,1059,1042,1024,1025,1016,1015,1014,1029,1028','B',
'1063,1064,1065','B',
'1008,1003,1002,1011,1012,1013','C',
'1020,1023,1019,1021,1017,1015,1029,1031','C',
'1004,1003,1002,1010,1026','C',
'1004,1001,1002,1011,1012,1013,1014,1029,1030,1034,1035','C',
'1018,1019,1020,1023,1024,1042,1043,1044,1047,1048,1046,1049','C',
'1018,1019,1020,1023,1024,1042,1043,1045,1044,1047,1046,1049,1050','C',
'1048,1049,1050','C',
'1001,1005,1018,1019,1020,1023,1024,1042,1051','C',
'1048,1047,1044,1045,1050,1051','C',
'1010,1002,1001,1018,1019,1020,1052,1055,1056,1058,1057,1063,1064','C',
'1010,1002,1001,1018,1019,1020,1052,1055,1056,1058,1059,1060,1062','C',
'1001,1011,1012,1015,1017,1023,1020,1018,1005','C',
'1001,1015,1017,1016,1029,1030,1034,1032,1102,1103','C',
'1003,1002,1011,1012,1013,1027,1028,1031','C',
'1008,1003,1004,1001,1005,1018,1020,1052,1055','C',
'1007,1003,1004,1002,1001,1005,1018,1019,1021,1017','C',
'1020,1021,1017,1015,1016,1036','C',
'1004,1001,1002,1011,1012,1013','C',
'1002,1011,1012,1013,1014,1029,1030,1034','C',
'1055,1056,1042,1043,1041,1044','C',
'1056,1054,1055','C',
'1058,1057,1061','C',
'1055,1054,1057,1058','C',
'1058,1060,1062','C',
'1058,1061,1060','C',
'1024,1025,1037,1036,1030,1029,1028,1027','C',
'1063,1062,1060,1045,1044','C',
'1003,1002,1001,1005,1018,1019,1021,1012,1011,1002,1001,1005,1004,1007','D',
'1001,1017,1015,1016,1029,1031,1081,1083,1086,1090,1091,1092,1095','D',
'1001,1017,1037,1041,1044,1046,1049,1117,1113,1116,1115,1120,1122,1121,1124','D',
'1005,1001,1002,1010,1011,1012,1013,1027,1028','D',
'1001,1005,1018,1019,1021,1017,1015,1014','D',
'1002,1011,1012,1013,1014,1029,1031,1032,1033','D',
'1001,1011,1012,1013,1014,1029,1030','D',
'1001,1011,1012,1013,1014,1029,1030,1034,1035','D',
'1001,1005,1018,1020,1023,1024,1042','D',
'1001,1005,1018,1019,1020,1023,1024,1042,1043,1041','D',
'1001,1005,1018,1019,1020,1023,1017,1025,1036,1037,1041,1047,1048,1046','D',
'1001,1005,1018,1019,1020,1023,1024,1042,1043,1045,1050,1049','D',
'1042,1024,1025,1017,1015,1012,1013,1027,1067,1068,1070','A',
'1001,1018,1019,1020,1023,1017,1025,1036,1037,1038,1040,1109,1112,1114,1115,1122,1120','B',
'1044,1041,1037,1036,1038,1035,1030,1034,1032,1082,1083','B',
'1002,1010,1067,1068,1070','C',
'1103,1102,1101,1100,1088,1089,1093,1092','C',
'1002,1011,1012,1013,1027,1083','C',
'1002,1010,1067,1073,1069,1071,1072','C',
'1001,1011,1012,1013,1014,1029,1030,1031,1032,1100','C',
'1103,1102,1101,1100,1082,1083,1084,1068,1069,1073','C',
'1058,1059,1042,1043,1037,1036,1030,1029,1031,1081,1083,1084,1068,1067,1069,1073','C',
'1058,1060,1059,1045,1043,1044,1047,1109,1110,1111','C',
'1111,1110,1109,1038,1034,1032,1083,1085,1092','C',
'1002,1010,1067,1069,1071,1070,1072,1085,1090,1089,1093','D',
'1002,1010,1067,1073,1074,1076,1079,1091','D',
'1001,1011,1012,1013,1014,1029,1031,1081,1083,1087,1089,1088','D',
'1002,1010,1067,1069,1070,1084,1083','D',
'1001,1011,1012,1013,1014,1029,1031,1081,1083,1087,1089,1093,1092,1091','D',
'1001,1011,1012,1013,1014,1029,1031,1081,1083,1082,1100','D',
'1002,1010,1026,1067,1069,1070,1072,1085,1086,1083','D',
'1002,1010,1067,1073,1074,1076,1078,1080,1095,1096','D',
'1001,1011,1012,1013,1014,1029,1031,1032,1082,1101,1104','D',
'1001,1018,1019,1020,1023,1017,1025,1036,1037,1038,1040,1039,1109,1112,1113,1115,1122','D',
'1001,1018,1019,1020,1023,1017,1025,1036,1037,1038,1040,1109,1108,1107,1105','D',
'1001,1018,1019,1020,1023,1024,1042,1117,1118,1116,1120,1119','D',
'1001,1011,1012,1013,1027,1068,1069,1071,1075,1079','D',
'1001,1011,1012,1013,1027,1068,1069,1070','D',
'1093,1089,1090,1085,1072,1075,1079,1076,1078','C',
'1083,1087,1089,1093','C',
'1075,1074,1079,1091,1092,1093','C',
'1092,1093,1097,1098','C',
'1083,1082,1087,1089,1088,1093,1097,1099','C',
'1100,1082,1083','C',
'1074,1075,1072,1085,1090,1089,1087,1083','C',
'1076,1077,1078,1079,1091,1092,1093','C',
'1068,1069,1073,1074,1075','C',
'1068,1069,1070,1072,1085,1090,1089,1088','C',
'1069,1070,1072,1085,1090,1091,1092,1095','C',
'1069,1071,1075,1079,1091,1092','C',
'1083,1087,1089,1093,1092,1091','C',
'1083,1087,1089,1090,1091','C',
'1083,1082,1087,1089,1088,1093,1097,1099','C',
'1083,1087,1089,1093,1094','D',
'1062,1129,1051,1128,1127,1117,1049,1112,1109,1110,1107,1103,1102','A',
'1127,1128,1117,1118,1116,1113','A',
'1113,1112,1110,1111,1107,1103,1104,1101,1100,1102','B',
'1104,1101,1103,1102','C',
'1113,1112,1110,1107,1106','C',
'1127,1128,1117,1113,1112,1110,1109','C',
'1062,1129,1128,1127,1118,1119,1120,1121,1122,1124,1125','C',
'1088,1100,1101,1102,1103,1105,1106','C',
'1102,1103,1107,1110,1109,1112,1049,1113,1115,1122','D',
'1113,1115,1122,1124','D',
'1113,1112,1109,1108,1103,1102,1100,1101','D',
'1113,1115,1114,1123','D',
'1113,1115,1116,1120,1121,1125','D',
'1113,1115,1122,1121,1119,1126,1125,1124','D',
'1100,1101,1102,1103,1108,1109,1110,1111','D',
'1062,1129,1051,1128,1127,1117,1118,1116,1113,1112,1110,1109,1111','D',
'1002,1011,1012,1013,1014,1029,1016,1030,1036,1034,1035','E',
'1058,1054,1056,1055,1052,1020,1023,1021,1017,1015,1012,1013,1027,1028','E',
'1001,1005,1018,1019,1020,1023,1024,1042,1043,1041,1047','E',
'1001,1011,1012,1013,1027,1084,1085,1086','E',
'1018,1019,1012,1013,1027,1067,1073,1074','E',
'1017,1015,1012,1013,1027,1067,1073,1074,1076,1077,1078','E',
'1020,1023,1017,1015,1012,1013,1027,1068,1069,1071,1075','E',
'1020,1023,1017,1015,1014,1029,1031,1081,1083,1087,1089,1088,1093','E',
'1018,1019,1020,1023,1017,1025,1037,1036,1038,1034,1035,1102,1103','E',
'1027,1028,1031,1032,1102,1103','E',
'1111,1110,1109,1040,1038,1034,1030,1031,1081,1083,1084,1068,1070,1069,1073','E',
'1068,1069,1073,1074,1076,1077','E',
'1083,1082,1087,1086,1085,1075,1074,1076,1078','E',
'1068,1069,1073,1074,1076,1078','E',
'1074,1075,1085,1086,1082,1083','E',
'1100,1082,1083,1084,1068,1069','E',
'1113,1116,1118,1127,1117','E',
'1001,1005,1018,1019,1020,1022,1023,1024,1042,1043,1044,1047,1046,1049','F',
'1026,1010,1003,1004,1001,1002,1011,1012,1013,1014,1029,1030,1034,1035','F',
'1001,1005,1018,1019,1020,1022,1023,1017,1025,1037,1040','F',
'1001,1005,1018,1019,1020,1022,1023,1024,1042,1043,1045,1044,1046,1050','F',
'1001,1005,1018,1020,1052,1055,1056','F',
'1001,1005,1018,1020,1052,1055,1053','F',
'1001,1005,1018,1020,1052,1055,1056,1054,1058,1057,1063,1064','F',
'1001,1005,1018,1020,1052,1055,1056,1054,1058,1059,1060','F',
'1001,1005,1018,1020,1052,1055,1054,1057,1063,1064','F',
'1001,1005,1018,1020,1052,1055,1056,1054,1058,1059,1060','F',
'1001,1005,1018,1020,1052,1055,1056,1054,1058,1061,1062,1129','F',
'1001,1011,1012,1013,1027,1028,1031,1032,1033,1034,103,1029,1014','G',
'1001,1005,1018,1019,1021,1017,1015,1014,1029,1030,1030,1035','G',
'1001,1005,1018,1020,1052,1055,1056,1054,1058,1059,1060,1062,1061,1057','G',
'1001,1005,1018,1020,1052,1055,1054,1058,1057,1064,1063,1062,1061,1056','G',
'1001,1005,1018,1019,1020,1023,1024,1042,1043,1041,1044,1046,1049,1050,1051,1045','G',
'1001,1005,1018,1019,1020,1023,1017,1025,1037,1041,1044,1047,1048,1039,1038,1036','G',
'1001,1005,1018,1020,1052,1055,1053,1056,1054,1058,1059','G',
'1026,1010,1002,1003,1004,1001,1005,1018,1019,1020,1023,1024,1042','G',
'1001,1005,1018,1019,1020,1023,1017,1025,1037,1036,1038,1039,1040','G',
'1002,1010,1067,1068,1069,1071,1073','G',
'1002,1010,1067,1069,1070,1072,1075,1074,1073','G',
'1002,1010,1067,1073,1074,1076,1079,1078,1080,1077','G',
'1001,1011,1012,1013,1027,1028,1084,1083,1087,1089,1093,1092,1091','G',
'1001,1011,1012,1013,1027,1084,1083,1082,1086,1085,1090,1089,1088,1100,1032,1031,1029,1014','G',
'1001,1011,1012,1013,1014,1029,1030,1034,1035,1102,1103,1108,1109,1039,1040,1038,1036,1037,1025,1016,1017,1015,1021','G',
'1001,1011,1012,1013,1027,1028,1084,1083,1082,1100,1101,1102,1035,1034,1030,1029,1014','G',
'1001,1018,1019,1020,1023,1017,1025,1036,1037,1038,1040,1109,1112,1114,1123,1115,1116,1113','G',
'1001,1018,1019,1020,1023,1024,1042,1043,1125,1124,1121,1122,1115,1113,1116,1118,1117,1128','G',
'1001,1018,1019,1020,1023,1024,1042,1128,1127,1120,1122,1124,1125','G'
)['item 1','item 2','item 3','item 4','item 5','item 6','item 7','item 8','item 9','item 10','item 11','item 12','item 13','item 14','item 15','item 16','item 17','item 18','item 19','item 20','item 21','item 22','item 23','item 24','item 25','item 26','item 27','item 28','item 29','item 30','item 31','item 32','item 33','item 34','item 35','item 36','item 37','item 38','item 39','item 40','item 41','item 42','item 43','item 44','item 45','item 46','item 47','item 48','item 49','item 50','item 51','item 52','item 53','item 54','item 55','item 56','item 57','item 58','item 59','item 60','item 61','item 62','item 63','item 64','item 65','item 66','item 67','item 68','item 69','item 70','item 71','item 72','item 73','item 74','item 75','item 76','item 77','item 78','item 79','item 80','item 81','item 82','item 83','item 84','item 85','item 86','item 87','item 88','item 89','item 90','item 91','item 92','item 93','item 94','item 95','item 96','item 97','item 98','item 99','item 100','item 101','item 102','item 103','item 104','item 105','item 106','item 107','item 108','item 109','item 110','item 111','item 112','item 113','item 114','item 115','item 116','item 117','item 118','item 119','item 120','item 121','item 122','item 123','item 124','item 125','item 126','item 127','item 128','item 129','item 130','item 131','item 132','item 133','item 134','item 135','item 136','item 137','item 138','item 139','item 140','item 141','item 142','item 143','item 144','item 145','item 146','item 147','item 148','item 149','item 150','item 151','item 152']56,112,148,242,0,-23,1024,6652,0,-23,1024,665,0,MIDM2,376,52,586,514,0,MIDM65535,52427,65534[R_t,Self][R_t,Self]R t['Route','Time']56,144,148,12['Route','Time']Time of day['Morning','Day','Afternoon','Evening1','Evening2','Night1','Night2']168,248,148,122,0,-23,1025,666['Morning','Day','Afternoon','Evening1','Evening2','Night1','Night2']Route quality['A','B','C','D','E','F','G']280,64,148,12['A','B','C','D','E','F','G']Route coverageTable(Route_quality,Time_of_day1)(
1,1,1,0,0,0,0,
1,1,1,1,0,0,0,
1,1,1,1,1,0,0,
1,1,1,1,1,1,0,
1,0,1,0,0,0,0,
0,0,0,0,0,1,0,
0,0,0,0,0,0,1
)280,32,148,242,0,-23,1024,6652,310,210,416,303,0,MIDM2,459,88,731,303,0,MIDM52425,39321,65535[Route_quality,Time_of_day1][Route_quality,Time_of_day1](routes)Etapitindex etappi:= 1..max(max((textlength(routes)+1)/5));
var c:= for z[]:= routes do slice(Splittext(z,','),Etappi);
c:= evaluate(c);504,120,152,12routes(trips,routes,c)Etappimatkatvar a:=0;
var x:=1;
while x<=size(from) do (
var y:=1;
while y<=size(from) do (
var b:= slice(slice(trips,from,x),to1,y);
if b>0 then (
var e:= slice(slice(routes,from,x),to1,y);
var d:= c[.i=e];
var s:= if d=slice(from,x) then d.etappi else 0;
var f:= if d=slice(to1,y) then d.etappi else 0;
s:= sum(s,s.etappi);
f:= sum(f,f.etappi);
s:= if d.etappi >=min([s,f]) and d.etappi<=max([s,f]) then 1 else 0;
a:= if c.i= e then s*b+a else a)
else 0;
y:= y+1);
x:= x+1);
a504,152,152,122,672,92,476,632trips,routes,c(a)Reittilyhennysvar c:= if a>= Scenario_input[Input_var='Public level'] then 1 else 0;
c:= cumulate(c,c.etappi)/sum(c,c.etappi);
var start:= sum((if c=0 then 1 else 0),c.etappi)*5+1;
start:= if isnan(start) then 2 else start;
var end:= (size(c.etappi)-sum((if c=1 then 1 else 0),c.etappi))*5+4;
end:= if isnan(end) then 1 else end;
selecttext(Timely_routes,start,end)504,88,152,122,515,101,476,224aActive routesindex i:= 1..size(sum(Active_routes_pre,time_of_day1));
slice(Active_routes_pre,Active_routes_pre.Active_routes_pre,i)280,240,148,242,102,90,476,3812,295,17,865,581,0,MIDM[I,Time_of_day1](routes)Route#var x:= 1;
var a:=0;
while x<=size(routes.i) do (
var b:= slice(routes,routes.i,x);
b:= if b=null then '' else b;
a:= if findintext(from&'',b)>0 and findintext(to1&'',b)>0 then x else a;
x:= x+1);
a504,24,152,122,488,77,492,373routesBus existencevar g:= Route_(Active_routes);
g:= sum((if time_of_day_by_time= time_of_day1 then g else 0),time_of_day1);
if g>0 then 1 else 0280,304,148,242,152,162,568,455,0,MIDM[To1,From](routes)Route#2var x:= 1;
var a:=0;
index mp:= ['Meno','Paluu'];
while x<=size(routes.i) do (
var b:= slice(routes,routes.i,x);
b:= if b=null then '' else b;
var m:= findintext(from&'',b);
var p:= findintext(to1&'',b);
a:= if m>0 and p>0 then
(if m<p then array(mp,[x,0]) else array(mp,[0,x]) )
else a;
x:= x+1);
a504,56,152,122,30,70,401,357routesActive routes prevar a:= etapit(Timely_routes);
var b:= Route_(Timely_routes);
var g:= Adjusted_trip_rate[mode1='Public'];
g:= sum((if time_of_day_by_time= time_of_day1 then g else 0),time);
g:= g/sum((if time_of_day_by_time= time_of_day1 then 1 else 0),time)/time_unit;
a:= Etappimatkat(g,b,a);
a:= Reittilyhennys(a);
b:= sum(if textlength(a)>0 then 1 else 0,time_of_day1);
a:= a[.i=subset(b)];280,176,148,242,189,49,957,581,0,MIDM[Self,Time_of_day1]['','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','',''](textlength(Bus_route_ends)+1)/572,272,148,24[R_t,Bus_route_ends]Choose pub matrixChoice(Self,3)392,112,148,24[Formnode Choose_pub_matrix1]52425,39321,65535[1,2,3]Bus tripstrips/time unitCalculates the trips for public transport. This is using route#2 so that trips going along the route and coming backwards are separated. This is necessary because the number of buses needed depends on the higher number.var b:= etapit(Active_routes);
var a:= Route_2(Active_routes);
var g:= all_trips[mode1='Public'];
g:= max((if time_of_day_by_time= time_of_day1 then g else 0),time);
Etappimatkat(g,a,b)400,96,148,242,102,90,476,3912,242,493,835,252,0,MIDM[I,Time_of_day1][Index I]Other partsContains functions, indexes, and nodes that are used in several modules, and log nodes.jtue2. Aprta 2004 14:1948,24248,368,148,241,0,0,1,1,1,0,,0,1,40,0,435,498,17HourHour of day.Sequence( 0, 23 )296,120,148,121,1,1,1,1,1,0,,0,1,104,114,416,303,0,MIDM[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23]Vehicle type['Bus','Minibus','Car (d)','Car (g)']176,272,148,122,102,90,476,224[0,0,1,0]['Bus','Minibus','Car (d)','Car (g)']ModeThe transport mode: either personal car or composite traffic.['Car','Composite','Public']176,304,148,121,0,0,1,1,1,0,,0,2,220,199,476,224['Car','Composite','Public']Trip lengthkmThe lengths of the trips shown as a frequency distribution.var comp:= aggr_period(All_trips);
comp:= comp[Mode1='Composite'];
var car:= comp[Mode1='Car'];
var a:= array(Vehicle,[comp,0,0,0,0,car]);
index length:= 1..50;
for x[]:= length do (
var e:= if round(distances)=x then a else 0;
e:= sum(sum(e,from),to1) )56,88,148,242,102,90,476,4072,55,202,932,513,1,MIDMZoneThe areas are classified into three categories: 1) downtown (downtown of Helsinki), 2) centre (other major centres within the Metropolitan area), and 3) suburb (all other areas).Table(Self)(
1,2,3)['Downtown','Centre','Suburb']56,16,148,122,10,257,476,4412,280,290,416,329,0,MIDM2,414,239,416,303,0,MIDM52425,39321,65535[0,0,0,1]['Downtown','Centre','Suburb']Traffic speedkm/hAverage speed of traffic.4056,312,148,242,93,231,476,32265535,52427,65534VehicleIndex of travel type (vehicle type including the number of changes).['d9','d8','d7','d6','d5','d4','d3','d2','d1','c9','c8','c7','c6','c5','c4','c3','c2','c1']176,216,148,121,1,1,1,1,1,0,,0,2,554,151,476,399['d9','d8','d7','d6','d5','d4','d3','d2','d1','c9','c8','c7','c6','c5','c4','c3','c2','c1']General functionsFunctions that are used in several modules of this model, or in several models. It is therefore practical to place them into one module.jtue2. Aprta 2004 14:4748,2456,200,148,241,305,164,240,340,21(in,out:prob;classes)VariationToistaiseksi Variation1 ei toimi, jos classes on indeksi. Tämän voi koettaa ratkaista siten, että tehdään isompi indeksi, jossa concatataan kaikki eripituiset varia-indeksit, sortataan suuruusjärjestykseen, ja slicataan pienemmät indeksit siihen. Tämän lisäksi täytyy linearinterp-funktiolla luoda puuttuviin kohtiin lukuja, jossa funktio kulkee nätisti. Nyt tätä ei ruveta tekemään.for x[]:= classes do (
index varia:= sequence(1/x,1,1/x);
var c:= rank(sample(in),run);
c:= ceil(c*x/samplesize)/x;
var a:= if c=Varia then sample(out) else 0;
var b:= if c=Varia then 1 else 0;
a:= sum(a,run)/sum(b,run);
if isnan(a) then 0 else a )168,208,148,242,57,16,476,648in,out,classes(out:prob;deci:indextype;input:prob;input_ind:indextype;luokkia)VOIVersio 1.index a:= ['Total VOI'];
index variable:= concat(a,input_ind);
for x[]:= luokkia do (
index varia:= sequence(1/x,1,1/x);
var in:= ceil(rank(input,run)*x/samplesize)/x;
var ncuu:= min(mean(sample(out)),deci);
var d:= (if a='Total VOI' then mean(min(sample(out),deci))-ncuu else 0);
var evpi:= if in=Varia then out else 0;
evpi:= sum(min(mean(evpi),deci),varia)-ncuu;
concat(d,evpi,a,input_ind,variable) )56,208,148,242,474,71,476,546out,deci,input,input_ind,luokkiaTime unithTime unit in hours. (Should equal the acceptable waiting time.)1/(size(time)/24)56,96,148,241,1,1,1,1,1,0,,0,52425,39321,65535(Trips,Delay)Time shifttime unitsshifts travels forward and backward in time. This is the way how travel times are taken into account.
Trips = number of trips traveled at each time point.
Delay = Travel time as number of time units. If delay is negative, the result is earlier in time than Trip.
Time_order = a helper variable containing the rank number of each time point.var time_order:= time/time_unit+1;
var a:= Time_order-Delay;
var b:= (if a >max(Time_order,time) or a< min(Time_order,time) then 1 else a);
slice(Trips,time,b)168,96,148,241,1,1,1,1,1,0,,0,2,367,75,476,512Trips,Delay(data)Clean rowsindex in1:= 1..size(data);
var b:= slice(data,in1);
var c:= unique(b,in1);
b:= slice(b,in1,c);
b:= slice(b,in1);
c:= subset(istext(b));
b:= slice(b,in1,c);
index a:= 1..size(b);
slice(b,a)168,32,148,242,2,165,476,389data(a)Aggr periodvar per:= if time>=6 and time<20 then ' 6.00-20.00' else
if time>=20 and time<24 then '20.00-24.00' else ' 0.00- 6.00';
var x:= 1;
var c:= 0;
while x<= size(time) do (
var b:= slice(a,time,x);
c:= if slice(per,time,x)=period then c+b else c;
x:= x+1);
c
{for x:= period do (
var b:= if per=x then a else 0;
sum(b,time))}
{var b:= if time>=6 and time<20 then a else 0;
b:= sum(b,time);
var c:= if time>=20 and time<24 then a else 0;
c:= sum(c,time);
var d:= if time>=24 or time<6 then a else 0;
d:= sum(d,time);
array(period,[b,c,d])}56,152,148,242,508,9,476,559a22.7.2006 Jouni Tuomisto
Yritin tämmöistä versiota, mutta se vei paljon enemmän muistia ja laskenta-aikaa jostain syystä.
index i:= 1..14/time_unit;
var b:= array(period,[
array(i,sequence(6,20,time_unit)),
array(i,sequence(20,34,time_unit)),
array(i,sequence(-8,6,time_unit))]);
b:= a[time=b];
b:= if b=null then 0 else b;
b:= sum(b,b.i)(in,out:prob;classes)VariationToistaiseksi Variation1 ei toimi, jos classes on indeksi. Tämän voi koettaa ratkaista siten, että tehdään isompi indeksi, jossa concatataan kaikki eripituiset varia-indeksit, sortataan suuruusjärjestykseen, ja slicataan pienemmät indeksit siihen. Tämän lisäksi täytyy linearinterp-funktiolla luoda puuttuviin kohtiin lukuja, jossa funktio kulkee nätisti. Nyt tätä ei ruveta tekemään.for x[]:= classes do (
index varia:= sequence(1/x,1,1/x);
var c:= rank(sample(in),run);
c:= ceil(c*x/samplesize)/x;
var a:= if c=Varia then sample(out) else 0;
var b:= if c=Varia then 1 else 0;
a:= sum(a,run)/sum(b,run);
if isnan(a) then 0 else a )168,152,148,24in,out,classes(param1,sigdigits)roundingvar a:= floor(logten(param1));
var b:= param1/10^(a+1-sigdigits);
round(b)*10^(a+1-sigdigits)272,32,148,24param1,sigdigitsProfilingUse this library to see which variables and functions are taking most of the computation time when running your model.
This library requires Analytica Enterprise, or ADE. It will not work for other versions of Analytica.
Here's how to use the library:
1. First run your model, i.e. show (and therefore compute) results for the outputs you are interested in timing.
2. Click Timing "Result" button to show an array showing how long it took to evaluate each variable (in CPU seconds), ordered to show the largest times first.
If you want to time additional calculations, added to existing timings.
3. Make those calculations by showing results for those variables.
4. Click button "Recompute Timings"
5. Click Timing "Result" button again.
If you want to time additional calculations, starting from zero again.
6. Change relevant inputs to cause their dependents to need to be recomputed.
7. Click "Reset Timings" to set to zero.
8. Show results for outputs of interest.
9. Click Timing "Result" again to see new timings.
Lonnie ChrismanSun, Jul 13, 2003 12:18 PMindirectSun, Sep 14, 2003 7:20 AM48,2456,144,148,241,1,1,1,1,1,0,0,0,01,40,151,-466,323,212,90,44,476,224(m: TextType)Descendant ObjectsReturns a list including module m and all its descendants, i.e. objects (variables, functions, and modules) contained in m - and in any modules it contains, recursively.VAR res := [m];
VAR c := contains OF m ;
IFONLY IsUndef(c) THEN res
ELSE BEGIN
FOR v := c Do BEGIN
VAR d := Descendant_objects(Identifier OF v);
res := Concat(res, d);
0
END;
res
END
80,176,152,242,97,125,476,394m1(m: TextType)Computation ProfilesecReturns an array of the computation time (in seconds) taken to evaluate each variable (or user-defined function). Results exclude time spent evaluating each variable's inputs. Times are sorted in descending order to show the variables taking the most time at the top. The result is indexed by .objects, a local index containing only those variables with a nonzero computation time.
This function is useful for profiling a computationally intensive model to find where the time is being spent. The time includes all time spent in computing each variable since the model was opened, or since the last call to "Reset Timings".
INDEX allobjs := Descendant_Objects(m);
VAR allTimings := (FOR obj:=allobjs DO EvaluationTime OF obj );
INDEX UnsortedNodes := Subset(allTimings > 0);
VAR timings := allTimings[allobjs = UnsortedNodes];
INDEX objects := sortIndex(-timings, UnsortedNodes);
timings[UnsortedNodes = objects]200,176,160,242,88,-2,481,571mTiming profileCPU SecReturns an array with the evaluation Time spent in each variable and function./* First, determine which node is the "root" node of the model */
VAR m := Identifier OF Isin OF Self ;
VAR top := WHILE (NOT IsUndef(Isin OF m ))
DO m := Identifier OF Isin OF m ;
Computation_profile(top)328,176,148,242,578,25,355,477,0,MIDM[Formnode Timing_profile1, Formnode Whole_model_computat]1,F,10,3,0,0[Variable Objects]Whole Model Computational Profile1256,40,1124,161,0,0,1,0,0,0,72,0,1Timing_profile(m: TextType)Computation Profile allsecReturns an array of the computation time (in seconds) taken to evaluate each variable (or user-defined function). Results exclude time spent evaluating each variable's inputs. Times are sorted in descending order to show the variables taking the most time at the top. The result is indexed by .objects, a local index containing only those variables with a nonzero computation time.
This function is useful for profiling a computationally intensive model to find where the time is being spent. The time includes all time spent in computing each variable since the model was opened, or since the last call to "Reset Timings".INDEX allobjs := Descendant_Objects(m);
VAR allTimings := (FOR obj:=allobjs DO EvaluationTimeAll OF obj );
INDEX UnsortedNodes := Subset(allTimings > 0);
VAR timings := allTimings[allobjs = UnsortedNodes];
INDEX objects := sortIndex(-timings, UnsortedNodes);
timings[UnsortedNodes = objects]200,240,160,242,102,90,529,521mTiming profile allCPU SecThis displays the Time spent in each variable and function/* First, determine which node is the "root" node of the model */
VAR m := Identifier OF Isin OF Self ;
VAR top := WHILE (NOT IsUndef(Isin OF m ))
DO m := Identifier OF Isin OF m ;
Computation_profile_(top)328,240,148,242,655,142,407,516,0,MIDM1,F,10,3,0,0FromArea number of the origin. Equals the Area 129 coding (plus 1000) by Helsinki Metropolitan Area Council.1001..1029176,80,148,122,518,124,476,424[Formnode From1][1001,1002,1003,1004,1005,1006,1007,1008,1009,1010,1011,1012,1013,1014,1015,1016,1017,1018,1019,1020,1021,1022,1023,1024,1025,1026,1027,1028,1029]ToArea number of the destination. Equals the Area 129 coding (plus 1000) by Helsinki Metropolitan Area Council.copyindex(From)176,104,148,12[1001,1002,1003,1004,1005,1006,1007,1008,1009,1010,1011,1012,1013,1014,1015,1016,1017,1018,1019,1020,1021,1022,1023,1024,1025,1026,1027,1028,1029]RegAn index for areal data tables. Transformed to 'From' index.1001..1129176,24,148,122,446,194,476,288[1001,1002,1003,1004,1005,1006,1007,1008,1009,1010,1011,1012,1013,1014,1015,1016,1017,1018,1019,1020,1021,1022,1023,1024,1025,1026,1027,1028,1029,1030,1031,1032,1033,1034,1035,1036,1037,1038,1039,1040,1041,1042,1043,1044,1045,1046,1047,1048,1049,1050,1051,1052,1053,1054,1055,1056,1057,1058,1059,1060,1061,1062,1063,1064,1065,1066,1067,1068,1069,1070,1071,1072,1073,1074,1075,1076,1077,1078,1079,1080,1081,1082,1083,1084,1085,1086,1087,1088,1089,1090,1091,1092,1093,1094,1095,1096,1097,1098,1099,1100,1101,1102,1103,1104,1105,1106,1107,1108,1109,1110,1111,1112,1113,1114,1115,1116,1117,1118,1119,1120,1121,1122,1123,1124,1125,1126,1127,1128,1129]Reg1An index for areal data tables. Transformed to 'To' index.1001..1129176,48,148,12[1001,1002,1003,1004,1005,1006,1007,1008,1009,1010,1011,1012,1013,1014,1015,1016,1017,1018,1019,1020,1021,1022,1023,1024,1025,1026,1027,1028,1029,1030,1031,1032,1033,1034,1035,1036,1037,1038,1039,1040,1041,1042,1043,1044,1045,1046,1047,1048,1049,1050,1051,1052,1053,1054,1055,1056,1057,1058,1059,1060,1061,1062,1063,1064,1065,1066,1067,1068,1069,1070,1071,1072,1073,1074,1075,1076,1077,1078,1079,1080,1081,1082,1083,1084,1085,1086,1087,1088,1089,1090,1091,1092,1093,1094,1095,1096,1097,1098,1099,1100,1101,1102,1103,1104,1105,1106,1107,1108,1109,1110,1111,1112,1113,1114,1115,1116,1117,1118,1119,1120,1121,1122,1123,1124,1125,1126,1127,1128,1129]Area1The number of area. Equals the Area 129 coding (plus 1000) by Helsinki Metropolitan Area Council.[1001,1002,1003,1004,1005,1006,1007,1008,1009,1010,1011,1012,1013,1014,1015,1016,1017,1018,1019,1020,1021,1022,1023,1024,1025,1026,1027,1028,1029,1030,1031,1032,1033,1034,1035,1036,1037,1038,1039,1040,1041,1042,1043,1044,1045,1046,1047,1048,1049,1050,1051,1052,1053,1054,1055,1056,1057,1058,1059,1060,1061,1062,1063,1064,1065,1066,1067,1068,1069,1070,1071,1072,1073,1074,1075,1076,1077,1078,1079,1080,1081,1082,1083,1084,1085,1086,1087,1088,1089,1090,1091,1092,1093,1094,1095,1096,1097,1098,1099,1100,1101,1102,1103,1104,1105,1106,1107,1108,1109,1110,1111,1112,1113,1114,1115,1116,1117,1118,1119,1120,1121,1122,1123,1124,1125,1126,1127,1128,1129,1130]176,136,148,122,531,226,476,224[1001,1002,1003,1004,1005,1006,1007,1008,1009,1010,1011,1012,1013,1014,1015,1016,1017,1018,1019,1020,1021,1022,1023,1024,1025,1026,1027,1028,1029,1030,1031,1032,1033,1034,1035,1036,1037,1038,1039,1040,1041,1042,1043,1044,1045,1046,1047,1048,1049,1050,1051,1052,1053,1054,1055,1056,1057,1058,1059,1060,1061,1062,1063,1064,1065,1066,1067,1068,1069,1070,1071,1072,1073,1074,1075,1076,1077,1078,1079,1080,1081,1082,1083,1084,1085,1086,1087,1088,1089,1090,1091,1092,1093,1094,1095,1096,1097,1098,1099,1100,1101,1102,1103,1104,1105,1106,1107,1108,1109,1110,1111,1112,1113,1114,1115,1116,1117,1118,1119,1120,1121,1122,1123,1124,1125,1126,1127,1128,1129,1130]RegionThe names of the larger regions used in the model.['+Länsi-Espoo','+Pohjois-Espoo','+Etelä-Espoo','+Keski-Espoo','+Länsi-Vantaa','+Keski-Vantaa','+Pohjois-Vantaa','+Itä-Vantaa','+Kanta-Helsinki','+Länsi-Helsinki','+Vanha-Helsinki','+Konalanseutu','+Pakilanseutu','+Malminseutu','+Itä-Helsinki']176,184,148,122,470,236,476,365['+Länsi-Espoo','+Pohjois-Espoo','+Etelä-Espoo','+Keski-Espoo','+Länsi-Vantaa','+Keski-Vantaa','+Pohjois-Vantaa','+Itä-Vantaa','+Kanta-Helsinki','+Länsi-Helsinki','+Vanha-Helsinki','+Konalanseutu','+Pakilanseutu','+Malminseutu','+Itä-Helsinki']Composite traffic dummyThe placeholder for the composite traffic. This is used when an argument is linked to composite traffic in general, and there is no obvious node to which it can be linked.056,256,148,24Vehicle_nochIndex of travel type (vehicle type including the number of changes). This index is the same as Vehicle except that there is an additional row, No-change trips. This is the number of trips that are forced not to be divided into two parts. Note that these trips are included in other rows, and therefore this index must not be summed up.['d9','d8','d7','d6','d5','d4','d3','d2','d1','c9','c8','c7','c6','c5','c4','c3','c2','c1','Noch']176,240,148,121,1,1,1,1,1,0,,0,2,10,126,476,4442,40,50,416,452,0,MIDM[Formnode Vehicle_noch1]Subsidise groups?0172,348,1156,121,0,0,1,0,0,0,72,0,1Subsidise_groups_Area2The number of area. Equals the Area 129 coding (plus 1000) by Helsinki Metropolitan Area Council.[1001,1002,1003,1004,1005,1006,1007,1008,1009,1010,1011,1012,1013,1014,1015,1016,1017,1018,1019,1020,1021,1022,1023,1024,1025,1026,1027,1028,1029,1030,1031,1032,1033,1034,1035,1036,1037,1038,1039,1040,1041,1042,1043,1044,1045,1046,1047,1048,1049,1050,1051,1052,1053,1054,1055,1056,1057,1058,1059,1060,1061,1062,1063,1064,1065,1066,1067,1068,1069,1070,1071,1072,1073,1074,1075,1076,1077,1078,1079,1080,1081,1082,1083,1084,1085,1086,1087,1088,1089,1090,1091,1092,1093,1094,1095,1096,1097,1098,1099,1100,1101,1102,1103,1104,1105,1106,1107,1108,1109,1110,1111,1112,1113,1114,1115,1116,1117,1118,1119,1120,1121,1122,1123,1124,1125,1126,1127,1128,1129,1130]176,160,148,12Link intensity per namevehicles/hThe number of vehicles per hour driving along a link for the 30 most busy links at 8.00-9.00 in the morning. The result is indexed by the names of the areas that are connected by the particular link.
Tämä ei enää toimi, mutta jos harrastusta riittää, voi koettaa rakentaa tämän uudestaan.{var d:= Basic_ranking1;
var mist:= floor(d/10000);
var mihi:= d-floor(d/10000)*10000;
var a:= Vehicles_per_link1;
a:= a[From=mist,To1=mihi];
d:= area_name[area1=floor(d/10000)]&' - '&area_name[area1=d-floor(d/10000)*10000];
index link:= d;
var c:=cumulate(1,link);
slice(a,a.top30,c)}
0296,32,148,242,46,12,824,709,0,MIDM(e;j:indextype)Mirrorindex a:= 1..size(j)*2;
index etappi:= 1..max(max(max(max((textlength(e)+1)/5))));
var c:= for y[]:= e do slice(Splittext(y,','),Etappi);
c:= if istext(c) then c else '';
var b:= c[Etappi=1];
var x:=2;
while x<= size(Etappi) do (
b:= (if c[Etappi=x] = '' then b else c[Etappi=x] & ',' & b);
x:= x+1;
b);
concat(e,b,j,j,a);56,48,148,122,405,179,476,347e,jRoad dataThis module creates the node Route matrix, which contains the driving instructions from all areas to all other areas. Distances calculates the distances (by road) between the areas.
To make the construction of Route matrix as simple as possible for a new city, the roads are defined in the following way. First, the whole metropolitan are is divided into 15 regions, and these regions are further divided into 129 areas with 7300 inhabitants on average. The 129 areas are standard areas for urban planning, but the regions were formed for this particular purpose. The criteria for forming a region were that they
1) are exclusive and mutually exhaustive
2) are as large as possible without creating very unrealistic routes between areas. Routes are defined in a way that between any two regions, there is only one specific road that is used to cross the region borders (and travel the distance between the regions if they are not neighbours).
It is thus necessary to describe the routes between all areas within each region, and the routes between all regions. However, then it is possible to deduce the detailed routes between two areas that are in different regions using these hierarchical instructions.
The routes are described as lists of areas that are along the road between the origin and destination. The route description needs not be in full detail if the details between two areas are defined in Roads node. A minimum number of existing roads were selected so that the routes in the model would not be very unrealistic. This work was done manually with a map. Note that the absolute numbers of 'Average vehicle flow on the 30 most busy roads' are likely biased upwards because all traffic from smaller streets is packed to the major roads in the model.jtue8. Aprta 2004 14:15jtue19. elota 2004 10:4348,24360,64,148,241,360,201,619,366,172,102,90,476,282Arial, 13Route matrixThe complete route instruction matrix including all relevant information.var a:= Prematrix;
index e:= 1..max(max((textlength(a)+1)/5,From),To1);
var g:= for x[]:= a do slice(splittext(x,','),e);
g:= if g=null then 'tyhjä' else g;
var y:= 1;
while y<=size(e)-1 do (
var x:= 1;
while x<= size(Road_mirror) do (
var h:= Road_mirror[.a=x];
var b:= g[.e=y];
var c:= g[.e=y+1];
var d:= findintext(b,h);
var f:= findintext(c,h);
a:= if d>0 and f>0 and f>d then Textreplace(a,b&','&c,selecttext(h,d,f+3),true) else a;
x:=x+1);
y:=y+1);
a288,200,148,242,478,35,476,4802,70,80,784,372,0,MIDM[To1,From]Area nameThe name of each area.Table(Area1)(
'Kluuvi','Kamppi','Punavuori','Kaartinkaupunki','Kruunuhaka','Katajanokka','Kaivopuisto','Munkkisaari','Ruoholahti','Salmisaari','Etu-Töölö','Taka-Töölö','Meilahti','Ruskeasuo','Länsi-Pasila','Pohjois-Pasila','Itä-Pasila','Hakaniemi','Kallio','Sörnäinen','Alppila','Vallila','Hermanni','Arabianranta','Käpylä','Lauttasaari','Munkkiniemi','Munkkivuori','Etelä-Haaga','Pohjois-Haaga','Pitäjänmäki','Konala','Malminkartano','Kannelmäki','Hakuninmaa','Maunula','Patola','Länsi-Pakila','Paloheinä','Itä-Pakila','Pukinmäki','Viikki','Pihlajamäki','Malmi','Malmin lentokenttä','Tapanila','Tapaninvainio','Siltamäki','Tapulikaupunki','Puistola','Jakomäki','Kulosaari','Laajasalo','Roihuvuori','Herttoniemenranta','Herttoniemi','Puotila','Puotinharju','Myllypuro','Kontula','Vartioharju','Mellunmäki','Vuosaari','Kallahti','Niinisaari','Suomenlinna','Keilaniemi','Otaniemi','Tapiola','Pohjois-Tapiola','Niittykumpu','Mankkaa','Westend','Matinkylä','Olari','Iivisniemi','Suvisaaristo','Espoonlahti','Nöykkiö','Saunalahti','Mäkkylä','Lintuvaara','Etelä-Leppävaara','Laajalahti','Sepänkylä','Kuninkainen','Karakallio','Laaksolahti','Viherlaakso','Kauniainen','Tuomarila','Muurala','Bemböle','Nuuksio','Kauklahti','Espoonkartano','Vanhakartano','Röylä','Kalajärvi','Hämeenkylä','Varisto','Myyrmäki','Martinlaakso','Petikko','Kivistö','Seutula','Viinikkala','Ylästö','Pakkala','Veromies','Helsinki airport','Koivuhaka','Tikkurila','Ruskeasanta','Simonkylä','Jokiniemi','Kuninkaala','Hakkila','Päiväkumpu','Havukoski','Rekola','Koivukylä','Ilola','Korso','Metsola','Jokivarsi','Sotunki','Hakunila','Länsimäki','Vaihtopiste')512,32,148,242,102,90,476,4522,510,11,258,615,0,MIDM65535,52427,65534Modified names from the Area 129 coding by Helsinki Metropolitan Area Council.A dummy index.[1,2,3,4,5,6,7,8,9,10,11,12,13,14]64,64,148,12[1,2,3,4,5,6,7,8,9,10,11,12,13,14]A dummy index.[1,2,3,4,5,6,7,8,9,10,11,12,13,14]64,88,148,12[1,2,3,4,5,6,7,8,9,10,11,12,13,14]RoadsA list of frequently used roads. The purpose of this node is to simplify definitions in nodes Routes outside and routes inside.Table(Self)(
'1078,1076,1074,1073,1067,1010,1002,1001','1093,1085,1084,1028','1104,1032,1029,1028,1027,1013,1011,1002','1105,1103,1102,1035,1034,1030,1014,1012,1001','1123,1112,1109,1040,1025,1022,1020,1001,1002,1010','1125,1127,1128,1045,1042,1024,1025,1016,1014,1029','1062,1061,1058,1054,1055,1052,1020,1018,1001','1095,1093,1097,1104,1103,1107,1110,1109,1117,1128,1129','1067,1068,1084,1083,1032,1034,1038,1040,1041,1043,1045,1060,1058','1080,1078,1076,1074,1073,1067,1068','1096,1095,1093,1094','1090,1085,1084,1083,1082','1088,1087,1083,1084','1042,1041,1047,1048','1042,1043,1044,1046,1049','1052,1055,1054,1058,1057,1063,1065','1059,1060,1062,1065','1026,1010,1002,1001,1005,1006','1008,1003,1004,1001','1008,1003,1004,1005,1006','1032,1029,1014,1016,1025,1024')[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21]288,32,148,242,166,156,470,457,0,MIDM2,104,114,802,486,0,MIDM65535,52427,65534Data based on a map of Helsinki Metropolitan area (YTV liikenne: Pääkaupunkiseudun joukkoliikennekartta 11.8.2002).Routes outsideRoutes are defined in a way that between any two regions, there is only one route that is used. This route is described in this node. The route description needs not be in full detail, e.g. if a route between two areas is defined in Roads node, it is enough to define the start and end areas here.Table(In3,In4)(
'+Länsi-Espoo,1093,1097,+Pohjois-Espoo',0,0,0,0,0,0,0,0,0,0,0,0,0,
'+Länsi-Espoo,1093,1085,1074,+Etelä-Espoo','+Pohjois-Espoo,1097,1093,1085,1074,+Etelä-Espoo',0,0,0,0,0,0,0,0,0,0,0,0,
'+Länsi-Espoo,1093,1085,+Keski-Espoo','+Pohjois-Espoo,1097,1093,1085,+Keski-Espoo','+Etelä-Espoo,1068,1084,+Keski-Espoo',0,0,0,0,0,0,0,0,0,0,0,
'+Länsi-Espoo,1093,1104,+Länsi-Vantaa','+Pohjois-Espoo,1097,1104,+Länsi-Vantaa','+Etelä-Espoo,1068,1032,1104,+Länsi-Vantaa','+Keski-Espoo,1084,1032,1104,+Länsi-Vantaa',0,0,0,0,0,0,0,0,0,0,
'+Länsi-Espoo,1093,1110,+Keski-Vantaa','+Pohjois-Espoo,1097,1104,1110,+Keski-Vantaa','+Etelä-Espoo,1068,1040,1109,+Keski-Vantaa','+Keski-Espoo,1084,1040,1109,+Keski-Vantaa','+Länsi-Vantaa,1107,1110,+Keski-Vantaa',0,0,0,0,0,0,0,0,0,
'+Länsi-Espoo,1093,1128,1127,+Pohjois-Vantaa','+Pohjois-Espoo,1097,1128,1127,+Pohjois-Vantaa','+Etelä-Espoo,1068,1045,1127,+Pohjois-Vantaa','+Keski-Espoo,1084,1045,1127,+Pohjois-Vantaa','+Länsi-Vantaa,1107,1128,1127,+Pohjois-Vantaa','+Keski-Vantaa,1109,1128,1127,+Pohjois-Vantaa',0,0,0,0,0,0,0,0,
'+Länsi-Espoo,1093,1128,+Itä-Vantaa','+Pohjois-Espoo,1097,1128,+Itä-Vantaa','+Etelä-Espoo,1068,1045,1128,+Itä-Vantaa','+Keski-Espoo,1084,1045,1128,+Itä-Vantaa','+Länsi-Vantaa,1107,1128,+Itä-Vantaa','+Keski-Vantaa,1109,1117,1128,+Itä-Vantaa','+Pohjois-Vantaa,1127,1128,+Itä-Vantaa',0,0,0,0,0,0,0,
'+Länsi-Espoo,1093,1028,1011,+Kanta-Helsinki','+Pohjois-Espoo,1097,1104,1011,+Kanta-Helsinki','+Etelä-Espoo,1067,1010,+Kanta-Helsinki','+Keski-Espoo,1084,1028,1011,+Kanta-Helsinki','+Länsi-Vantaa,1102,1001,+Kanta-Helsinki','+Keski-Vantaa,1109,1001,+Kanta-Helsinki','+Pohjois-Vantaa,1127,1025,1001,+Kanta-Helsinki','+Itä-Vantaa,1128,1025,1001,+Kanta-Helsinki',0,0,0,0,0,0,
'+Länsi-Espoo,1093,1028,+Länsi-Helsinki','+Pohjois-Espoo,1097,1104,1029,+Länsi-Helsinki','+Etelä-Espoo,1068,1084,1028,+Länsi-Helsinki','+Keski-Espoo,1084,1028,+Länsi-Helsinki','+Länsi-Vantaa,1102,1014,+Länsi-Helsinki','+Keski-Vantaa,1109,1025,1016,+Länsi-Helsinki','+Pohjois-Vantaa,1127,1016,+Länsi-Helsinki','+Itä-Vantaa,1128,1016,+Länsi-Helsinki','+Kanta-Helsinki,1001,1012,+Länsi-Helsinki',0,0,0,0,0,
'+Länsi-Espoo,1093,1028,1029,1025,+Vanha-Helsinki','+Pohjois-Espoo,1097,1104,1029,1025,+Vanha-Helsinki','+Etelä-Espoo,1068,1032,1025,+Vanha-Helsinki','+Keski-Espoo,1084,1028,1029,1025,+Vanha-Helsinki','+Länsi-Vantaa,1102,1014,1025,+Vanha-Helsinki','+Keski-Vantaa,1109,1025,+Vanha-Helsinki','+Pohjois-Vantaa,1127,1024,+Vanha-Helsinki','+Itä-Vantaa,1128,1024,+Vanha-Helsinki','+Kanta-Helsinki,1001,1018,+Vanha-Helsinki','+Länsi-Helsinki,1016,1025,+Vanha-Helsinki',0,0,0,0,
'+Länsi-Espoo,1093,1084,1032,+Konalanseutu','+Pohjois-Espoo,1097,1104,1032,+Konalanseutu','+Etelä-Espoo,1068,1032,+Konalanseutu','+Keski-Espoo,1084,1032,+Konalanseutu','+Länsi-Vantaa,1102,1035,+Konalanseutu','+Keski-Vantaa,1109,1040,1034,+Konalanseutu','+Pohjois-Vantaa,1127,1045,1034,+Konalanseutu','+Itä-Vantaa,1128,1045,1034,+Konalanseutu','+Kanta-Helsinki,1001,1030,+Konalanseutu','+Länsi-Helsinki,1014,1030,+Konalanseutu','+Vanha-Helsinki,1025,1014,1030,+Konalanseutu',0,0,0,
'+Länsi-Espoo,1093,1084,1032,1038,+Pakilanseutu','+Pohjois-Espoo,1097,1104,1032,1038,+Pakilanseutu','+Etelä-Espoo,1068,1038,+Pakilanseutu','+Keski-Espoo,1084,1038,+Pakilanseutu','+Länsi-Vantaa,1102,1034,1038,+Pakilanseutu','+Keski-Vantaa,1109,1040,+Pakilanseutu','+Pohjois-Vantaa,1127,1045,1040,+Pakilanseutu','+Itä-Vantaa,1128,1045,1040,+Pakilanseutu','+Kanta-Helsinki,1001,1020,1040,+Pakilanseutu','+Länsi-Helsinki,1014,1030,1034,1038,+Pakilanseutu','+Vanha-Helsinki,1025,1040,+Pakilanseutu','+Konalanseutu,1034,1038,+Pakilanseutu',0,0,
'+Länsi-Espoo,1093,1084,1041,+Malminseutu','+Pohjois-Espoo,1097,1104,1032,1041,+Malminseutu','+Etelä-Espoo,1068,1041,+Malminseutu','+Keski-Espoo,1084,1041,+Malminseutu','+Länsi-Vantaa,1102,1034,1041,+Malminseutu','+Keski-Vantaa,1109,1040,1041,+Malminseutu','+Pohjois-Vantaa,1127,1045,+Malminseutu','+Itä-Vantaa,1128,1045,+Malminseutu','+Kanta-Helsinki,1001,1020,1040,1041,+Malminseutu','+Länsi-Helsinki,1014,1030,1034,1041,+Malminseutu','+Vanha-Helsinki,1025,1040,1041,+Malminseutu','+Konalanseutu,1034,1041,+Malminseutu','+Pakilanseutu,1040,1041,+Malminseutu',0,
'+Länsi-Espoo,1093,1084,1045,1060,+Itä-Helsinki','+Pohjois-Espoo,1097,1104,1032,1045,1060,+Itä-Helsinki','+Etelä-Espoo,1068,1045,1060,+Itä-Helsinki','+Keski-Espoo,1084,1045,1060,+Itä-Helsinki','+Länsi-Vantaa,1102,1034,1045,1060,+Itä-Helsinki','+Keski-Vantaa,1109,1040,1045,1060,+Itä-Helsinki','+Pohjois-Vantaa,1127,1045,1060,+Itä-Helsinki','+Itä-Vantaa,1128,1045,1060,+Itä-Helsinki','+Kanta-Helsinki,1001,1020,1052,+Itä-Helsinki','+Länsi-Helsinki,1014,1030,1034,1045,1060,+Itä-Helsinki','+Vanha-Helsinki,1020,1052,+Itä-Helsinki','+Konalanseutu,1034,1045,1060,+Itä-Helsinki','+Pakilanseutu,1040,1045,1060,+Itä-Helsinki','+Malminseutu,1045,1060,+Itä-Helsinki'
)64,32,148,242,505,193,476,5082,70,2,872,335,0,MIDM2,198,39,805,439,0,MIDM65535,52427,65534[In3,In4][In3,In4]Data based on a map of Helsinki Metropolitan area (YTV liikenne: Pääkaupunkiseudun joukkoliikennekartta 11.8.2002).Route listChanges the Routes outside into a one-dimensional list.var c:= if Routes_outside=0 then 0 else 1;
c:= sum(sum(c,in3),in4);
Index a:= 1..c;
Index b:= ['In3','In4','Data'];
var d:= Mdarraytotable(Routes_outside,a,b);
d:= d[.b='Data'];
d64,136,148,242,102,90,476,2972,214,56,537,610,0,MIDMRegion explode'Explodes' the regional route lists in a way that any driving instruction that applies to a region, applies also to all areas within the region.var b:= route_list;
var x:= 1;
while x<= size(Region) do ( {Käy läpi jokainen alue}
var c:= slice(Region,x);
var d:= slice(Regions,x);
var h:= b;
var j:= size(b);
d:= splittext(d,',');
var y:= 1;
while y<= size(b) do ( {Käy läpi jokainen ajo-ohje}
var f:= slice(h,y);
f:= if Istext(f) then f else '';
(if findintext(c,f)>0 then (
f:= textreplace(f,c,d,true); {Korvaa ryhmäaluenimet aluenimillä}
b:= concat(b,f)) else 0);
y:= y+1);
x:= x+1);
{Tästä alkaa vanha Aluerajaytys_b}
index In3:= 1..size(b);
b:= slice(b, In3);
b:= (if findintext('+',b)>0 then null else b); { Hävitetään aluenimet
var c:= unique(b,in3); Romauta kaikki toistuvat rivit
b:= slice(b,in3,c);
b:= slice(b,in3);}
var c:= subset(istext(b)); {Romauta kaikki tyhjät rivit}
b:= slice(b,in3,c);
index in5:= 1..size(b);
b:= slice(b,in5);
{Poista reitistä samat toistuvat pisteet
x:=1;
while x<=size(mista) do (
c:= slice(mista,x)&'';
b:= textreplace(b,c&','&c,c,true);
x:=x+1 );
b}64,200,148,242,102,90,476,5902,10,11,474,620,0,MIDMRegionsDescribes the small areas that belong into each larger region. The region names must start with '+'. All areas must be mentioned exactly once. Regions were selected in a way that they are as large as possible without creating very unrealistic routes between areas.Table(Region)(
'1091,1092,1093,1094,1095,1096','1097,1098,1099','1067,1068,1069,1070,1071,1073,1074,1075,1076,1077,1078,1079,1080','1072,1081,1082,1083,1084,1085,1086,1087,1088,1089,1090','1100,1101,1102,1103,1104,1105,1106,1107,1108','1109,1110,1111,1112,1113,1114,1115,1116,1123','1118,1119,1120,1121,1122,1124,1125,1126,1127','1117,1128,1129','1001,1002,1003,1004,1005,1006,1007,1008,1009,1010,1011,1026,1066','1012,1013,1014,1015,1016,1027,1028,1029','1017,1018,1019,1020,1021,1022,1023,1024,1025','1030,1031,1032,1033,1034,1035','1036,1037,1038,1039,1040','1041,1042,1043,1044,1045,1046,1047,1048,1049,1050,1051','1052,1053,1054,1055,1056,1057,1058,1059,1060,1061,1062,1063,1064,1065')64,264,148,242,88,98,771,523,0,MIDM2,20,224,651,394,0,MIDM52425,39321,65535Data based on a map of Helsinki Metropolitan area (YTV liikenne: Pääkaupunkiseudun joukkoliikennekartta 11.8.2002).PrematrixThe crude route instruction matrix without the information from Routes inside and Roads nodes.var mirror22:= Mirror(region_explode,region_explode.in5);
var a:= mirror22;
a:= evaluate(selecttext(a,1,4))*10000+evaluate(selecttext(a,textlength(a)-3));
index b:=a;
index c:= [1];
index d:= 2..size(Mirror22.a);
var e:= concat(c,d,c,d,b);
e:= slice(Mirror22,Mirror22.a,e);
e:= e[.b=From*10000+To1];
if e=null then From&','&To1 else e176,200,148,242,102,90,476,5862,408,52,759,604,0,MIDM[To1,From]1,I,4,2,0,0Road mirror'Mirrors' the driving instructions in a way that if an instruction applies to 'from A to B', its reverse applies to 'from B to A'.index roa:= 1..size(route_list1)+size(roads);
var a:= concat(roads,route_list1,roads,route_list1.a,roa);
a:= Mirror(a,roa);
a:= clean_rows(a);
var c:= for y[]:= a do (
var e:= (if findintext(y,a)>0 then 1 else 0);
e:= sum(e,e.a)-1 );
a:= if c>0 then 0 else a;
clean_rows(a)288,136,148,242,577,84,476,4092,219,-3,563,627,0,MIDMRoutes insideDefines the routes between every two areas within a region. The route description needs not be in full detail, e.g. if a route between two areas is defined in Roads node, it is enough to define the start and end areas here. A minimum number of existing roads were selected so that the routes in the model would not be very unrealistic. This work was done manually with a map.Table(In3,In4,region)(
'1091,1092','1097,1098','1067,1068','1072,1085,1084,1083,1081','1100,1101','1109,1110','1118,1127,1119','1117,1128','1001,1002','1012,1013','1017,1019,1018','1030,1034,1031','1036,1037','1041,1042','1052,1055,1053',
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
'1091,1092,1093','1097,1099','1067,1069','1072,1085,1084,1083,1082','1100,1101,1102','1109,1110,1111','1118,1120','1117,1128,1129','1001,1004,1003','1012,1014','1017,1019','1030,1034,1032','1036,1038','1041,1043','1052,1055,1054',
'1092,1093','1098,1097,1099','1068,1069','1081,1082','1101,1102','1110,1111','1119,1127,1120','1128,1129','1002,1004,1003','1013,1014','1018,1019','1031,1032','1037,1036,1038','1042,1043','1053,1055,1054',
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
'1091,1092,1093,1094',0,'1067,1073,1070','1072,1085,1084,1083','1100,1104,1103','1109,1112','1118,1120,1121','1117,1130','1001,1004','1012,1015','1017,1020','1030,1034,1032,1033','1036,1038,1039','1041,1044','1052,1055',
'1092,1093,1094',0,'1068,1070','1081,1083','1101,1103','1110,1109,1112','1119,1121','1128,1130','1002,1004','1013,1015','1018,1020','1031,1032,1033','1037,1040,1039','1042,1043,1044','1053,1055',
'1093,1094',0,'1069,1070','1082,1083','1102,1103','1111,1112','1120,1121','1129,1130','1003,1004','1014,1016,1015','1019,1020','1032,1033','1038,1039','1043,1044','1054,1055',
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
'1091,1092,1095',0,'1067,1073,1071','1072,1085,1084','1100,1104','1109,1113','1118,1120,1122',0,'1001,1005','1012,1015,1016','1017,1021','1030,1034','1036,1038,1040','1041,1043,1045','1052,1055,1056',
'1092,1095',0,'1068,1070,1071','1081,1083,1084','1101,1104','1110,1109,1113','1119,1121,1122',0,'1002,1001,1005','1013,1015,1016','1018,1019,1021','1031,1034','1037,1040','1042,1045','1053,1055,1056',
'1093,1095',0,'1069,1071','1082,1083,1084','1102,1101,1104','1111,1110,1109,1113','1120,1122',0,'1003,1004,1005','1014,1016','1019,1021','1032,1034','1038,1040','1043,1045','1054,1056',
'1094,1093,1095',0,'1070,1071','1083,1084','1103,1104','1112,1113','1121,1122',0,'1004,1005','1015,1016','1020,1021','1033,1032,1034','1039,1040','1044,1043,1045','1055,1056',
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
'1091,1092,1095,1096',0,'1067,1073','1072,1085','1100,1104,1103,1105','1109,1112,1114','1118,1120,1121,1124',0,'1001,1005,1006','1012,1013,1027','1017,1022','1030,1034,1035',0,'1041,10441046','1052,1055,1054,1058,1057',
'1092,1095,1096',0,'1068,1067,1073','1081,1083,1084,1085','1101,1103,1105','1110,1109,1112,1114','1119,1121,1124',0,'1002,1001,1005,1006','1013,1027','1018,1020,1022','1031,1034,1035',0,'1042,1043,10441046','1053,1055,1054,1058,1057',
'1093,1095,1096',0,'1069,1073','1082,1083,1084,1085','1102,1103,1105','1111,1110,1109,1112,1114','1120,1121,1124',0,'1003,1004,1005,1006','1014,1013,1027','1019,1022','1032,1035',0,'1043,1044,1046','1054,1058,1057',
'1094,1093,1095,1096',0,'1070,1073','1083,1084,1085','1103,1105','1112,1114','1121,1124',0,'1004,1006','1015,1027','1020,1022','1033,1032,1035',0,'1044,1046','1055,1054,1058,1057',
'1095,1096',0,'1071,1073','1084,1085','1104,1103,1105','1113,1114','1122,1121,1124',0,'1005,1006','1016,1015,1013,1027','1021,1022','1034,1035',0,'1045,1044,1046','1056,1054,1058,1057',
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,'1067,1073,1074','1072,1085,1086','1100,1104,1103,1106','1109,1113,1115','1118,1127,1125',0,'1001,1004,1007','1012,1013,1027,1028','1017,1022,1023','1030,1014',0,'1041,1047','1052,1055,1054,1058',
0,0,'1068,1067,1073,1074','1081,1083,1086','1101,1103,1106','1110,1109,1113,1115','1119,1126,1125',0,'1002,1004,1007','1013,1027,1028','1018,1020,1023','1031,1029,1014',0,'1042,1041,1047','1053,1055,1054,1058',
0,0,'1069,1073,1074','1082,1086','1102,1103,1106','1111,1110,1109,1113,1115','1120,1121,1125',0,'1003,1007','1014,1029,1028','1019,1022,1023','1032,1029,1014',0,'1043,1041,1047','1054,1058',
0,0,'1070,1073,1074','1083,1086','1103,1106','1112,1115','1121,1125',0,'1004,1007','1015,1013,1027,1028','1020,1023','1033,1032,1029,1014',0,'1044,1047','1055,1054,1058',
0,0,'1071,1074','1084,1086','1104,1103,1106','1113,1115','1122,1121,1125',0,'1005,1007','1016,1014,1029,1028','1021,1022,1023','1034,1030,1014',0,'1045,1044,1047','1056,1054,1058',
0,0,'1073,1074','1085,1086','1105,1106','1114,1115','1124,1125',0,'1006,1005,1007','1027,1028','1022,1023','1035,1034,1030,1014',0,'1046,1047','1057,1058',
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,'1067,1073,1074,1075','1072,1085,1086,1087','1100,1104,1103,1107','1109,1113,1116','1118,1127,1125,1126',0,'1001,1004,1003,1008','1012,1014,1029','1017,1025,1024',0,0,'1041,1047,1048','1052,1055,1054,1059',
0,0,'1068,1067,1073,1074,1075','1081,1083,1087','1101,1103,1107','1110,1109,1113,1116','1119,1126',0,'1002,1004,1003,1008','1013,1027,1028,1029','1018,1020,1023,1024',0,0,'1042,1041,1047,1048','1053,1055,1054,1059',
0,0,'1069,1073,1074,1075','1082,1087','1102,1107','1111,1110,1109,1113,1116','1120,1121,1125,1126',0,'1003,1008','1014,1029','1019,1022,1023,1024',0,0,'1043,1041,1047,1048','1054,1059',
0,0,'1070,1073,1074,1075','1083,1087','1103,1107','1112,1115,1116','1121,1125,1126',0,'1004,1008','1015,1016,1014,1029','1020,1023,1024',0,0,'1044,1047,1048','1055,1054,1059',
0,0,'1071,1074,1075','1084,1086,1087','1104,1103,1107','1113,1116','1122,1121,1125,1126',0,'1005,1008','1016,1014,1029','1021,1022,1023,1024',0,0,'1045,1044,1047,1048','1056,1059',
0,0,'1073,1074,1075','1085,1086,1087','1105,1103,1107','1114,1115,1116','1124,1125,1126',0,'1006,1008','1027,1028,1029','1022,1023,1024',0,0,'1046,1048','1057,1060,1059',
0,0,'1074,1075','1086,1087','1106,1103,1107','1115,1116','1125,1126',0,'1007,1003,1008','1028,1029','1023,1024',0,0,'1047,1048','1058,1059',
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,'1067,1073,1074,1076','1072,1085,1090,1089,1088','1100,1104,1103,1108','1109,1112,1123','1118,1127',0,'1001,1002,1009',0,'1017,1025',0,0,'1041,1044,1046,1049','1052,1055,1054,1058,1060',
0,0,'1068,1067,1073,1074,1076','1081,1083,1087,1088','1101,1103,1108','1110,1109,1112,1123','1119,1127',0,'1002,1009',0,'1018,1020,1022,1025',0,0,'1042,1043,1044,1046,1049','1053,1055,1054,1058,1060',
0,0,'1069,1073,1074,1076','1082,1087,1088','1102,1108','1111,1110,1109,1112,1123','1120,1127',0,'1003,1009',0,'1019,1022,1025',0,0,'1043,1044,1046,1049','1054,1058,1060',
0,0,'1070,1073,1074,1076','1083,1087,1088','1103,1108','1112,1123','1121,1120,1127',0,'1004,1009',0,'1020,1022,1025',0,0,'1044,1046,1049','1055,1054,1058,1060',
0,0,'1071,1074,1076','1084,1083,1087,1088','1104,1103,1108','1113,1112,1123','1122,1120,1127',0,'1005,1004,1009',0,'1021,1025',0,0,'1045,1044,1046,1049','1056,1059,1060',
0,0,'1073,1074,1076','1085,1090,1089,1088','1105,1103,1108','1114,1123','1124,1125,1127',0,'1006,1005,1004,1009',0,'1022,1025',0,0,'1046,1049','1057,1060',
0,0,'1074,1076','1086,1087,1088','1106,1103,1108','1115,1114,1123','1125,1127',0,'1007,1003,1009',0,'1023,1022,1025',0,0,'1047,1046,1049','1058,1060',
0,0,'1075,1076','1087,1088','1107,1108','1116,1115,1114,1123','1126,1127',0,'1008,1003,1009',0,'1024,1025',0,0,'1048,1049','1059,1060',
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,'1067,1073,1074,1076,1077','1072,1085,1090,1089',0,0,0,0,'1001,1002,1010',0,0,0,0,'1041,1043,1050','1052,1055,1054,1058,1061',
0,0,'1068,1067,1073,1074,1076,1077','1081,1083,1087,1089',0,0,0,0,'1002,1010',0,0,0,0,'1042,1043,1050','1053,1055,1054,1058,1061',
0,0,'1069,1073,1074,1076,1077','1082,1087,1089',0,0,0,0,'1003,1010',0,0,0,0,'1043,1050','1054,1058,1061',
0,0,'1070,1073,1074,1076,1077','1083,1087,1089',0,0,0,0,'1004,1010',0,0,0,0,'1044,1046,1050','1055,1054,1058,1061',
0,0,'1071,1074,1076,1077','1084,1083,1087,1089',0,0,0,0,'1005,1001,1002,1010',0,0,0,0,'1045,1050','1056,1054,1058,1061',
0,0,'1073,1074,1076,1077','1085,1090,1089',0,0,0,0,'1006,1005,1001,1002,1010',0,0,0,0,'1046,1050','1057,1061',
0,0,'1074,1076,1077','1086,1087,1089',0,0,0,0,'1007,1003,1010',0,0,0,0,'1047,1046,1050','1058,1061',
0,0,'1075,1076,1077','1087,1089',0,0,0,0,'1008,1003,1010',0,0,0,0,'1048,1046,1050','1059,1060,1061',
0,0,'1076,1077','1088,1089',0,0,0,0,'1009,1010',0,0,0,0,'1049,1050','1060,1061',
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,'1067,1073,1074,1076,1078','1072,1085,1090',0,0,0,0,'1001,1002,1011',0,0,0,0,'1041,1043,1045,1051','1052,1055,1054,1058,1061,1062',
0,0,'1068,1067,1073,1074,1076,1078','1081,1083,1084,1085,1090',0,0,0,0,'1002,1011',0,0,0,0,'1042,1045,1051','1053,1055,1054,1058,1061,1062',
0,0,'1069,1073,1074,1076,1078','1082,1083,1084,1085,1090',0,0,0,0,'1003,1002,1011',0,0,0,0,'1043,1045,1051','1054,1058,1061,1062',
0,0,'1070,1073,1074,1076,1078','1083,1084,1085,1090',0,0,0,0,'1004,1002,1011',0,0,0,0,'1044,1043,1045,1051','1055,1054,1058,1061,1062',
0,0,'1071,1074,1076,1078','1084,1085,1090',0,0,0,0,'1005,1001,1002,1011',0,0,0,0,'1045,1051','1056,1054,1058,1061,1062',
0,0,'1073,1074,1076,1078','1085,1090',0,0,0,0,'1006,1005,1001,1002,1011',0,0,0,0,'1046,1050,1051','1057,1061,1062',
0,0,'1074,1076,1078','1086,1090',0,0,0,0,'1007,1003,1002,1011',0,0,0,0,'1047,1046,1050,1051','1058,1061,1062',
0,0,'1075,1076,1078','1087,1089,1090',0,0,0,0,'1008,1003,1002,1011',0,0,0,0,'1048,1046,1050,1051','1059,1060,1062',
0,0,'1076,1078','1088,1089,1090',0,0,0,0,'1009,1002,1011',0,0,0,0,'1049,1050,1051','1060,1062',
0,0,'1077,1078','1089,1090',0,0,0,0,'1010,1002,1011',0,0,0,0,'1050,1051','1061,1062',
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,'1067,1073,1074,1076,1079',0,0,0,0,0,'1001,1002,1010,1026',0,0,0,0,0,'1052,1055,1054,1058,1057,1063',
0,0,'1068,1067,1073,1074,1076,1079',0,0,0,0,0,'1002,1010,1026',0,0,0,0,0,'1053,1055,1054,1058,1057,1063',
0,0,'1069,1073,1074,1076,1079',0,0,0,0,0,'1003,1010,1026',0,0,0,0,0,'1054,1058,1057,1063',
0,0,'1070,1073,1074,1076,1079',0,0,0,0,0,'1004,1010,1026',0,0,0,0,0,'1055,1054,1058,1057,1063',
0,0,'1071,1074,1076,1079',0,0,0,0,0,'1005,1001,1002,1010,1026',0,0,0,0,0,'1056,1054,1058,1057,1063',
0,0,'1073,1074,1076,1079',0,0,0,0,0,'1006,1005,1001,1002,1010,1026',0,0,0,0,0,'1057,1063',
0,0,'1074,1076,1079',0,0,0,0,0,'1007,1003,1010,1026',0,0,0,0,0,'1058,1057,1063',
0,0,'1075,1076,1079',0,0,0,0,0,'1008,1003,1010,1026',0,0,0,0,0,'1059,1060,1057,1063',
0,0,'1076,1079',0,0,0,0,0,'1009,1010,1026',0,0,0,0,0,'1060,1057,1063',
0,0,'1077,1076,1079',0,0,0,0,0,'1010,1026',0,0,0,0,0,'1061,1057,1063',
0,0,'1078,1079',0,0,0,0,0,'1011,1002,1010,1026',0,0,0,0,0,'1062,1063',
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,'1067,1073,1074,1076,1078,1080',0,0,0,0,0,'1001,1005,1066',0,0,0,0,0,'1052,1055,1054,1058,1057,1063,1064',
0,0,'1068,1067,1073,1074,1076,1078,1080',0,0,0,0,0,'1002,1001,1005,1066',0,0,0,0,0,'1053,1055,1054,1058,1057,1063,1064',
0,0,'1069,1073,1074,1076,1078,1080',0,0,0,0,0,'1003,1004,1005,1066',0,0,0,0,0,'1054,1058,1057,1063,1064',
0,0,'1070,1073,1074,1076,1078,1080',0,0,0,0,0,'1004,1005,1066',0,0,0,0,0,'1055,1054,1058,1057,1063,1064',
0,0,'1071,1074,1076,1078,1080',0,0,0,0,0,'1005,1066',0,0,0,0,0,'1056,1054,1058,1057,1063,1064',
0,0,'1073,1074,1076,1078,1080',0,0,0,0,0,'1006,1005,1066',0,0,0,0,0,'1057,1063,1064',
0,0,'1074,1076,1078,1080',0,0,0,0,0,'1007,1005,1066',0,0,0,0,0,'1058,1057,1063,1064',
0,0,'1075,1076,1078,1080',0,0,0,0,0,'1008,1003,1004,1005,1066',0,0,0,0,0,'1059,1060,1057,1063,1064',
0,0,'1076,1078,1080',0,0,0,0,0,'1009,1002,1001,1005,1066',0,0,0,0,0,'1060,1057,1063,1064',
0,0,'1077,1078,1080',0,0,0,0,0,'1010,1002,1001,1005,1066',0,0,0,0,0,'1061,1057,1063,1064',
0,0,'1078,1080',0,0,0,0,0,'1011,1002,1001,1005,1066',0,0,0,0,0,'1062,1063,1064',
0,0,'1079,1080',0,0,0,0,0,'1026,1010,1002,1001,1005,1066',0,0,0,0,0,'1063,1064',
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,'1052,1055,1054,1058,1057,1063,1065',
0,0,0,0,0,0,0,0,0,0,0,0,0,0,'1053,1055,1054,1058,1057,1063,1065',
0,0,0,0,0,0,0,0,0,0,0,0,0,0,'1054,1058,1057,1063,1065',
0,0,0,0,0,0,0,0,0,0,0,0,0,0,'1055,1054,1058,1057,1063,1065',
0,0,0,0,0,0,0,0,0,0,0,0,0,0,'1056,1054,1058,1057,1063,1065',
0,0,0,0,0,0,0,0,0,0,0,0,0,0,'1057,1063,1065',
0,0,0,0,0,0,0,0,0,0,0,0,0,0,'1058,1057,1063,1065',
0,0,0,0,0,0,0,0,0,0,0,0,0,0,'1059,1060,1062,1065',
0,0,0,0,0,0,0,0,0,0,0,0,0,0,'1060,1062,1065',
0,0,0,0,0,0,0,0,0,0,0,0,0,0,'1061,1062,1065',
0,0,0,0,0,0,0,0,0,0,0,0,0,0,'1062,1065',
0,0,0,0,0,0,0,0,0,0,0,0,0,0,'1063,1065',
0,0,0,0,0,0,0,0,0,0,0,0,0,0,'1064,1065',
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
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)176,32,148,242,109,4,872,346,0,MIDM2,184,194,805,439,0,MIDM65535,52427,65534[In3,In4][Region,In3]Data based on a map of Helsinki Metropolitan area (YTV liikenne: Pääkaupunkiseudun joukkoliikennekartta 11.8.2002).Route listChanges the Routes inside into a one-dimensional list.var c:= if Routes_inside=0 then 0 else 1;
c:= sum(sum(sum(c,in3),in4),region);
Index f:= 1..c;
Index b:= ['Region','In3','In4','Data'];
var d:= Mdarraytotable(Routes_inside,f,b);
d:= d[.b='Data'];
d:= if textlength(d)=9 then 0 else d;
clean_rows(d)176,136,148,242,283,96,476,3462,49,13,370,610,0,MIDMRoutesindex j:= 1..size(from)^2;
var a:= if from=to1 then ',' else Route_matrix;
a:= a[from=ceil(j/size(from))+1000, to1=(ceil(j/size(from))-j/size(from))*size(from)+1001];
a:= a[.j=sortindex(textlength(a))];
a:= slice(a,j);
var b:=0;
for x[]:= a do (
var y:= size(j);
while y>=1 do (
if findintext(x,slice(a,j,y))>0 then (
b:= if j=y then b+1 else b;
y:=0)
else y:=y-1));
a:= a[.j=subset(b)];
index i:= 1..size(a);
slice(a,i)400,200,148,242,120,130,443,607,0,MIDMStatic nodes'Static nodes' contains previously computed simulations in a static form. The traffic optimising is rather time-consuming work (1 hour per scenario), and it cannot be done in real time. Therefore, all health effect and cost estimates are calculated from previously computed numbers that are stored in this module.ktluser30. lokta 2004 9:4548,24248,256,148,241,0,0,1,1,1,0,,0,1,40,0,609,361,17ScenariosA table of different scenarios to be studied. Each row contains the values for the input variables used for the scenario.Table(Input_var,Scenario)(
0.1,
1,
7,
0,
2,
0,
0,
8,
4,
2,
8
)['Composite fraction','Guarantee level','Lim']288,48,148,242,344,113,476,2242,40,50,1070,303,0,MIDM2,45,69,1129,554,0,MIDM[Formnode Scenarios1]52425,39321,65535[Scenario,Input_var][Scenario,Input_var]ScenarioIndex for a list of scenarios to be modelled.[1]288,80,148,122,102,90,476,547[1]IteratorThe combined result of various variables using the assumptions listed in Scenarios node. Each scenario is run one by one, and the results are stored in this node.var x:= 1;
var a:= 0;
var c:= 0;
while x<= size(scenario) do (
a:= scenarios[scenario=x];
a:= whatif(Outputs1,Scenario_input,a);
c:= if scenario=x then a else c;
x:= x+1);
c176,48,148,122,463,75,476,3672,329,56,629,389,0,MIDM[Formnode Iterator1][Scenario,Period][Index Travel_type][Length,1,Vehicle_type,4,Zone,2,Output1,2,Period,1,Scenario,1]OutputThe output variables from the traffic optimising module:
Number of passenger trips
Vehicle kilometres driven
Parking lots needed for the vehicles that are used
Average vehicle numbers per hour for the 30 most busy links at 8.00-9.00 in the morning
Number of vehicles needed
Waiting time due to traffic jams and waiting for composite vehicle to arrive.['Composite trips','All trips','Nochange trips','Vehicle km','Park rush veh','Waiting']64,80,148,122,511,22,476,2242,14,684,191,203,0,MIDM[0,0,1,0]PeriodMorning-day, evening, and night are looked at separately.[' 6.00-20.00','20.00-24.00',' 0.00- 6.00']64,104,148,122,102,90,476,512[0,0,1,0][' 6.00-20.00','20.00-24.00',' 0.00- 6.00']BAU scenario output164,48,148,24Outputs1Trip iteratortrips/hThe combined result of Trips per hour using the assumptions listed in Scenarios node. Each scenario is run one by one, and the results are stored in this node.var x:= 1;
var a:= 0;
var c:= 0;
while x<= size(scenario) do (
a:= scenarios[scenario=x];
a:= whatif(Trips_per_hour,Scenario_input,a);
c:= if scenario=x then a else c;
x:= x+1);
c176,24,148,122,386,142,476,4762,479,39,629,389,1,MIDM[Time,Vehicle]Scenario datavar a:= Ajot[ajo=choose_ajo, Scen_1=Scen_ind, length1=length, vehicle_type1=vehicle_type, Zone1=zone, Parameter1=output1, period1=period];
if isnumber(a) then a else 0288,232,148,242,248,258,664,303,0,MIDM2,11,81,772,303,0,MIDM[Output1,Scen_ind][Scen_ind,Output1][0,0,0,0]Scen indIndex for a list of scenarios to be modelled.1..10288,192,148,12[0,0,0,1][1,2,3,4,5,6,7,8,9,10]Scenario descriptionA table of different scenarios to be studied. Each row contains the values for the input variables used for the scenario.Desc[Scen_1=Scen_ind, input_var1=input_var, ajo=choose_ajo]['Composite fraction','Guarantee level','Lim']288,160,148,242,107,145,476,2242,27,325,605,277,0,MIDM2,45,69,705,554,0,MIDM52425,39321,65535[Input_var,Scen_ind][Scen_ind,Input_var][0,0,0,0]Scenario selection20.6.2006 Jouni Tuomisto
Esimerkki kuinka 1.0.4-versiossa valitaan eri skenaarioajojen väliltä.
var a:= 0;
a:= if choose_scen = 'Article + sensitivity' then Scen1 else 0;
a:= if choose_scen='Inverse guarantee' then Scen1_0_4 else a;
a:= if choose_scen='Only 9 seat vehicles' then Scen_9seats else a;
index scenario:= if choose_scen = 'Article + sensitivity' then copyindex(Scen1_1) else
(if choose_scen='Inverse guarantee' then copyindex(Scenario1_0) else
(if choose_scen='Only 9 seat vehicles' then copyindex(Scenario5) else [0]));
a:= if choose_scen = 'Article + sensitivity' then a[Scen1_1=scenario] else a;
a:= if choose_scen='Inverse guarantee' then a[Scenario1_0=scenario] else a;
a:= if choose_scen='Only 9 seat vehicles' then a[Scenario5=scenario] else a;
a0408,48,148,242,102,90,476,437Scenario resultsjtue22. elota 2006 8:49ekue22. Augta 2007 17:3048,2464,160,148,241,1,1,1,1,1,0,0,0,01,74,99,592,442,17sequence(0,23.99,0.2)384,64,148,12[0,0.2,0.4,0.6,0.8,1,1.2,1.4,1.6,1.8,2,2.2,2.4,2.6,2.8,3,3.2,3.4,3.6,3.8,4,4.2,4.4,4.6,4.8,5,5.2,5.4,5.6,5.8,6,6.2,6.4,6.6,6.8,7,7.2,7.4,7.6,7.8,8,8.199999999999999,8.4,8.6,8.800000000000001,9,9.199999999999999,9.4,9.6,9.800000000000001,10,10.2,10.4,10.6,10.8,11,11.2,11.4,11.6,11.8,12,12.2,12.4,12.6,12.8,13,13.2,13.4,13.6,13.8,14,14.2,14.4,14.6,14.8,15,15.2,15.4,15.6,15.8,16,16.2,16.4,16.6,16.8,17,17.2,17.4,17.6,17.8,18,18.2,18.4,18.6,18.8,19,19.2,19.4,19.6,19.8,20,20.2,20.4,20.6,20.8,21,21.2,21.4,21.6,21.8,22,22.2,22.4,22.6,22.8,23,23.2,23.4,23.6,23.8]Scen data 1Table(Length1,Vehicle_type1,Zone1,Parameter1,Scen_1,Period1)(
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
9992,951,284,
10.161K,953,290,
10.39K,1004,304,
10.083K,964,272,
10.408K,946,264,
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0,0,0,
0,0,0,
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0,0,0,
0,0,0,
182.972574M,33.516604M,40.948482M,
179.372464M,32.916288M,39.150492M,
177.671741M,32.715882M,39.149436M,
179.866706M,33.117022M,39.149646M,
181.16896M,33.117908M,39.750888M,
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0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,468.0788K,
0,0,458.127633K,
0,0,451.4368K,
0,0,458.059533K,
0,0,468.0843K,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
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0,0,0,
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0,0,0,
0,0,0,
0,0,0,
27.606K,2526,755,
27.65K,2564,730,
27.292K,2637,718,
27.222K,2504,755,
27.38K,2574,726,
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0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
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'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
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105.87K,9896,2769,
105.344K,9796,2782,
104.87K,10.042K,2819,
105.18K,9862,2853,
104.907K,9857,2773,
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1,0.75,0.6,0.5,0.4,0,0,0,0,0,
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0,0,0,0,
0,0,0,0,
0,0,0,0,
0,0,0,0,
0,0,0,0,
0,0,0,0,
0,0,0,0,
0,0,0,0,
0,0,0,0,
0,0,0,0,
0,0,0,0,
0,0,0,0,
0,0,0,0,
0,0,0,0,
0,0,0,0,
0,0,0,0,
0,0,0,0,
0,0,0,0
)384,32,148,242,232,242,646,303,1,MIDM65535,52427,65534[Time_stat,Scen_1][Vehicle_type1,3,Scen_1,1,Time_stat,1]length1['< 5 km','>= 5 km']504,24,148,12['< 5 km','>= 5 km']Vehicle type1['Bus','Minibus','Car (d)','Car (g)']504,48,148,12['Bus','Minibus','Car (d)','Car (g)']Zone1[1,2,3]504,72,148,121,1,0,1,1,1,0,,0,2,102,90,476,3532,488,498,416,303,0,MIDM[1,2,3]Parameter['Composite trips','All trips','Nochange trips','Vehicle km','Park rush veh','Waiting']504,96,148,12['Composite trips','All trips','Nochange trips','Vehicle km','Park rush veh','Waiting']Period1[' 6.00-20.00','20.00-24.00',' 0.00- 6.00']504,120,148,12[' 6.00-20.00','20.00-24.00',' 0.00- 6.00']Input var1['Car fraction','Public fraction','Guarantee level','Large guarantee?','Public matrix','Public level','No-change fraction','Max size','Min direct load','Vehicle types','Drop points/area']504,152,148,12[1,1,1,0]['Car fraction','Public fraction','Guarantee level','Large guarantee?','Public matrix','Public level','No-change fraction','Max size','Min direct load','Vehicle types','Drop points/area']Ajo 11Table(Length1,Vehicle_type1,Zone1,Parameter1,Scen_1,Period1)(
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
1,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
19.269K,2668,8714,
6352,2437,5314,
13.16K,2634,5662,
15.474K,1474,5594,
9690,1432,5576,
12.39K,3611,5754,
10.84K,764,2406,
13.251K,2111,2426,
9742,2535,6146,
11.096K,1600,4594,
0,'NAN',63.067,
0,'NAN',37.833,
0,'NAN',60.133,
0,'NAN',49.833,
0,'NAN',42.033,
0,'NAN',48.167,
0,'NAN',33.967,
0,'NAN',40.367,
0,'NAN',38.033,
0,'NAN',48.4,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
1,0,0,
0,0,0,
0,0,0,
1,0,0,
0,0,0,
0,0,0,
0,0,1,
1,0,1,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
6,2,4,
1,2,1,
0,0,0,
6,0,2,
4,1,1,
6,3,3,
3,1,1,
3,1,1,
5,3,2,
2,2,3,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
0,0,0,
4696,236,16,
10.456K,724,160,
15.148K,1360,360,
18.924K,1888,656,
22.636K,2272,948,
25.372K,2860,1144,
28.168K,3200,1544,
30.74K,3720,1704,
32.624K,4088,2000,
0,0,1,
777,70,49,
1597,132,64,
2470,226,110,
3200,285,148,
4078,392,206,
4885,433,246,
5667,494,253,
6587,613,300,
7322,650,337,
0,0,0,
120,4,0,
544,12,0,
1228,40,0,
1828,72,8,
2660,120,28,
3332,132,16,
4096,168,32,
4980,304,40,
5752,280,40,
0,0,0,
1355.5,67.4,5.6,
3069.3,210.2,44.8,
4520.6,395.6,101.2,
5638.5,550.5,187,
6764.6,678.9,275.6,
7584.5,841.9,328.5,
8409.6,951.1,444.7,
9137.2,1104.6,487,
9686.9,1205.9,580.7,
0,'NAN',0,
136,'NAN',1017,
334,'NAN',2830,
535,'NAN',4517,
731,'NAN',6298,
868,'NAN',8143,
1185,'NAN',9959,
1226,'NAN',11.751K,
1443,'NAN',12.951K,
1632,'NAN',14.876K,
'NAN','NAN','NAN',
9.338,8.982,9.3,
9.397,9.302,8.996,
9.313,9.345,9.196,
9.291,9.356,9.175,
9.214,9.325,9.146,
9.169,9.422,9.274,
9.109,9.39,9.318,
9.044,9.262,9.319,
8.98,9.386,9.393,
0,0,0,
4176,88,0,
11.928K,516,32,
20.092K,1116,80,
27.932K,1736,312,
35.268K,2512,528,
42.38K,3348,752,
49.1K,4024,1044,
55.644K,4644,1292,
61.716K,5240,1712,
1,3,0,
3890,340,188,
7785,675,388,
11.58K,1058,548,
15.467K,1358,749,
19.502K,1710,985,
23.424K,2153,1131,
27.462K,2459,1342,
31.38K,2773,1576,
35.161K,3029,1783,
0,0,0,
96,0,0,
784,12,0,
2060,36,0,
4060,64,8,
6188,120,0,
9004,212,16,
11.556K,312,36,
14.9K,428,52,
17.764K,488,72,
0,0,0,
1413.8,28.1,0,
4312.3,172.5,9,
7529.3,397.2,21.1,
10.7751K,606.5,99.4,
13.9095K,904.3,165.7,
16.9925K,1231.6,242.1,
19.8648K,1484.8,346.9,
22.7346K,1729.6,427.8,
25.3977K,1965.5,587.9,
0,0,0,
223,0,0,
612,0,0,
925,0,0,
1241,0,0,
1627,0,0,
2015,0,0,
2306,0,0,
2543,0,0,
3138,0,0,
'NAN','NAN','NAN',
9.062,8.979,'NAN',
8.967,9.029,9.068,
8.824,9.018,9.003,
8.616,9.051,8.914,
8.463,9.03,9.094,
8.251,8.978,8.996,
8.132,8.912,8.92,
7.939,8.881,8.903,
7.828,8.884,8.963,
0,0,0,
21.584K,496,32,
69.8K,2556,200,
121.432K,5744,796,
173.936K,9748,1480,
225.128K,14.308K,2620,
271.444K,19.06K,4000,
317.728K,24.008K,5524,
361.7K,27.84K,7252,
403.376K,33.088K,9248,
26,8,12,
22.641K,1974,1141,
45.645K,3977,2247,
67.856K,6058,3274,
90.311K,7783,4446,
113.587K,9914,5534,
135.338K,11.989K,6702,
158.559K,14.043K,7842,
180.854K,15.788K,8810,
203.574K,17.96K,9999,
0,0,0,
704,8,0,
4024,36,8,
9928,184,20,
18.512K,308,48,
28.84K,628,84,
40.692K,988,120,
53.848K,1488,204,
68.324K,1984,268,
83.504K,2560,384,
0,0,0,
8609.4,191.8,12,
28.8547K,1001.5,83,
51.1103K,2293.2,316.7,
75.1506K,3883.3,583.6,
98.5797K,5803.1,1062.7,
120.5841K,7834.3,1611.3,
142.478K,9925.8,2244.8,
163.7484K,11.5872K,2944.1,
184.3976K,13.888K,3761.6,
0,0,0,
1095,0,0,
2836,0,0,
4484,0,0,
6242,0,0,
8066,0,0,
9898,0,0,
11.802K,0,0,
12.862K,0,0,
14.814K,0,0,
'NAN','NAN','NAN',
9.016,9.017,9.068,
8.973,9.125,8.697,
8.89,9.042,8.878,
8.779,9.083,8.902,
8.682,9.048,8.943,
8.566,9.014,8.991,
8.469,8.982,8.944,
8.36,8.943,8.987,
8.259,8.919,8.962,
4,0,1,
2430,421,357,
2929,591,569,
3202,698,691,
3312,741,747,
3276,808,886,
3468,829,923,
3402,838,988,
3377,826,1002,
3472,920,1077,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
9.6,0,2,
2203.1,415.3,429.1,
2502,560.8,596,
2621.9,655.6,678,
2671.9,661,737.2,
2662.6,688.8,843.7,
2835.7,727,869.7,
2735,710.7,915.6,
2730.8,722.7,895.1,
2744.3,758.3,969.1,
16,'NAN',218,
335,'NAN',3693,
353,'NAN',4059,
428,'NAN',4313,
381,'NAN',4241,
349,'NAN',4250,
374,'NAN',4101,
479,'NAN',4129,
412,'NAN',4100,
369,'NAN',4051,
7,'NAN',7,
8.119,8.023,7.787,
8.194,8.089,7.967,
8.254,8.099,8.005,
8.271,8.135,8.004,
8.292,8.172,8.045,
8.305,8.178,8.051,
8.324,8.217,8.096,
8.321,8.188,8.121,
8.348,8.248,8.13,
8,4,1,
6830,878,521,
9360,1488,1061,
10.573K,1953,1508,
11.727K,2148,1842,
12.859K,2369,2141,
13.196K,2563,2387,
14.31K,2746,2667,
14.418K,2971,2858,
14.906K,3072,3093,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
21.8,13.3,1.6,
8869.5,1309.9,1065.6,
11.5586K,2070,1753.5,
12.8008K,2544.4,2314.5,
13.6598K,2779.1,2592.7,
14.8222K,2929.6,2886,
15.1139K,3152.9,3220.9,
15.8439K,3340.1,3457.7,
15.9973K,3565.5,3663.2,
16.451K,3638.4,3984,
54,0,0,
762,0,0,
727,0,0,
792,0,0,
762,0,0,
878,0,0,
809,0,0,
936,0,0,
848,0,0,
882,0,0,
7,7,7,
7.945,7.754,7.376,
8.001,7.876,7.638,
8.032,7.949,7.751,
8.062,7.956,7.834,
8.087,7.981,7.884,
8.097,8.002,7.886,
8.128,8.018,7.94,
8.133,8.043,7.936,
8.144,8.048,7.957,
137,36,59,
52.146K,6529,3700,
73.983K,11.362K,7189,
85.75K,14.719K,9854,
93.238K,16.883K,12.816K,
98.93K,19.105K,15.336K,
102.955K,20.558K,17.638K,
106.849K,21.753K,19.855K,
108.471K,23.272K,21.201K,
110.879K,24.295K,22.991K,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
396,114.2,184.3,
78.0746K,12.195K,8786.9,
100.9885K,18.0713K,14.7029K,
113.3641K,22.0473K,18.7866K,
120.3675K,24.3573K,22.4211K,
125.9044K,26.8556K,25.4311K,
130.066K,28.1091K,28.1859K,
133.6172K,29.6235K,30.6793K,
134.9101K,31.0318K,32.0755K,
137.0684K,32.017K,33.9438K,
365,0,0,
4461,0,0,
4981,0,0,
5486,0,0,
5434,0,0,
5384,0,0,
5408,0,0,
5306,0,0,
5409,0,0,
5473,0,0,
7.009,7,7.022,
7.943,7.688,7.383,
8.055,7.867,7.576,
8.103,7.937,7.66,
8.137,7.985,7.754,
8.162,8.02,7.821,
8.179,8.048,7.858,
8.198,8.063,7.902,
8.209,8.086,7.924,
8.22,8.105,7.952,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
8183,678,430,
7248,654,331,
6417,536,310,
5697,513,275,
4950,446,206,
4142,348,197,
3185,294,144,
2356,234,120,
1538,157,73,
809,75,35,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
12.8085K,1198.4,803.5,
11.4374K,1154.8,669.3,
10.2982K,954.7,610.3,
9214.1,939,543.7,
8039.8,812.6,430,
6826.1,648.3,406.8,
5402.8,567,310.3,
4143.2,458.4,266.1,
2828.1,307.7,157.2,
1576.7,162.2,72.9,
7244,'NAN',68.38K,
6606,'NAN',63.233K,
6118,'NAN',56.453K,
4896,'NAN',50.08K,
4166,'NAN',44.581K,
4365,'NAN',38K,
3464,'NAN',31.56K,
2461,'NAN',24.484K,
1773,'NAN',17.312K,
841,'NAN',9591,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
39.016K,3511,1902,
35.385K,3087,1715,
31.554K,2738,1550,
27.709K,2341,1356,
23.566K,2099,1142,
19.488K,1724,957,
15.76K,1327,734,
11.87K,1039,535,
7776,745,373,
3772,353,203,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
92.0975K,9435.5,5502.5,
84.4844K,8447.6,4923.7,
76.3031K,7517.2,4564.3,
68.0175K,6510,4013.2,
58.9465K,5851.7,3423.3,
49.7026K,4963.1,2839.1,
41.3678K,3835.3,2180.3,
31.8305K,3042.7,1630.3,
21.6314K,2189.1,1107.4,
11.0302K,1068.1,607.1,
13.903K,0,0,
13.319K,0,0,
12.294K,0,0,
10.866K,0,0,
9712,0,0,
8212,0,0,
6729,0,0,
5091,0,0,
3739,0,0,
2097,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
226.822K,19.856K,10.976K,
203.828K,17.844K,9948,
181.031K,15.662K,8979,
158.393K,13.714K,7761,
136.076K,11.894K,6716,
113.126K,9978,5495,
90.236K,7818,4421,
67.363K,5957,3274,
44.709K,3907,2301,
22.665K,1925,1149,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
634.5532K,62.3865K,36.8673K,
576.349K,56.6234K,33.5777K,
518.9331K,50.1254K,30.3282K,
460.526K,44.3069K,26.1883K,
402.6305K,39.0065K,22.9309K,
340.0809K,32.9882K,18.8304K,
278.0774K,26.1917K,15.1653K,
213.4045K,20.0904K,11.2442K,
145.9264K,13.4493K,7911.3,
76.3374K,6517.4,3998.4,
70.364K,0,0,
65.974K,0,0,
58.583K,0,0,
52.487K,0,0,
46.794K,0,0,
40.266K,0,0,
32.464K,0,0,
26.199K,0,0,
18.831K,0,0,
10.604K,0,0,
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1376,40,8,
3508,160,16,
5696,372,56,
7636,552,88,
9760,728,180,
11.872K,904,248,
13.648K,1132,320,
15.668K,1340,436,
17.664K,1448,640,
2,0,1,
2461,201,147,
4968,437,242,
7476,679,363,
9762,873,500,
12.465K,1053,603,
15.045K,1299,714,
17.358K,1490,843,
19.882K,1731,966,
22.316K,2021,1105,
0,0,0,
104,0,0,
668,8,0,
1696,44,0,
2668,64,8,
4144,120,4,
5896,176,16,
7232,236,32,
8980,308,52,
10.72K,392,56,
0,0,0,
1246.8,30.6,5,
3618.6,143.4,12.8,
6716.8,357.7,44.1,
9146,548.2,83.8,
12.5549K,731.7,154.2,
15.9048K,893.8,218.2,
18.7564K,1177.8,290,
22.2824K,1443.1,406.5,
25.7715K,1622.9,588.1,
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8.647,8.916,9.176,
8.03,8.8,8.959,
7.392,8.43,8.866,
7.107,8.434,8.417,
6.616,8.196,8.916,
6.175,7.973,8.648,
5.975,7.983,8.489,
5.698,7.849,8.34,
5.504,7.595,8.593,
0,0,0,
28.776K,600,16,
103.252K,3480,192,
193.236K,8228,696,
289.592K,14.484K,1936,
385.808K,21.036K,3316,
482.288K,28.664K,5172,
580.552K,36.46K,6968,
677.264K,45.372K,9648,
775.26K,53.108K,12.068K,
207,62,79,
82.307K,7389,4194,
164.865K,14.561K,8262,
247.184K,21.802K,12.159K,
330.845K,29.041K,16.193K,
412.365K,35.995K,20.219K,
495.034K,43.457K,24.685K,
577.766K,50.661K,28.617K,
659.354K,58.353K,32.407K,
742.406K,65.212K,36.526K,
0,0,0,
1416,8,0,
10.02K,88,8,
26.524K,372,16,
49.808K,844,64,
78.592K,1508,172,
115.232K,2400,228,
153.736K,3564,376,
197.16K,4964,536,
243.58K,6628,708,
0,0,0,
31.699K,577.3,10.6,
124.8657K,3718.5,168.7,
246.2752K,9299,686.6,
383.6554K,16.6002K,1953.5,
527.9755K,24.5603K,3458.5,
677.9784K,34.477K,5501.1,
830.8137K,44.814K,7626.7,
987.7495K,56.3524K,10.5618K,
1.1451849M,67.0935K,13.3232K,
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8.647,8.986,8.71,
8.442,8.914,8.862,
8.249,8.856,8.883,
8.069,8.803,8.771,
7.857,8.754,8.856,
7.706,8.682,8.803,
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1624,240,160,
2081,402,265,
2405,483,404,
2771,499,500,
2948,561,547,
3099,585,601,
3266,595,724,
3458,693,700,
3553,707,699,
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6707.8,1036.7,934.6,
9031.6,1674.9,1119.7,
10.9537K,1923.1,1726.2,
12.69K,2160.2,1852.9,
13.2226K,2358.1,2188.8,
14.1325K,2595.3,2446.9,
14.4922K,2613.3,2780.7,
15.6722K,3067.4,2844.7,
15.6572K,3054.9,2809,
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7.971,7.803,7.384,
8,7.905,7.712,
8.014,7.989,7.825,
8.036,7.965,7.901,
8.076,8.025,7.903,
8.087,8.005,7.947,
8.11,7.99,8.01,
8.111,8.027,7.983,
8.144,8.052,7.957,
324,103,130,
92.708K,11.007K,6047,
137.425K,19.437K,11.753K,
162.999K,25.905K,16.911K,
179.924K,30.831K,21.573K,
192.35K,34.64K,25.918K,
202.098K,38.434K,30.594K,
209.769K,41.257K,34.209K,
214.855K,43.518K,37.298K,
219.47K,45.582K,40.505K,
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3249.8,1023.5,1295.4,
421.897K,62.4063K,44.6658K,
567.2805K,95.531K,73.9453K,
650.4025K,118.1097K,95.5377K,
705.4948K,135.2987K,115.0879K,
745.7603K,148.6669K,131.4507K,
778.1162K,159.8221K,148.5641K,
804.6402K,168.5908K,162.2876K,
821.7222K,176.3391K,173.1046K,
837.0129K,182.6665K,184.9083K,
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8.029,7.809,7.494,
8.083,7.901,7.636,
8.116,7.948,7.699,
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8.162,8.021,7.815,
8.178,8.041,7.852,
8.189,8.063,7.875,
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24.762K,2226,1255,
22.447K,2007,1056,
20.133K,1697,937,
17.48K,1551,814,
14.928K,1302,761,
12.314K,1036,591,
9954,854,464,
7409,661,353,
4861,429,236,
2462,239,131,
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0,0,0,
219.4819K,22.3027K,13.3253K,
201.4193K,19.8445K,11.4297K,
183.3406K,17.0689K,10.0302K,
162.5057K,15.4965K,8902.6,
138.4995K,13.6001K,8470.6,
117.6523K,10.9515K,6538.2,
96.4301K,9116.7,5157.9,
72.9906K,6926.6,3837.3,
49.7293K,4745.3,2540.2,
26.3688K,2650.2,1499.5,
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'NAN','NAN','NAN',
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'NAN','NAN','NAN',
'NAN','NAN','NAN',
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824.691K,72.335K,40.262K,
742.845K,64.933K,35.94K,
659.988K,58.042K,32.227K,
577.174K,50.3K,28.704K,
494.437K,43.53K,24.417K,
411.309K,36.34K,20.18K,
329.845K,28.936K,16.273K,
247.887K,21.7K,12.04K,
164.582K,14.449K,8029,
82.264K,7222,4115,
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0,0,0,
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8.2975279M,799.4336K,464.9211K,
7.5456138M,722.3074K,415.4238K,
6.7678881M,649.2187K,374.9703K,
5.9936226M,565.8136K,333.6525K,
5.199925M,494.8317K,285.1762K,
4.3956002M,416.0128K,235.7427K,
3.5800932M,333.17K,188.9586K,
2.7520976M,252.3943K,141.5849K,
1.8659971M,169.3913K,94.2799K,
955.4618K,85.2907K,48.545K,
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'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
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'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN'
)168,40,148,122,104,114,820,303,0,MIDM2,12,154,710,303,0,MIDM65535,52427,65534[Scen_1,Period1][Scen_1,Parameter1][0,0,1,0][Length1,1,Vehicle_type1,1,Zone1,1,Period1,1,Output11,1,Scen_1,1]Ajo 11 descA table of different scenarios to be studied. Each row contains the values for the input variables used for the scenario.
* Kuvaus: toistetaan version 1.0.1 laskenta niin tarkkaan kuin mahdollista
* KŠytetyt oletukset
o Malliversio 1.7.8
o Scenarios, taulu 8
o Matkamatriisi, vanha mallitettu
o Joukkoliikennematriisi: 2
o From: 1001..1129
o Vehicle_noch: ['d9','d8','d4','d3','d2','d1','c9','c8','c4','c3','c2','c1','Noch']
* Ajon suoritti: Jouni
* KŠynnistettiin: 21.8.2006 16:29
* Valmistui: 22.8.2006 8:00 (ajoaika 26388 s eli 44 min per skenaario) Tulokset
* Huomautuksia: Muistia kului noin puolet eli reilu 1 GB. Aggr_period vie nyt enemmŠn aikaa mutta vŠhemmŠn muistia kuin v. 1.7.7. Solmut joita laskettiin yli 1000 s:
Aggr_period 6483.941, Etappimatkat 5441.484, Time_shift 4801.703, Trips 2318.584, All_trips 1556.698, Route_2 1250.376Table(Input_var1,Scen_1)(
1,0.9,0.8,0.7,0.6,0.5,0.4,0.3,0.2,0.1,
1,1,1,1,1,1,1,1,1,1,
7,7,7,7,7,7,7,7,7,7,
0,0,0,0,0,0,0,0,0,0,
2,2,2,2,2,2,2,2,2,2,
0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,
8,8,8,8,8,8,8,8,8,8,
4,4,4,4,4,4,4,4,4,4,
2,2,2,2,2,2,2,2,2,2,
8,8,8,8,8,8,8,8,8,8
)['Composite fraction','Guarantee level','Lim']56,40,148,122,102,90,476,5342,319,378,927,328,0,MIDM2,45,69,655,554,0,MIDM52425,39321,65535[Scen_1,Input_var1]Ajo 14Table(Length1,Vehicle_type1,Zone1,Parameter1,Scen_1,Period1)(
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
10.238K,941,270,
9419,866,245,
8117,812,208,
7045,659,198,
6114,596,179,
5126,464,126,
4146,377,126,
3040,306,63,
2058,182,43,
998,97,26,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
866.686K,120.931K,92.592K,
820.511K,117.617K,86.45K,
781.249K,116.003K,89.56K,
747.433K,111.535K,80.384K,
711.837K,109.122K,81.614K,
673.065K,105.722K,80.296K,
627.703K,102.102K,76.352K,
579.127K,97.011K,75.846K,
524.384K,94.564K,69.788K,
485.014K,87.48K,66.218K,
0,0,2358.967,
0,0,2184.9,
0,0,2083.733,
0,0,1985.333,
0,0,1882.1,
0,0,1724.3,
0,0,1671.033,
0,0,1532.9,
0,0,1384.3,
0,0,1259.933,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
27.342K,2509,455,
24.617K,2157,414,
22.084K,2046,345,
19.374K,1785,323,
16.4K,1498,273,
13.607K,1264,198,
10.821K,1056,168,
8182,780,138,
5601,509,105,
2695,274,51,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
102.644K,8475,1938,
92.1K,7719,1750,
81.595K,6731,1522,
71.797K,6077,1274,
61.232K,5182,1052,
51.206K,4174,923,
40.308K,3279,814,
30.44K,2529,568,
20.267K,1699,353,
10.288K,866,191,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
7532,1040,467,
13.445K,1401,492,
21.232K,2378,689,
25.373K,2918,847,
36.533K,4405,1311,
38.942K,4983,1481,
41.417K,5441,1502,
43.016K,5740,1627,
45.089K,5938,1779,
46.599K,6404,1874,
0,0,1,
1039,109,26,
2083,197,59,
3081,284,83,
4091,376,104,
5098,464,168,
6252,568,169,
7148,666,156,
8262,751,224,
9162,815,243,
0,0,0,
263,5,0,
1115,38,0,
2011,87,0,
3024,149,8,
4033,240,15,
5190,330,33,
6040,413,10,
7251,463,48,
8178,538,70,
2372.6,340,155.1,
4083.2,440.6,161.4,
6420.4,734.3,224.3,
7597,886.8,270.7,
10.8366K,1327.3,412.5,
11.5363K,1489.8,459.4,
12.219K,1630.7,465.9,
12.7032K,1704.9,499.2,
13.3053K,1753.9,548.8,
13.7405K,1892.7,577.4,
276,138.655,3859,
303,147.276,4073,
405,164.345,4602,
422,178.759,4935,
622,189.379,5392,
622,194.897,5780,
686,217.621,6153,
678,223.793,6468,
707,242.138,6879,
808,258.793,7135,
9.253,9.085,9.104,
9.372,9.156,9.046,
9.405,9.314,9.096,
9.312,9.333,9.16,
9.521,9.518,9.25,
9.422,9.493,9.183,
9.32,9.429,9.168,
9.244,9.392,9.299,
9.146,9.377,9.207,
9.067,9.38,9.188,
24.723K,2499,506,
29.433K,2880,477,
39.243K,3894,614,
43.604K,4244,764,
58.464K,5977,1127,
63.728K,6473,1248,
67.09K,6942,1100,
71.101K,7257,1232,
74.784K,7730,1425,
77.521K,8096,1424,
6659,677,467,
9413,897,531,
12.127K,1151,568,
15.039K,1388,638,
17.667K,1653,662,
20.596K,2019,739,
22.899K,2197,683,
25.954K,2434,807,
28.766K,2629,820,
31.179K,2978,863,
2561,130,37,
2548,95,29,
3080,103,33,
4446,133,20,
5817,201,34,
8025,258,34,
9791,353,29,
12.394K,481,31,
15.027K,579,39,
17.468K,781,43,
9518.9,969.6,207.9,
11.2053K,1098.2,195.2,
14.8531K,1457.3,240.9,
16.6145K,1581.6,304.2,
22.0783K,2225.8,441,
24.2677K,2409.3,482,
25.6167K,2609.1,419,
27.3974K,2756.4,466.5,
29.015K,2949,531.2,
30.0763K,3064.5,541.6,
616,0,0,
647,0,0,
790,0,0,
832,0,0,
942,0,0,
905,0,0,
1029,0,0,
1140,0,0,
1220,0,0,
1232,0,0,
8.704,8.813,8.395,
8.815,8.949,8.512,
8.963,9.097,8.538,
8.85,9.059,8.746,
9.004,9.196,8.759,
8.852,9.14,8.781,
8.735,9.104,8.856,
8.547,9.006,8.853,
8.379,9.003,8.838,
8.232,8.857,8.844,
123.007K,13.064K,1763,
142.961K,14.359K,2015,
161.191K,15.642K,2006,
181.265K,17.069K,2261,
189.761K,17.492K,2093,
210.859K,19.241K,2394,
228.578K,20.634K,2383,
245.038K,22.126K,2652,
260.666K,23.95K,2809,
274.817K,25.044K,2832,
42.81K,5083,1962,
52.712K,5762,2200,
62.678K,6704,2292,
73.21K,7509,2599,
83.091K,8662,2821,
93.676K,9376,2948,
104.061K,10.098K,3195,
113.54K,10.812K,3263,
124.333K,11.784K,3567,
133.986K,12.703K,3686,
10.673K,480,75,
10.892K,459,70,
12.22K,509,77,
15.1K,611,71,
18.488K,765,94,
23.476K,849,97,
29.039K,974,127,
35.715K,1202,95,
41.787K,1592,98,
48.477K,2037,112,
57.0672K,6037,836.7,
65.2623K,6707.2,970.9,
72.9106K,7189.3,999.6,
81.7938K,7719.5,1084.6,
86.6587K,8103.7,1040.4,
94.5412K,8641,1187.2,
102.8894K,9324.7,1127.5,
110.8767K,9882,1280.1,
117.8775K,10.9091K,1359.8,
124.6331K,11.253K,1355.1,
3744,0,0,
3834,0,0,
4249,0,0,
4501,0,0,
4766,0,0,
5038,0,0,
5410,0,0,
5585,0,0,
5929,0,0,
6074,0,0,
8.613,8.771,8.585,
8.696,8.803,8.598,
8.721,8.806,8.589,
8.691,8.806,8.655,
8.577,8.727,8.558,
8.522,8.766,8.574,
8.435,8.747,8.526,
8.328,8.722,8.625,
8.249,8.643,8.609,
8.157,8.574,8.618,
1277,289,360,
1466,354,350,
1415,367,415,
1208,410,426,
1330,365,407,
1297,398,437,
1346,333,464,
1375,352,465,
1316,401,483,
1185,374,454,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
1194.6,325.2,382.3,
1298.3,378.8,403.3,
1219.3,363.1,449.9,
1096.6,386.5,479.8,
1143.6,365.9,447.6,
1119.4,380.4,475.3,
1182.2,328.4,498.6,
1184.1,354.7,491.8,
1157.9,378.2,494.9,
1044.2,367,471.8,
215,137.793,3558,
291,144.931,4021,
297,142.414,4223,
275,142.069,4032,
321,136.241,4113,
291,148.793,4014,
292,145.552,4195,
245,148.552,4217,
265,148.621,4212,
185,151.103,4288,
8.009,7.846,7.889,
8.074,7.858,7.802,
8.104,7.983,7.843,
8.102,7.999,7.822,
8.179,7.968,7.836,
8.183,8.027,7.85,
8.141,7.986,7.872,
8.188,7.943,7.882,
8.179,8.051,7.903,
8.142,8.001,7.879,
6437,1221,900,
6769,1373,988,
7057,1451,1145,
7650,1555,1144,
7918,1578,1176,
8027,1681,1229,
8176,1733,1317,
8264,1743,1328,
8255,1759,1307,
8302,1821,1360,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
8421.6,1875.8,1555.7,
8877.2,2021.5,1643.5,
8943.7,2103.5,1835.7,
9655.8,2214.5,1911,
9609.6,2167.4,1933.8,
9791.1,2306.6,1983,
9809.2,2347.7,2023.3,
9859.3,2306,1993.8,
9761.2,2316.5,2058.9,
9778.4,2454.8,2114.4,
606,0,0,
706,0,0,
634,0,0,
661,0,0,
606,0,0,
541,0,0,
614,0,0,
668,0,0,
703,0,0,
570,0,0,
7.882,7.734,7.588,
7.863,7.764,7.618,
7.894,7.763,7.629,
7.895,7.803,7.614,
7.94,7.81,7.618,
7.949,7.821,7.643,
7.957,7.824,7.686,
7.96,7.854,7.704,
7.967,7.85,7.657,
7.985,7.83,7.661,
32.501K,7692,4986,
36.904K,8150,5381,
39.269K,8491,5501,
40.554K,8815,5712,
42.188K,9389,5874,
42.692K,9487,5969,
43.731K,9674,6362,
43.877K,9979,6321,
45.242K,9824,6778,
46.546K,10.452K,7096,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
49.3478K,12.7759K,10.1839K,
57.6701K,13.5719K,11.1185K,
61.869K,13.9174K,11.3002K,
62.9383K,14.5756K,11.6025K,
64.648K,15.1909K,12.1657K,
64.5537K,15.5487K,11.9752K,
65.898K,15.6544K,12.6456K,
66.2376K,16.0637K,12.6719K,
67.4343K,15.8303K,13.3666K,
68.8924K,16.5825K,13.9455K,
3864,0,0,
4257,0,0,
4602,0,0,
4367,0,0,
4487,0,0,
4413,0,0,
4591,0,0,
4667,0,0,
4625,0,0,
4942,0,0,
7.913,7.801,7.558,
7.884,7.794,7.547,
7.881,7.803,7.545,
7.893,7.808,7.563,
7.898,7.815,7.537,
7.913,7.806,7.562,
7.922,7.815,7.569,
7.919,7.821,7.559,
7.937,7.827,7.579,
7.941,7.836,7.583,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
11.069K,941,215,
10.873K,998,180,
10.885K,943,185,
11.049K,1012,189,
11.035K,989,178,
11.104K,1027,202,
10.922K,1003,185,
10.876K,999,191,
10.988K,1064,187,
11.017K,996,178,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
17.1256K,1579.1,415.9,
16.7905K,1675.1,348.4,
16.9019K,1584,349,
17.1335K,1699.2,374.8,
17.089K,1647.2,360.5,
17.1579K,1724.6,390,
16.9023K,1676.9,371.9,
16.8661K,1669.1,383.4,
17.0375K,1755.3,363.9,
16.9801K,1669.3,345,
2708,2372.828,60.693K,
2804,2398.828,60.691K,
3019,2374.31,60.985K,
3149,2401.586,60.453K,
2711,2387.069,60.822K,
2891,2395.448,60.685K,
2971,2364.793,60.896K,
2976,2339.759,60.984K,
2862,2404.345,60.824K,
2958,2370.793,60.999K,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
45.618K,4172,792,
45.843K,4074,832,
45.927K,4081,843,
45.8K,4022,780,
45.543K,4124,827,
45.61K,4073,843,
45.737K,4161,794,
45.919K,4144,785,
45.661K,4174,896,
45.486K,4049,844,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
105.2669K,10.6975K,2272.3,
105.4725K,10.5542K,2334,
106.1901K,10.4904K,2456.4,
105.7064K,10.3548K,2278.4,
105.1468K,10.5291K,2389.8,
105.226K,10.5332K,2447.9,
105.3507K,10.5542K,2257.1,
105.7853K,10.6159K,2283,
104.8217K,10.6738K,2540.7,
105.0505K,10.3268K,2433.8,
11.212K,0,0,
11.248K,0,0,
11.692K,0,0,
10.839K,0,0,
11.467K,0,0,
11.019K,0,0,
11.423K,0,0,
11.336K,0,0,
11.455K,0,0,
11.384K,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
217.163K,19.385K,3799,
216.705K,19.404K,3838,
217.093K,19.133K,3983,
216.365K,19.378K,3929,
216.573K,19.299K,3902,
217.141K,19.466K,3865,
216.251K,19.428K,3831,
216.449K,19.502K,3924,
216.368K,19.28K,3887,
216.507K,19.476K,3892,
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604.2103K,59.4905K,12.757K,
602.5312K,59.4431K,12.9127K,
604.3514K,58.7124K,13.391K,
603.0542K,59.347K,13.079K,
603.5453K,59.3843K,13.0165K,
603.6124K,59.6322K,12.9166K,
602.8676K,59.701K,12.7623K,
602.8882K,59.7184K,13.0446K,
602.355K,59.3406K,13.0478K,
603.1162K,59.6977K,12.9942K,
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51.034K,0,0,
51.861K,0,0,
50.685K,0,0,
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51.559K,0,0,
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51.309K,0,0,
51.639K,0,0,
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180.449K,13.827K,2408,
162.534K,12.556K,2152,
144.334K,11.011K,1962,
126.708K,9639,1649,
107.767K,8224,1451,
89.731K,6772,1269,
72.014K,5586,992,
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2562,184,28,
3055,199,36,
3756,297,24,
4552,349,32,
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6826,443,79,
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8057,549,49,
8761,685,59,
7538,556,38,
4838,494,222,
5565,550,197,
6247,651,225,
6828,649,240,
7507,711,235,
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8614,819,280,
9562,896,274,
10.202K,952,263,
10.773K,1055,279,
1498,73,12,
1515,72,13,
1609,99,11,
1658,90,8,
2047,101,0,
2284,84,8,
2569,101,0,
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3280,150,11,
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3393,189.1,34.5,
3981.6,228.5,38.5,
5129.9,326.1,26.6,
6193.3,427.7,36.7,
9507.3,661.1,52.2,
10.4535K,623.5,84.6,
11.2535K,747.9,53.1,
12.1404K,848,73.6,
13.2473K,972.6,64.7,
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6.751,7.33,7.457,
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214.804K,18.4K,1983,
234.342K,19.449K,2118,
250.28K,21.053K,2189,
272.728K,21.944K,2175,
282.51K,22.502K,2089,
304.112K,24.077K,2319,
323.437K,24.92K,2310,
346.132K,26.464K,2515,
366.182K,28.167K,2777,
385.433K,30.12K,2749,
215.702K,23.434K,8038,
232.977K,24.617K,8413,
250.885K,26.069K,8736,
268.48K,27.299K,8977,
286.468K,28.709K,9203,
305.399K,30.163K,9613,
322.222K,31.232K,9749,
341.89K,32.571K,9960,
358.455K,33.795K,10.379K,
375.882K,35.451K,10.538K,
36.876K,1253,124,
36.666K,1291,117,
38.536K,1327,145,
41.217K,1452,108,
47K,1592,163,
53.5K,1719,94,
59.237K,1833,133,
68.881K,2057,116,
78.562K,2414,143,
88.09K,2796,145,
308.5257K,24.67K,2654,
328.2794K,25.6334K,2751.3,
348.923K,27.9444K,2942.5,
375.3088K,29.2614K,2822.6,
394.8478K,30.2838K,2829,
423.3711K,32.1969K,2935.1,
448.1603K,33.2296K,3028.4,
479.2192K,34.7527K,3283.3,
505.847K,37.2681K,3676.4,
532.3626K,39.6617K,3543.3,
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8.099,8.56,8.461,
8.126,8.586,8.363,
8.163,8.567,8.509,
8.047,8.521,8.299,
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8622.8,1741.7,769.5,
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9870,2089.3,1427,
10.0027K,2346.8,1439.4,
9945.6,2378.4,1702.9,
10.131K,2483.3,1597,
10.4348K,2515,1463.1,
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76.746K,15.175K,7956,
81.494K,15.886K,8210,
85.097K,15.961K,8432,
87.493K,16.794K,8786,
91.433K,17.328K,8951,
93.681K,17.808K,9245,
95.879K,18.357K,9428,
97.298K,18.683K,9350,
97.56K,18.562K,9689,
100.343K,19.236K,9862,
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400.4095K,83.1379K,51.641K,
424.3802K,86.6024K,53.5517K,
442.6077K,89.5165K,54.8287K,
444.1601K,89.9117K,55.8138K,
468.8406K,96.4524K,58.6779K,
480.6712K,99.7542K,60.5792K,
489.5747K,101.7738K,61.5811K,
495.5841K,104.077K,61.495K,
498.4153K,102.3319K,62.7023K,
501.3324K,104.3048K,63.5111K,
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7.834,7.706,7.462,
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7.854,7.718,7.475,
7.857,7.73,7.472,
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31.213K,2826,582,
31.37K,2873,543,
31.63K,2755,556,
31.469K,2862,526,
31.156K,2776,622,
31.245K,2712,546,
31.579K,2757,550,
31.203K,2738,544,
31.384K,2792,516,
31.287K,2913,577,
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267.1311K,26.6437K,6117.4,
269.6046K,26.8575K,5620.6,
271.7522K,25.6842K,5935.8,
270.4408K,26.7624K,5587.3,
266.35K,26.5256K,6384.2,
268.5152K,25.4603K,5875.1,
270.3391K,26.0833K,5965.8,
267.7258K,25.6797K,5586.4,
269.5933K,26.5427K,5020.7,
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847.785K,76.053K,15.062K,
848.446K,75.962K,15.039K,
847.288K,76.103K,15.11K,
846.815K,75.779K,15.479K,
848.101K,76.007K,15.227K,
846.616K,75.84K,15.072K,
847.276K,76.011K,15.289K,
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846.958K,76.162K,15.121K,
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8.7503169M,849.8962K,179.4961K,
8.7582076M,847.3552K,178.5221K,
8.7382448M,848.5002K,179.668K,
8.7389034M,844.8921K,184.6123K,
8.7499391M,847.3592K,181.5263K,
8.7386196M,847.7165K,179.5465K,
8.7335175M,849.4181K,180.845K,
8.7649369M,852.5186K,180.5388K,
8.7343178M,851.8373K,179.5788K,
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)168,112,148,122,104,114,820,303,0,MIDM2,30,434,710,303,0,MIDM65535,52427,65534[Scen_1,Period1][Scen_1,Parameter1]Ajo 15 descKuvaus: KakkoskŠsikirjoitukseen tulevia tuloksia
KŠytetyt oletukset
Malliversio 1.7.8
Scenarios, taulu 11
Matkamatriisi, HLT
Joukkoliikennematriisi, 3
From: 1001..1129
Vehicle_noch: ['d9','d8','d7','d6','d5','d4','d3','d2','d1','c9','c8','c7','c6','c5','c4','c3','c2','c1','Noch']
Kuka otti ajaakseen: Marja-Leena, Mikan kone
Milloin ajo kŠynnistettiin: 28.8.2006 klo 16:15
Milloin ajo valmistui: 29.8.2006 klo 9 mennessŠ Tulokset
Huomautuksia:Table(Input_var1,Scen_1)(
0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6,
1,0.9,0.8,0.7,0.6,0.5,0.4,0.3,0.2,0.1,
7,7,7,7,7,7,7,7,7,7,
0,0,0,0,0,0,0,0,0,0,
2,2,2,2,2,2,2,2,2,2,
0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,
8,8,8,8,8,8,8,8,8,8,
4,4,4,4,4,4,4,4,4,4,
2,2,2,2,2,2,2,2,2,2,
8,8,8,8,8,8,8,8,8,8
)['Composite fraction','Guarantee level','Lim']56,136,148,122,84,213,927,328,0,MIDM2,45,69,655,554,0,MIDM52425,39321,65535[Scen_1,Input_var1]Ajo 13Table(Length1,Vehicle_type1,Zone1,Parameter1,Scen_1,Period1)(
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
6214,569,163,
6287,555,164,
6094,608,155,
6032,569,172,
6114,596,179,
6132,551,192,
6252,568,169,
6139,563,136,
6211,562,169,
6146,543,162,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
711.283K,107.581K,87.95K,
709.278K,108.779K,86.45K,
701.572K,108.247K,79.696K,
709.032K,108.343K,78.616K,
711.837K,109.122K,81.614K,
712.292K,108.095K,81.732K,
710.091K,110.946K,79.994K,
707.156K,107.403K,79.19K,
704.278K,107.969K,88.146K,
712.39K,108.093K,82.458K,
0,0,1880.733,
0,0,1865.3,
0,0,1841.233,
0,0,1872.667,
0,0,1882.1,
0,0,1882.267,
0,0,1857.667,
0,0,1852.233,
0,0,1851.133,
0,0,1859.267,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
16.551K,1525,276,
16.416K,1468,278,
16.563K,1522,267,
16.614K,1521,282,
16.4K,1498,273,
16.477K,1555,276,
16.273K,1526,252,
16.523K,1536,270,
16.502K,1468,262,
16.346K,1507,243,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
61.413K,5105,1218,
61.428K,5133,1186,
61.384K,5023,1124,
61.385K,5224,1089,
61.232K,5182,1052,
61.698K,5202,1131,
61.556K,5029,1126,
61.341K,5104,1135,
61.621K,4988,1151,
61.261K,5150,1210,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
27.988K,3545,1032,
32.179K,4118,1085,
35.572K,4656,1172,
36.004K,5084,1328,
38.26K,5332,1462,
40.131K,5860,1637,
41.461K,6337,1733,
43.373K,6552,1930,
44.875K,7067,1941,
45.95K,7208,2157,
4024,372,108,
5210,519,130,
6266,590,145,
7442,684,165,
8447,767,180,
9628,899,208,
10.762K,976,239,
11.694K,1074,214,
13.014K,1210,256,
13.941K,1264,271,
2914,149,22,
4139,274,17,
5150,356,18,
6409,397,14,
7418,514,32,
8663,624,41,
9760,654,74,
10.748K,788,42,
12.068K,931,71,
12.993K,917,65,
8364.7,1077.5,331.2,
9618.8,1250.2,333.7,
10.5648K,1413.5,373.1,
10.6654K,1516.2,410.9,
11.2895K,1604.3,449.4,
11.7861K,1744.3,506.2,
12.2049K,1872.4,549.1,
12.7394K,1955,601.8,
13.1136K,2126.3,592.3,
13.4147K,2129.8,670,
566,186.069,5408,
596,223.069,6556,
675,267.552,7865,
726,313.276,8941,
807,355.138,10.225K,
821,391.517,11.44K,
879,438.552,12.587K,
895,466.759,13.912K,
921,522.345,14.881K,
1000,565.966,16.08K,
9.214,9.359,9.044,
9.125,9.249,9.112,
9.079,9.235,9.131,
8.911,9.265,9.197,
8.843,9.202,9.177,
8.732,9.164,9.148,
8.631,9.208,9.095,
8.57,9.115,9.235,
8.464,9.067,9.166,
8.384,9.114,9.241,
47.86K,4643,814,
55.468K,5573,854,
61.25K,6503,1008,
67.299K,7160,1298,
73.587K,7910,1432,
81.028K,8755,1592,
87.321K,9804,1565,
93.6K,10.311K,1831,
99.411K,11.22K,2001,
105.006K,11.989K,2086,
17.45K,1661,646,
22.156K,2006,746,
26.744K,2508,814,
31.448K,2900,912,
35.729K,3256,979,
40.593K,3779,1090,
44.834K,4286,1068,
49.662K,4591,1219,
54.52K,4975,1371,
58.579K,5376,1427,
5788,196,37,
8728,252,30,
12.394K,438,45,
16.408K,618,37,
20.147K,807,84,
24.68K,1167,114,
29.021K,1367,70,
33.308K,1671,115,
38.355K,1869,143,
42.272K,2225,177,
18.3619K,1745.1,315.2,
21.5696K,2089.6,326.8,
24.3029K,2498.2,390,
26.6809K,2763.7,506.5,
29.3824K,3061.8,553,
32.5931K,3417.4,637,
35.2742K,3801.9,583.1,
37.755K,4051.6,715.9,
40.1515K,4427,769.2,
42.595K,4718.8,815,
965,0,0,
1051,0,0,
1407,0,0,
1491,0,0,
1694,0,0,
1876,0,0,
2155,0,0,
2306,0,0,
2472,0,0,
2756,0,0,
8.743,9.002,8.603,
8.543,9.028,8.715,
8.287,8.945,8.666,
8.059,8.837,8.786,
7.879,8.784,8.623,
7.691,8.591,8.559,
7.526,8.586,8.756,
7.389,8.452,8.636,
7.198,8.429,8.606,
7.103,8.332,8.549,
205.765K,19.526K,2358,
256.238K,24.544K,3025,
306.666K,30.032K,3414,
355.353K,35.398K,4364,
395.715K,40.634K,4714,
426.8K,44.399K,5402,
463.191K,49.361K,6212,
500.384K,53.523K,7005,
533.155K,58.152K,7597,
566.17K,62.685K,8344,
84.041K,8453,2682,
104.993K,10.272K,3162,
126.569K,12.253K,3513,
148.534K,14.232K,3939,
169.511K,16.251K,4358,
191.845K,18.069K,4691,
212.682K,20.057K,5216,
234.565K,21.882K,5460,
256.392K,23.992K,5850,
277.713K,26.003K,6159,
18.995K,685,79,
27.542K,969,114,
38.758K,1448,126,
52.297K,1967,158,
67.084K,2633,192,
83.622K,3495,229,
100.248K,4196,301,
118.317K,5112,310,
137.061K,6130,353,
155.368K,7387,418,
91.094K,8690.5,1116.5,
113.5515K,10.8885K,1457,
136.623K,13.2944K,1635.9,
159.1247K,15.6383K,2093.5,
178.1989K,17.9336K,2230.2,
193.6451K,19.8168K,2582,
211.3095K,21.9242K,2929.7,
228.9723K,23.8216K,3287,
244.8K,25.8708K,3505.2,
260.3232K,28.1241K,3930.6,
4775,0,0,
5814,0,0,
6868,0,0,
7748,0,0,
8831,0,0,
9774,0,0,
10.718K,0,0,
12.043K,0,0,
12.737K,0,0,
13.627K,0,0,
8.675,8.832,8.647,
8.626,8.848,8.616,
8.543,8.821,8.642,
8.447,8.817,8.665,
8.331,8.791,8.656,
8.174,8.715,8.66,
8.06,8.699,8.639,
7.949,8.654,8.664,
7.828,8.614,8.68,
7.729,8.548,8.656,
1336,396,397,
1320,405,403,
1378,381,462,
1414,380,442,
1422,426,449,
1414,408,463,
1474,372,455,
1404,401,454,
1505,384,502,
1336,435,471,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
1191,383.2,430,
1160.2,368.6,460.1,
1238.5,363.5,504.3,
1263.6,359.9,480.6,
1258.6,386.3,475.5,
1261.4,401,468.2,
1291.1,352,465.6,
1241,367.4,459.8,
1333.3,347.2,503.2,
1246.5,372.7,470.8,
306,146.759,4042,
364,147.552,4347,
225,147.552,4178,
227,151,4357,
331,145.966,4263,
238,125.931,3938,
248,120.345,4131,
272,124.621,3877,
220,123.931,3995,
333,125.379,3978,
8.13,7.948,7.852,
8.144,8.054,7.812,
8.152,8,7.83,
8.151,8.008,7.844,
8.174,8.006,7.875,
8.159,8.005,7.907,
8.164,8.03,7.898,
8.164,8.058,7.891,
8.194,8.069,7.901,
8.155,8.151,7.916,
7830,1578,1096,
8529,1711,1209,
8277,1665,1277,
8326,1672,1213,
8504,1736,1283,
8341,1843,1311,
8290,1809,1319,
8535,1955,1430,
8481,1841,1464,
8537,1951,1510,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
9565.6,2193.8,1855.2,
10.5146K,2412.4,1916.5,
10.1985K,2359.4,1994,
10.2518K,2292.4,1970.4,
10.1576K,2406.6,2062.3,
10.1183K,2429.4,2033.6,
9967.1,2399.1,2156.4,
10.1451K,2581.9,2231.5,
10.0155K,2442.7,2267.9,
9970.1,2619.4,2302.3,
547,0,0,
691,0,0,
553,0,0,
617,0,0,
539,0,0,
596,0,0,
606,0,0,
595,0,0,
591,0,0,
566,0,0,
7.93,7.795,7.606,
7.934,7.802,7.668,
7.938,7.79,7.67,
7.95,7.832,7.635,
7.98,7.813,7.656,
7.972,7.861,7.688,
7.988,7.865,7.639,
8.003,7.865,7.693,
8.007,7.85,7.687,
8.019,7.87,7.702,
40.512K,8785,5730,
43.322K,9622,6506,
45.779K,10.366K,7220,
46.735K,10.874K,7435,
47.916K,11.193K,8107,
48.123K,11.371K,8360,
48.456K,11.576K,8901,
48.497K,11.862K,8963,
48.349K,12.263K,9637,
48.405K,12.327K,9938,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
62.2059K,14.4976K,11.428K,
65.2537K,15.5464K,12.856K,
67.4334K,16.3808K,13.9782K,
67.8239K,16.9175K,14.3823K,
68.1408K,17.3282K,15.3196K,
67.7196K,17.3797K,15.7103K,
67.7092K,17.5145K,16.5007K,
67.8712K,17.8627K,16.595K,
67.384K,18.4001K,17.6026K,
67.0642K,18.2979K,17.8455K,
4476,0,0,
4600,0,0,
4461,0,0,
4770,0,0,
4495,0,0,
4062,0,0,
4478,0,0,
4069,0,0,
4333,0,0,
4327,0,0,
7.905,7.808,7.566,
7.93,7.828,7.584,
7.951,7.845,7.598,
7.975,7.865,7.603,
7.995,7.878,7.625,
8.005,7.886,7.633,
8.015,7.896,7.647,
8.018,7.904,7.656,
8.025,7.916,7.674,
8.035,7.921,7.687,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
11.069K,941,215,
9834,899,157,
8725,754,152,
7701,702,133,
6679,598,102,
5568,505,96,
4306,404,72,
3231,334,60,
2083,225,29,
1090,101,14,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
17.1256K,1579.1,415.9,
15.3099K,1517.8,310.7,
13.6873K,1266.1,286.4,
12.1506K,1214.4,272.6,
10.6139K,1052.1,219.1,
9006.1,908.2,195.7,
7090.1,751.1,152.5,
5497.9,624.7,132,
3664,430.9,63.3,
2068,212.9,29.4,
2708,2372.828,60.692K,
2674,2181.483,55.441K,
2481,1936.379,50.341K,
2300,1739.655,44.287K,
1811,1483.793,39.328K,
1818,1248.724,33.343K,
1387,1007.966,27.716K,
1091,758.069,22.22K,
640,526.448,15.614K,
485,261.931,8902,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
45.618K,4172,792,
41.301K,3654,753,
36.831K,3248,675,
32.151K,2774,547,
27.481K,2521,510,
22.743K,2022,414,
18.35K,1602,325,
13.87K,1231,241,
9006,869,188,
4435,418,88,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
105.2669K,10.6975K,2272.3,
96.1098K,9583.2,2129.3,
87.1552K,8526.7,1985.5,
77.1227K,7395.2,1619.2,
66.8191K,6753.1,1508.1,
56.3897K,5613.9,1208.9,
46.7501K,4469.1,942,
36.349K,3488.5,729.8,
24.5038K,2501.1,536.1,
12.7677K,1216.2,262.6,
11.212K,0,0,
10.448K,0,0,
9577,0,0,
8272,0,0,
7646,0,0,
6221,0,0,
5056,0,0,
4508,0,0,
3101,0,0,
1994,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
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)168,88,148,122,104,114,820,303,0,MIDM2,30,434,710,303,0,MIDM65535,52427,65534[Scen_1,Period1][Scen_1,Parameter1]Ajo 14 descTable(Input_var1,Scen_1)(
1,1,1,1,1,1,1,1,1,1,
1,0.9,0.8,0.7,0.6,0.5,0.4,0.3,0.2,0.1,
7,7,7,7,7,7,7,7,7,7,
0,0,0,0,0,0,0,0,0,0,
2,2,2,2,2,2,2,2,2,2,
0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,
8,8,8,8,8,8,8,8,8,8,
4,4,4,4,4,4,4,4,4,4,
2,2,2,2,2,2,2,2,2,2,
8,8,8,8,8,8,8,8,8,8
)['Composite fraction','Guarantee level','Lim']56,112,148,122,-29,144,927,328,0,MIDM2,45,69,655,554,0,MIDM52425,39321,65535[Scen_1,Input_var1]Ajo 15Table(Length1,Vehicle_type1,Zone1,Parameter1,Scen_1,Period1)(
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10.238K,941,270,
9419,866,245,
8117,812,208,
7045,659,198,
6114,596,179,
5126,464,126,
4146,377,126,
3040,306,63,
2058,182,43,
998,97,26,
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866.686K,120.931K,92.592K,
820.511K,117.617K,86.45K,
781.249K,116.003K,89.56K,
747.433K,111.535K,80.384K,
711.837K,109.122K,81.614K,
673.065K,105.722K,80.296K,
627.703K,102.102K,76.352K,
579.127K,97.011K,75.846K,
524.384K,94.564K,69.788K,
485.014K,87.48K,66.218K,
0,0,2358.967,
0,0,2184.9,
0,0,2083.733,
0,0,1985.333,
0,0,1882.1,
0,0,1724.3,
0,0,1671.033,
0,0,1532.9,
0,0,1384.3,
0,0,1259.933,
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27.342K,2509,455,
24.617K,2157,414,
22.084K,2046,345,
19.374K,1785,323,
16.4K,1498,273,
13.607K,1264,198,
10.821K,1056,168,
8182,780,138,
5601,509,105,
2695,274,51,
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102.644K,8475,1938,
92.1K,7719,1750,
81.595K,6731,1522,
71.797K,6077,1274,
61.232K,5182,1052,
51.206K,4174,923,
40.308K,3279,814,
30.44K,2529,568,
20.267K,1699,353,
10.288K,866,191,
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26.6K,3393,873,
28.375K,3825,935,
29.979K,3974,989,
37.036K,5199,1371,
38.26K,5332,1462,
39.353K,5626,1682,
51.804K,7642,2141,
53.145K,7795,2271,
53.878K,8011,2499,
58.633K,8695,2534,
4434,378,83,
5337,500,107,
6418,578,123,
7545,689,157,
8447,767,180,
9580,851,242,
10.558K,972,241,
11.447K,1089,236,
12.512K,1171,297,
13.53K,1219,308,
3335,150,4,
4213,255,18,
5329,338,5,
6507,419,9,
7418,514,32,
8517,566,72,
9558,678,56,
10.472K,822,46,
11.57K,842,80,
12.603K,918,102,
7946.8,1012.7,282.2,
8450.7,1160.7,298.4,
8941.2,1201.5,311.6,
10.9601K,1557.3,424.3,
11.2895K,1604.3,449.4,
11.6214K,1685.1,534.5,
15.1398K,2254.8,664.3,
15.4866K,2316.1,691.2,
15.7193K,2354.5,765.6,
17.1142K,2570.6,768.6,
547,297.69,9042,
565,313.655,9266,
664,326.552,9698,
746,346.379,9867,
807,355.138,10.225K,
881,362.103,10.631K,
968,378.897,10.85K,
992,390.414,11.168K,
890,409.31,11.499K,
975,421.034,11.713K,
9.08,9.305,9.164,
8.964,9.207,9.037,
8.802,9.134,9.165,
8.937,9.263,9.257,
8.843,9.202,9.177,
8.713,9.169,9.021,
9.093,9.434,9.238,
9.024,9.347,9.309,
8.922,9.352,9.257,
8.93,9.368,9.248,
53.68K,5571,925,
56.878K,5816,953,
60.265K,6360,972,
70.996K,7698,1371,
73.587K,7910,1432,
77.452K,8441,1539,
96.716K,10.735K,1817,
100.116K,10.928K,1892,
102.577K,11.403K,2098,
107.259K,11.843K,2124,
24.898K,2296,762,
27.738K,2509,846,
30.391K,2829,912,
33.163K,3049,959,
35.729K,3256,979,
38.794K,3627,1079,
41.249K,3799,1008,
44.401K,4079,1105,
46.954K,4365,1170,
49.264K,4622,1231,
10.794K,420,60,
12.827K,484,68,
15.254K,581,60,
17.902K,739,42,
20.147K,807,84,
23.215K,1012,88,
25.734K,1103,75,
28.767K,1337,103,
31.693K,1620,86,
34.035K,1760,99,
21.0702K,2140.1,367.2,
22.6165K,2229,377.9,
24.1678K,2469.8,382.4,
28.2098K,2989.1,526.5,
29.3824K,3061.8,553,
31.131K,3284,613.5,
37.8867K,4104.4,698.8,
39.3074K,4189.3,717.9,
40.3936K,4436.7,786.9,
42.3482K,4515.2,809.9,
1541,0,0,
1464,0,0,
1801,0,0,
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8.247,8.846,8.552,
8.1,8.792,8.495,
7.931,8.77,8.571,
8.018,8.8,8.785,
7.879,8.784,8.623,
7.713,8.656,8.619,
8.067,8.913,8.833,
7.929,8.771,8.725,
7.787,8.671,8.843,
7.724,8.623,8.818,
325.231K,33.958K,4076,
341.977K,35.244K,4401,
361.403K,37.12K,4395,
372.254K,38.188K,4698,
395.715K,40.634K,4714,
412.979K,42.357K,4970,
410.198K,40.91K,4716,
424.98K,42.793K,5108,
436.54K,44.181K,5473,
447.398K,44.815K,5286,
129.395K,12.792K,3483,
139.287K,13.527K,3774,
149.916K,14.328K,3894,
159.884K,15.31K,4114,
169.511K,16.251K,4358,
180.495K,17.193K,4455,
190.443K,17.817K,4693,
199.701K,18.587K,4811,
210.276K,19.399K,5162,
220.688K,20.427K,5262,
41.16K,1617,186,
46.876K,1803,180,
53.455K,1956,177,
60.059K,2379,163,
67.084K,2633,192,
75.705K,2980,178,
83.265K,3247,255,
91.468K,3593,236,
99.855K,4104,271,
109.252K,4785,229,
146.137K,15.0717K,1954.4,
153.5978K,15.681K,2096.5,
162.6134K,16.4037K,2079.9,
167.956K,16.9604K,2243.5,
178.1989K,17.9336K,2230.2,
186.2332K,18.7032K,2327.3,
187.4164K,18.4448K,2218.4,
194.1748K,19.0876K,2451.5,
199.7447K,19.8804K,2595.3,
205.2397K,20.0308K,2519.4,
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1238.2,381.5,491.5,
1204.4,370.9,478.8,
1258.6,386.3,475.5,
1188.1,363.3,486.9,
1256.7,367.5,443.8,
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247,125.414,4052,
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331,145.966,4263,
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325,152.31,4314,
267,147.69,4379,
389,149.586,4434,
277,152.483,4536,
8.103,7.989,7.88,
8.143,7.994,7.835,
8.16,8.069,7.846,
8.123,8.055,7.851,
8.174,8.006,7.875,
8.159,8.092,7.813,
8.219,8.021,7.872,
8.169,8.128,7.924,
8.202,8.121,7.852,
8.239,8.028,7.912,
7857,1526,1070,
8210,1626,1119,
8222,1706,1276,
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9863.5,2269.9,1734.6,
9966.4,2325.8,1751.4,
10.0679K,2385,1966.4,
10.3173K,2341.5,2035.2,
10.1576K,2406.6,2062.3,
9967.5,2437.6,2054.3,
10.1246K,2446.7,2100.1,
10.3084K,2438.9,2109.5,
10.1529K,2441.9,2155.1,
10.0386K,2627.4,2286.4,
550,0,0,
628,0,0,
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7.98,7.813,7.656,
7.961,7.791,7.669,
7.985,7.844,7.697,
7.982,7.851,7.734,
7.989,7.855,7.699,
8.003,7.887,7.696,
47.566K,11.094K,7109,
47.763K,11.103K,7423,
47.576K,11.084K,7770,
47.329K,10.914K,7587,
47.916K,11.193K,8107,
47.921K,11.494K,8316,
49.348K,11.928K,8396,
48.729K,11.753K,8301,
49.224K,11.582K,8744,
49.529K,12.379K,9190,
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69.4496K,17.438K,13.6461K,
69.0816K,17.2633K,14.4318K,
68.8558K,17.1468K,14.904K,
68.3181K,16.9494K,14.5621K,
68.1408K,17.3282K,15.3196K,
68.1729K,17.7232K,15.6303K,
69.547K,17.9943K,15.9876K,
69.6603K,18.025K,15.9266K,
69.7256K,17.989K,16.5026K,
70.282K,18.8583K,17.0329K,
4025,0,0,
4300,0,0,
4188,0,0,
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4495,0,0,
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4632,0,0,
4896,0,0,
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7.977,7.878,7.615,
7.979,7.868,7.614,
7.995,7.878,7.625,
7.997,7.885,7.641,
7.998,7.892,7.62,
7.996,7.879,7.606,
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6550,562,121,
6585,607,115,
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6622,640,128,
6616,599,113,
6577,576,111,
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10.5083K,1009.1,268,
10.4937K,1069.6,201.8,
10.4965K,998.1,232.9,
10.4967K,1057.9,238.1,
10.6139K,1052.1,219.1,
10.5135K,1112.3,260.3,
10.5337K,1040.7,250.4,
10.5093K,1008.8,229.3,
10.722K,1132.8,230.8,
10.5814K,1045.4,235.6,
1913,1489.552,38.845K,
2004,1487.931,39.091K,
1918,1479.793,39.455K,
2019,1503.897,38.935K,
1811,1483.793,39.326K,
1761,1491.241,38.879K,
1913,1473.621,38.98K,
1964,1448.586,39.168K,
1940,1499.034,38.768K,
2107,1486.034,38.898K,
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27.379K,2553,497,
27.518K,2462,517,
27.663K,2403,499,
27.676K,2361,459,
27.481K,2521,510,
27.412K,2465,503,
27.387K,2559,469,
27.472K,2499,487,
27.473K,2438,546,
27.401K,2405,476,
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66.7516K,6878.4,1443.6,
66.5797K,6721.2,1467.4,
67.3347K,6502.8,1494.4,
67.5215K,6403.8,1369.3,
66.8191K,6753.1,1508.1,
66.9414K,6667.4,1496.4,
66.5091K,6817.6,1383.5,
66.9337K,6717.2,1437.2,
66.7025K,6558.1,1575.6,
66.8318K,6479,1414,
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130.578K,11.676K,2278,
130.13K,11.639K,2264,
129.855K,11.509K,2381,
129.691K,11.577K,2414,
130.153K,11.71K,2365,
130.322K,11.649K,2358,
129.869K,11.709K,2333,
130.288K,11.727K,2376,
130.425K,11.665K,2292,
129.805K,11.752K,2316,
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383.0858K,37.156K,7765.1,
381.9398K,37.1091K,7699.1,
380.801K,36.6291K,8138.4,
380.7955K,36.8536K,8126.1,
382.1598K,37.5434K,8002.3,
382.7675K,37.0185K,7998.8,
381.4788K,37.3668K,7896.9,
382.0664K,37.3665K,7998.1,
382.2874K,37.2088K,7802.7,
381.1567K,37.5194K,7807.6,
33.504K,0,0,
33.887K,0,0,
34.456K,0,0,
33.334K,0,0,
34.6K,0,0,
33.797K,0,0,
33.753K,0,0,
34.267K,0,0,
33.133K,0,0,
33.645K,0,0,
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6636,568,117,
5808,492,95,
5400,453,82,
4659,396,68,
3956,352,61,
3271,278,66,
2631,227,52,
1977,185,28,
1339,123,21,
680,53,12,
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180.449K,13.827K,2408,
162.534K,12.556K,2152,
144.334K,11.011K,1962,
126.708K,9639,1649,
107.767K,8224,1451,
89.731K,6772,1269,
72.014K,5586,992,
54.014K,4158,724,
35.856K,2739,494,
17.881K,1352,265,
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14.212K,1329,155,
14.644K,1448,199,
15.386K,1451,183,
16.561K,1609,191,
17.184K,1557,170,
17.828K,1618,246,
19.677K,1875,199,
20.333K,1958,252,
21.122K,2061,226,
21.539K,2164,260,
17.221K,1618,461,
18.074K,1654,439,
18.827K,1755,454,
19.455K,1785,444,
19.932K,1833,488,
20.74K,1858,467,
21.136K,1910,491,
22.021K,2028,487,
22.627K,2089,470,
23.282K,2238,501,
8094,329,16,
8655,360,30,
9282,378,35,
9739,428,22,
10.437K,451,29,
10.902K,452,23,
11.31K,491,8,
12.037K,592,27,
12.58K,620,33,
13.318K,722,42,
19.1015K,1454.5,147.4,
19.6213K,1578.3,202.2,
21.0107K,1521,184.9,
23.5K,1803.8,200.8,
24.3024K,1836.6,178.8,
25.2651K,2003.6,241.2,
29.5249K,2514.3,211.7,
30.5135K,2573.8,327.1,
31.9626K,2727.7,269.7,
32.4358K,3040.5,371,
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5.604,7.538,8.281,
5.474,7.601,7.903,
5.38,7.528,7.632,
5.49,7.457,8.144,
5.373,7.283,7.847,
5.34,7.328,8.391,
5.591,7.444,8.636,
5.488,7.208,8.189,
5.461,7.239,7.888,
5.322,6.987,7.857,
664.171K,58.646K,5297,
686.789K,60.894K,5852,
701.291K,62.327K,5619,
715.013K,62.453K,5703,
722.993K,62.467K,5776,
741.116K,64.327K,6031,
747.655K,63.621K,5944,
768.337K,65.265K,5763,
785.204K,67.07K,6439,
799.117K,68.045K,5960,
555.179K,53.763K,14.062K,
572.151K,55.034K,14.5K,
588.892K,56.52K,14.627K,
606.972K,57.809K,15.046K,
625.986K,59.019K,15.222K,
643.997K,60.708K,15.737K,
660.529K,61.683K,15.866K,
681.906K,63.036K,15.936K,
697.068K,64.307K,16.528K,
714.501K,65.589K,16.547K,
146.932K,5119,331,
154.981K,5219,337,
164.727K,5837,310,
176.51K,6306,327,
189.121K,6659,363,
201.973K,7348,308,
213.092K,7436,400,
229.008K,8030,387,
242.93K,8625,414,
256.237K,9273,431,
963.1173K,79.6002K,7171.3,
991.7294K,81.9458K,7836,
1.0125581M,84.1884K,7402,
1.0387814M,84.6295K,7641.5,
1.062084M,85.0028K,7785.7,
1.0890207M,87.8006K,8033,
1.1070058M,88.1311K,7949.3,
1.1408055M,89.7548K,7760,
1.1636609M,92.3596K,8620.2,
1.1856055M,93.7326K,7899.9,
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7.815,8.565,8.475,
7.758,8.519,8.499,
7.679,8.467,8.475,
7.572,8.432,8.451,
7.506,8.387,8.531,
7.412,8.356,8.405,
7.333,8.32,8.432,
7.263,8.293,8.444,
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1952,357,216,
1956,357,235,
1884,382,230,
2048,380,250,
2051,392,297,
2021,364,254,
2206,432,334,
2201,479,297,
2292,423,313,
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10.6087K,1949.6,980,
10.7679K,2203.6,1129,
10.7836K,2145,1208.2,
11.4207K,2320.9,1312.9,
11.4248K,2399.6,1438.9,
11.1699K,2078.2,1432.7,
12.933K,2813.4,2215.3,
12.579K,2995.3,1966.7,
13.067K,2832,2134,
13.6034K,2650.8,2194.5,
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7.912,7.832,7.669,
7.882,7.696,7.667,
7.867,7.801,7.622,
7.895,7.776,7.607,
7.939,7.736,7.691,
7.917,7.803,7.646,
7.924,7.788,7.639,
7.939,7.822,7.6,
7.954,7.753,7.602,
7.96,7.814,7.631,
99.507K,22.937K,13.043K,
99.597K,22.94K,13.002K,
102.624K,23.42K,13.35K,
103.425K,23.752K,13.582K,
108.822K,24.728K,13.775K,
110.034K,24.96K,14.056K,
111.013K,25.316K,14.041K,
112.903K,25.681K,14.02K,
113.017K,25.409K,14.158K,
113.96K,25.54K,14.559K,
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460.4642K,113.4338K,78.338K,
458.0887K,112.4573K,78.7736K,
472.0015K,115.9375K,79.8953K,
479.2349K,116.873K,81.2027K,
517.8888K,126.1666K,83.69K,
521.3759K,127.4011K,85.5176K,
531.4703K,128.8773K,87.0611K,
544.3587K,133.1901K,87.1752K,
543.8592K,130.3816K,88.7792K,
553.0631K,132.4885K,89.8717K,
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7.952,7.83,7.552,
7.963,7.839,7.544,
7.958,7.829,7.55,
7.956,7.837,7.562,
7.945,7.82,7.552,
7.95,7.824,7.556,
7.951,7.83,7.545,
7.948,7.828,7.544,
7.952,7.83,7.531,
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18.83K,1702,343,
18.861K,1769,301,
19.05K,1651,327,
18.842K,1726,322,
18.731K,1654,369,
18.766K,1635,343,
19.057K,1666,339,
18.744K,1606,331,
18.959K,1655,309,
18.778K,1730,355,
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170.0948K,16.9478K,3573.1,
170.0705K,16.9999K,3181.2,
171.2865K,16.024K,3567.6,
170.7458K,16.8158K,3416.8,
168.2475K,16.5698K,3885.1,
168.6342K,16.0233K,3782.9,
171.9225K,16.3162K,3668.2,
170.4132K,15.9605K,3373.9,
172.0685K,16.1631K,2992.7,
169.5879K,17.1949K,3649.4,
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508.308K,45.724K,9038,
509.272K,45.545K,8952,
509.281K,45.652K,9219,
508.323K,45.269K,9410,
508.583K,45.697K,9208,
508.018K,45.295K,8948,
508.969K,45.56K,9172,
508.248K,45.596K,9169,
508.345K,45.65K,8972,
508.824K,45.657K,9178,
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5.490104M,527.5326K,108.7452K,
5.4971801M,524.1576K,107.2983K,
5.4897309M,524.2204K,111.4799K,
5.4852748M,520.555K,113.0743K,
5.4879325M,524.3988K,110.4937K,
5.4874435M,521.9861K,107.5848K,
5.4896408M,524.2651K,109.911K,
5.4918759M,525.8214K,109.9486K,
5.4829812M,526.8071K,107.1254K,
5.4938503M,524.1193K,110.9175K,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN'
)168,136,148,122,104,114,820,303,0,MIDM2,30,434,710,303,0,MIDM65535,52427,65534[Scen_1,Period1][Scen_1,Parameter1]Ajo 13 descAjo 13
* Kuvaus: KakkoskŠsikirjoitukseen tulevia tuloksia
* KŠytetyt oletukset
o Malliversio 1.7.8
o Scenarios, taulu 9
o Matkamatriisi, HLT
o Joukkoliikennematriisi, 3
o From: 1001..1129
o Vehicle_noch: ['d9','d8','d7','d6','d5','d4','d3','d2','d1','c9','c8','c7','c6','c5','c4','c3','c2','c1','Noch']
* Kuka otti ajaakseen: Olli
* Milloin ajo kŠynnistettiin: 21.8.2006 16.20
* Milloin ajo valmistui: 22.8.2006 10.40 Tulokset
* HuomautuksiaTable(Input_var1,Scen_1)(
1,0.9,0.8,0.7,0.6,0.5,0.4,0.3,0.2,0.1,
0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6,
7,7,7,7,7,7,7,7,7,7,
0,0,0,0,0,0,0,0,0,0,
2,2,2,2,2,2,2,2,2,2,
0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,
8,8,8,8,8,8,8,8,8,8,
4,4,4,4,4,4,4,4,4,4,
2,2,2,2,2,2,2,2,2,2,
8,8,8,8,8,8,8,8,8,8
)['Composite fraction','Guarantee level','Lim']56,88,148,122,319,378,927,328,0,MIDM2,45,69,655,554,0,MIDM52425,39321,65535[Scen_1,Input_var1]Ajo 12Table(Length1,Vehicle_type1,Zone1,Parameter1,Scen_1,Period1)(
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
10.238K,941,270,
10.458K,975,271,
10.2K,1009,267,
10.126K,943,281,
10.205K,972,283,
10.224K,928,294,
10.398K,945,295,
10.188K,972,219,
10.32K,933,267,
10.16K,912,269,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
866.686K,120.931K,92.592K,
859.422K,119.491K,90.194K,
876.603K,121.505K,89.978K,
873.068K,121.487K,86.72K,
867.993K,122.141K,92.592K,
865.545K,121.312K,92.328K,
875.472K,119.415K,92.09K,
866.959K,120.269K,90.512K,
870.184K,118.853K,92.328K,
865.613K,121.059K,92.322K,
0,0,2358.967,
0,0,2329.267,
0,0,2379.667,
0,0,2422.333,
0,0,2341.667,
0,0,2316.633,
0,0,2364.3,
0,0,2367.267,
0,0,2304.5,
0,0,2353.633,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
27.342K,2509,455,
27.297K,2402,455,
27.472K,2526,427,
27.701K,2514,456,
27.285K,2477,450,
27.422K,2557,429,
27.094K,2582,420,
27.439K,2588,458,
27.603K,2485,457,
27.175K,2540,435,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
102.644K,8475,1938,
102.217K,8551,1916,
101.98K,8404,1908,
102.519K,8557,1844,
102.029K,8704,1836,
102.641K,8526,1848,
101.864K,8308,1940,
101.861K,8420,1881,
102.477K,8406,1867,
102.092K,8625,1958,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
7532,1040,467,
13.455K,1473,530,
19.249K,2181,609,
23.483K,2875,767,
26.781K,3382,880,
29.39K,3917,1051,
31.196K,4292,1035,
33.113K,4547,1318,
34.7K,5060,1344,
35.8K,5357,1376,
0,0,1,
1039,99,23,
2160,189,33,
3348,310,56,
4356,391,76,
5536,522,106,
6616,599,113,
7645,665,131,
8905,839,158,
9927,895,164,
0,0,0,
269,18,0,
1075,42,0,
2286,108,0,
3227,167,4,
4541,280,21,
5575,315,13,
6656,372,29,
7888,575,23,
8968,594,23,
2372.6,340,155.1,
4032.2,458.9,177.8,
5758,670.4,204.6,
7060.3,876.7,250.5,
8001.4,1027.2,283.2,
8769.5,1191.3,339.9,
9331.4,1288.7,332.6,
9870.5,1365.7,423.4,
10.347K,1536.9,424,
10.6059K,1618.3,433.8,
276,138.655,3859,
313,175.69,4986,
478,219.69,6343,
527,261.517,7680,
566,305.552,8965,
607,341.586,10.303K,
675,388.414,11.484K,
751,419.069,12.822K,
795,473.828,13.899K,
916,512.31,15.146K,
9.253,9.085,9.104,
9.443,9.142,9.047,
9.38,9.271,9.081,
9.197,9.266,9.161,
9.112,9.263,9.126,
8.941,9.166,9.059,
8.813,9.201,9.163,
8.707,9.193,9.152,
8.561,9.034,9.171,
8.437,9.067,9.229,
24.723K,2499,506,
30.726K,3198,517,
38.53K,4010,652,
46.171K,4831,837,
53.553K,5509,973,
60.404K,6444,1146,
67.021K,7295,1072,
73.312K,7868,1314,
78.953K,8698,1499,
84.509K,9325,1548,
6659,677,467,
11.275K,1072,569,
15.835K,1504,654,
20.361K,1907,738,
24.844K,2277,802,
29.648K,2777,937,
34.013K,3230,900,
38.746K,3539,1031,
43.419K,3958,1176,
47.75K,4343,1235,
2561,130,37,
3250,118,30,
5053,195,45,
7635,285,33,
10.723K,388,67,
14.566K,630,101,
18.48K,829,46,
22.44K,996,104,
27.261K,1284,116,
31.241K,1473,141,
9518.9,969.6,207.9,
11.6224K,1203.4,206.5,
14.8679K,1545.7,262.8,
17.9858K,1862.3,334.6,
21.1238K,2099.8,388.4,
24.1489K,2497,478.5,
26.9743K,2856.2,407.8,
29.7049K,3082,532.3,
32.118K,3446.9,592.9,
34.5905K,3646.1,626.5,
616,0,0,
766,0,0,
1124,0,0,
1167,0,0,
1574,0,0,
1735,0,0,
2023,0,0,
2116,0,0,
2292,0,0,
2599,0,0,
8.704,8.813,8.395,
8.768,8.974,8.569,
8.629,8.935,8.456,
8.456,8.895,8.678,
8.249,8.89,8.547,
8.012,8.725,8.393,
7.804,8.653,8.726,
7.62,8.571,8.47,
7.379,8.46,8.506,
7.233,8.425,8.469,
123.007K,13.064K,1763,
179.167K,17.812K,2423,
233.506K,23.338K,2796,
278.86K,28.461K,3619,
324.637K,33.32K,4054,
368.318K,38.792K,4856,
408.689K,43.75K,5476,
447.323K,48.264K,6364,
482.644K,53.037K,7003,
519.728K,57.987K,7763,
42.81K,5083,1962,
64.204K,6854,2432,
85.973K,8872,2729,
107.4K,10.899K,3184,
128.714K,12.729K,3574,
150.902K,14.745K,3974,
172.374K,16.778K,4402,
194.045K,18.566K,4714,
215.536K,20.574K,5134,
236.882K,22.528K,5411,
10.673K,480,75,
13.878K,589,106,
20.118K,859,108,
29.289K,1190,122,
41.023K,1611,158,
54.913K,2208,189,
70.024K,2936,257,
86.022K,3629,256,
103.055K,4473,298,
119.853K,5478,351,
57.0672K,6037,836.7,
81.446K,8228.2,1184,
105.3992K,10.7009K,1353.9,
124.7072K,12.661K,1721.1,
145.9076K,14.8939K,1917.9,
166.0126K,17.2816K,2298.1,
185.279K,19.4521K,2573.6,
203.2211K,21.4156K,2980.1,
220.0669K,23.4636K,3235.7,
237.2591K,25.8243K,3654.3,
3744,0,0,
4752,0,0,
5738,0,0,
6918,0,0,
7920,0,0,
8943,0,0,
9938,0,0,
11.377K,0,0,
12.099K,0,0,
13.026K,0,0,
8.613,8.771,8.585,
8.725,8.83,8.561,
8.734,8.842,8.612,
8.667,8.875,8.672,
8.565,8.854,8.658,
8.452,8.834,8.673,
8.33,8.798,8.643,
8.217,8.77,8.696,
8.095,8.737,8.699,
8.002,8.685,8.687,
1277,289,360,
1360,299,291,
1420,341,386,
1368,347,372,
1295,345,373,
1332,320,407,
1336,370,419,
1404,422,415,
1380,424,436,
1408,435,491,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
1194.6,325.2,382.3,
1293.8,311.9,359,
1295.1,329.2,418.3,
1188.4,362.3,435.4,
1176.1,326.5,426.7,
1218.2,340,432.7,
1193,352,464.7,
1263.9,390,468.6,
1212.5,392.1,456.4,
1244.2,395,488.1,
215,137.793,3558,
323,140.345,4035,
316,139.414,4064,
347,139.069,4088,
268,128.862,4112,
272,130.034,3989,
173,128.586,3980,
243,130.138,3962,
282,131.448,3979,
272,124.207,4084,
8.009,7.846,7.889,
8.006,7.921,7.739,
8.081,8.009,7.846,
8.125,7.883,7.783,
8.072,7.98,7.782,
8.11,7.918,7.84,
8.121,8.02,7.823,
8.099,8.063,7.785,
8.176,8.032,7.853,
8.159,8.078,7.901,
6437,1221,900,
6588,1212,964,
7127,1384,1056,
7757,1451,1101,
7827,1603,1153,
8302,1629,1177,
8061,1708,1221,
8403,1737,1273,
8420,1664,1330,
8482,1867,1377,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
8421.6,1875.8,1555.7,
9159.1,1854.6,1529.4,
9435,1982.6,1672.8,
10.0007K,2146.6,1775.3,
9784.9,2298.2,1857.9,
10.1749K,2252.5,1863.3,
9888.5,2280.9,2018,
10.0376K,2377.8,2009.2,
9982.8,2292.8,2092.8,
9971.5,2570.7,2151.5,
606,0,0,
622,0,0,
521,0,0,
623,0,0,
641,0,0,
593,0,0,
537,0,0,
706,0,0,
576,0,0,
634,0,0,
7.882,7.734,7.588,
7.82,7.721,7.663,
7.861,7.774,7.65,
7.894,7.757,7.653,
7.916,7.785,7.644,
7.94,7.81,7.654,
7.941,7.852,7.621,
7.973,7.823,7.666,
7.99,7.81,7.669,
7.99,7.843,7.666,
32.501K,7692,4986,
39.541K,9255,5827,
43.962K,10.106K,6456,
45.358K,10.532K,6652,
46.538K,10.827K,7223,
47.882K,11.269K,7669,
48.433K,11.662K,8220,
48.954K,11.832K,8424,
49.293K,12.261K,9011,
48.486K,12.185K,9363,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
49.3478K,12.7759K,10.1839K,
61.4449K,15.206K,11.8178K,
66.5576K,16.0622K,12.8371K,
67.4845K,16.7386K,12.9923K,
67.8234K,16.909K,13.95K,
69.0157K,17.3698K,14.6677K,
69.4119K,17.7163K,15.5106K,
69.6665K,18.1134K,15.8929K,
69.4819K,18.5888K,16.7645K,
67.7462K,18.2563K,17.0871K,
3864,0,0,
4163,0,0,
4340,0,0,
4241,0,0,
4210,0,0,
4159,0,0,
4350,0,0,
4088,0,0,
4289,0,0,
4481,0,0,
7.913,7.801,7.558,
7.891,7.812,7.569,
7.923,7.839,7.588,
7.938,7.846,7.599,
7.958,7.861,7.605,
7.974,7.875,7.621,
7.985,7.896,7.634,
7.998,7.892,7.638,
8.011,7.902,7.656,
8.024,7.915,7.676,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
11.069K,941,215,
9834,899,157,
8725,754,152,
7701,702,133,
6679,598,102,
5568,505,96,
4306,404,72,
3231,334,60,
2083,225,29,
1090,101,14,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
17.1256K,1579.1,415.9,
15.3099K,1517.8,310.7,
13.6873K,1266.1,286.4,
12.1506K,1214.4,272.6,
10.6139K,1052.1,219.1,
9006.1,908.2,195.7,
7090.1,751.1,152.5,
5497.9,624.7,132,
3664,430.9,63.3,
2068,212.9,29.4,
2708,2372.828,60.692K,
2674,2181.483,55.441K,
2481,1936.379,50.341K,
2300,1739.655,44.287K,
1811,1483.793,39.328K,
1818,1248.724,33.343K,
1387,1007.966,27.716K,
1091,758.069,22.22K,
640,526.448,15.614K,
485,261.931,8902,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
45.618K,4172,792,
41.301K,3654,753,
36.831K,3248,675,
32.151K,2774,547,
27.481K,2521,510,
22.743K,2022,414,
18.35K,1602,325,
13.87K,1231,241,
9006,869,188,
4435,418,88,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
105.2669K,10.6975K,2272.3,
96.1098K,9583.2,2129.3,
87.1552K,8526.7,1985.5,
77.1227K,7395.2,1619.2,
66.8191K,6753.1,1508.1,
56.3897K,5613.9,1208.9,
46.7501K,4469.1,942,
36.349K,3488.5,729.8,
24.5038K,2501.1,536.1,
12.7677K,1216.2,262.6,
11.212K,0,0,
10.448K,0,0,
9577,0,0,
8272,0,0,
7646,0,0,
6221,0,0,
5056,0,0,
4508,0,0,
3101,0,0,
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217.163K,19.385K,3799,
195.096K,17.48K,3440,
173.413K,15.292K,3160,
151.453K,13.508K,2774,
130.153K,11.71K,2365,
108.48K,9745,1914,
86.382K,7719,1498,
64.523K,5857,1160,
42.955K,3783,806,
21.807K,1892,400,
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604.2103K,59.4905K,12.757K,
548.4747K,54.0777K,11.6159K,
494.3745K,47.6711K,10.6694K,
437.5236K,42.5243K,9278.8,
382.1598K,37.5434K,8002.3,
323.1377K,31.5399K,6504.1,
263.7998K,25.3687K,5118,
202.6437K,19.3321K,3941.3,
138.9549K,12.818K,2769.1,
72.8766K,6363.3,1388.3,
51.473K,0,0,
47.01K,0,0,
43.184K,0,0,
37.633K,0,0,
34.602K,0,0,
29.465K,0,0,
24.835K,0,0,
20.478K,0,0,
14.556K,0,0,
8710,0,0,
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180.449K,13.827K,2408,
180.557K,13.979K,2425,
180.318K,13.805K,2434,
180.728K,13.844K,2385,
179.974K,13.761K,2417,
179.675K,13.741K,2527,
179.858K,13.798K,2499,
180.377K,13.767K,2366,
179.522K,13.541K,2419,
179.376K,13.692K,2459,
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2562,184,28,
6231,506,75,
9026,851,94,
11.83K,1125,122,
14.194K,1282,136,
16.727K,1573,225,
19.55K,1749,282,
21.913K,1987,332,
24.878K,2334,331,
27.427K,2550,437,
4838,494,222,
7961,744,261,
11.003K,1091,323,
14.222K,1367,357,
17.308K,1615,446,
20.472K,1873,491,
23.829K,2155,560,
26.792K,2399,584,
30.05K,2710,593,
32.98K,3106,710,
1498,73,12,
2103,94,22,
3612,161,21,
5861,266,13,
8171,330,25,
10.614K,437,19,
13.319K,596,24,
15.669K,752,67,
18.42K,988,57,
21.189K,1214,102,
3393,189.1,34.5,
7242.1,523.4,79.8,
10.9692K,866.2,107.1,
15.4345K,1157.2,127.1,
18.969K,1398.4,140.8,
23.1825K,1748.1,222.6,
27.6825K,2070.2,291.1,
31.9092K,2323.9,349.8,
37.0028K,2777.4,354.5,
41.2768K,3292.1,509,
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5.275,6.447,6.004,
7.005,7.732,7.023,
6.66,7.918,7.373,
6.068,7.614,8.126,
5.572,7.458,7.754,
5.192,7.408,8.358,
4.927,6.954,8.405,
4.72,6.789,7.637,
4.578,6.49,7.807,
4.402,6.165,7.474,
214.804K,18.4K,1983,
325.865K,27.211K,2677,
436.43K,37.206K,3395,
554.756K,48.049K,4437,
663.955K,58.588K,5552,
767.436K,68.98K,6694,
869.435K,79.345K,7865,
973.014K,89.437K,8827,
1.06976M,99.918K,10.27K,
1.165556M,108.808K,11.225K,
215.702K,23.434K,8038,
299.38K,30.849K,9750,
383.968K,38.471K,11.232K,
468.175K,45.924K,12.779K,
553.781K,53.482K,14.256K,
638.928K,60.997K,15.872K,
723.325K,68.58K,17.414K,
808.927K,76.244K,18.995K,
892.25K,83.996K,20.527K,
977.334K,91.35K,22.006K,
36.876K,1253,124,
49.275K,1726,147,
72.37K,2476,218,
104.796K,3667,254,
146.029K,5246,337,
192.633K,6970,387,
243.73K,8945,549,
297.848K,11.751K,621,
356.92K,14.413K,782,
418.429K,17.524K,825,
308.5257K,24.67K,2654,
459.3502K,36.537K,3508.2,
620.5299K,50.0899K,4526.6,
793.4565K,64.9841K,5986.7,
963.895K,79.4369K,7507.4,
1.1290902M,94.4698K,8981.9,
1.2934883M,109.32K,10.7892K,
1.4609721M,124.4968K,12.0023K,
1.6186564M,139.7323K,13.7547K,
1.7792322M,152.7263K,15.1196K,
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8.155,8.585,8.462,
8.113,8.606,8.4,
8.017,8.579,8.466,
7.841,8.533,8.461,
7.667,8.479,8.507,
7.496,8.43,8.414,
7.348,8.328,8.442,
7.184,8.264,8.418,
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1221,189,120,
1529,245,171,
1731,332,169,
1835,350,206,
2023,331,259,
1959,373,252,
2062,381,270,
2098,408,264,
1984,422,300,
2131,402,315,
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6977.5,1181.6,632.1,
9071.3,1485.9,865.8,
10.0089K,1836.1,882.2,
10.1412K,1962.1,1046.2,
10.9234K,2058.7,1147.3,
10.8891K,2052.5,1262.1,
10.7514K,2202.8,1393.1,
11.1017K,2225.5,1294.4,
10.5992K,2380.2,1443.1,
10.8814K,2277.5,1455.6,
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7.737,7.527,7.567,
7.772,7.656,7.584,
7.811,7.708,7.608,
7.888,7.77,7.587,
7.924,7.68,7.678,
7.881,7.795,7.674,
7.948,7.784,7.65,
7.944,7.799,7.65,
7.928,7.85,7.711,
7.981,7.835,7.714,
76.746K,15.175K,7956,
91.297K,18.683K,9613,
97.963K,20.737K,10.856K,
97.834K,21.869K,12.059K,
98.739K,23.028K,13.152K,
101.696K,23.626K,14.195K,
102.927K,24.454K,15.1K,
103.271K,25.358K,16.113K,
104.05K,25.277K,16.758K,
105.97K,26.207K,17.673K,
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400.4095K,83.1379K,51.641K,
451.9354K,98.7945K,61.0723K,
471.6822K,108.0748K,67.8285K,
455.4915K,108.4555K,73.3601K,
456.2939K,113.4704K,79.0363K,
462.7706K,114.9179K,84.0429K,
464.7038K,117.5628K,88.5195K,
465.0426K,119.8555K,94.564K,
469.442K,120.7607K,97.7124K,
476.4203K,124.6996K,102.169K,
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7.845,7.737,7.453,
7.871,7.753,7.493,
7.901,7.78,7.51,
7.937,7.821,7.542,
7.955,7.833,7.555,
7.975,7.848,7.575,
7.989,7.868,7.587,
7.998,7.886,7.595,
8.004,7.886,7.597,
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31.213K,2826,582,
28.269K,2617,472,
25.436K,2203,439,
22.046K,1986,369,
18.731K,1654,369,
15.589K,1330,262,
12.522K,1091,211,
9343,843,163,
6195,566,108,
3139,311,60,
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267.1311K,26.6437K,6117.4,
245.604K,24.7164K,4898.1,
223.3041K,20.9553K,4665.9,
196.8619K,19.1741K,3962.1,
168.2475K,16.5698K,3885.1,
143.5094K,13.184K,2895.3,
116.8468K,11.0503K,2406.8,
89.0923K,8478.4,1771.6,
61.1751K,6060.7,1091,
32.7067K,3320.5,698.3,
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847.82K,76.053K,15.062K,
764K,68.307K,13.429K,
678.208K,60.907K,12.142K,
593.114K,52.949K,10.941K,
508.588K,45.697K,9208,
423.159K,38.037K,7555,
338.293K,30.451K,6117,
254.873K,22.779K,4468,
169.453K,15.159K,3048,
84.555K,7556,1525,
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8.7504269M,849.8962K,179.4961K,
7.9636373M,767.9223K,159.7848K,
7.1326335M,688.8689K,145.6666K,
6.3168282M,603.7318K,130.8659K,
5.4879575M,524.3988K,110.4937K,
4.6356773M,442.6312K,91.0669K,
3.76391M,356.925K,72.6684K,
2.9010842M,270.3288K,54.3191K,
1.9666177M,182.0755K,36.9318K,
1.0085869M,91.3888K,18.5982K,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN'
)168,64,148,122,104,114,820,303,0,MIDM2,35,151,710,303,0,MIDM65535,52427,65534[Scen_1,Period1][Scen_1,Parameter1][Length1,1,Vehicle_type1,3,Zone1,1,Period1,1,Output11,1,Scen_1,1]Ajo 12 descAjo 12
* Kuvaus: KakkoskŠsikirjoitukseen tulevia tuloksia
* KŠytetyt oletukset
o Malliversio 1.7.8
o Scenarios, taulu 8
o Matkamatriisi, HLT
o Joukkoliikennematriisi, 3
o From: 1001..1129
o Vehicle_noch: ['d9','d8','d7','d6','d5','d4','d3','d2','d1','c9','c8','c7','c6','c5','c4','c3','c2','c1','Noch']
* Kuka otti ajaakseen: Juha
* Milloin ajo kŠynnistettiin: 21.08.2006 KLO 15:35
* Milloin ajo valmistui: 22.08.2006 KLO 14:30 Tulokset
* HuomautuksiaTable(Input_var1,Scen_1)(
1,0.9,0.8,0.7,0.6,0.5,0.4,0.3,0.2,0.1,
1,1,1,1,1,1,1,1,1,1,
7,7,7,7,7,7,7,7,7,7,
0,0,0,0,0,0,0,0,0,0,
2,2,2,2,2,2,2,2,2,2,
0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,
8,8,8,8,8,8,8,8,8,8,
4,4,4,4,4,4,4,4,4,4,
2,2,2,2,2,2,2,2,2,2,
8,8,8,8,8,8,8,8,8,8
)['Composite fraction','Guarantee level','Lim']56,64,148,122,646,113,476,2242,319,378,927,328,0,MIDM2,45,69,655,554,0,MIDM52425,39321,65535[Scen_1,Input_var1]Ajo 16Table(Length1,Vehicle_type1,Zone1,Parameter1,Scen_1,Period1)(
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
10.238K,941,270,
6287,555,164,
6094,608,155,
6032,569,172,
6114,596,179,
6132,551,192,
6252,568,169,
6139,563,136,
6211,562,169,
6146,543,162,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
866.686K,120.931K,92.592K,
709.278K,108.779K,86.45K,
701.572K,108.247K,79.696K,
709.032K,108.343K,78.616K,
711.837K,109.122K,81.614K,
712.292K,108.095K,81.732K,
710.091K,110.946K,79.994K,
707.156K,107.403K,79.19K,
704.278K,107.969K,88.146K,
712.39K,108.093K,82.458K,
0,0,2358.967,
0,0,1865.3,
0,0,1841.233,
0,0,1872.667,
0,0,1882.1,
0,0,1882.267,
0,0,1857.667,
0,0,1852.233,
0,0,1851.133,
0,0,1859.267,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
27.342K,2509,455,
16.416K,1468,278,
16.563K,1522,267,
16.614K,1521,282,
16.4K,1498,273,
16.477K,1555,276,
16.273K,1526,252,
16.523K,1536,270,
16.502K,1468,262,
16.346K,1507,243,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
102.644K,8475,1938,
61.428K,5133,1186,
61.384K,5023,1124,
61.385K,5224,1089,
61.232K,5182,1052,
61.698K,5202,1131,
61.556K,5029,1126,
61.341K,5104,1135,
61.621K,4988,1151,
61.261K,5150,1210,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
7532,1040,467,
38.037K,5586,1430,
37.684K,5406,1423,
38.362K,5554,1461,
38.26K,5332,1462,
38.075K,5339,1507,
38.083K,5479,1481,
37.892K,5425,1525,
37.883K,5391,1523,
37.643K,5326,1508,
0,0,1,
8469,811,188,
8441,782,176,
8558,779,183,
8447,767,180,
8574,764,176,
8452,781,198,
8348,832,163,
8359,791,171,
8382,773,172,
0,0,0,
7377,529,56,
7421,532,27,
7587,496,18,
7418,514,32,
7559,510,24,
7414,510,40,
7327,591,12,
7339,504,29,
7361,505,32,
2372.6,340,155.1,
11.2K,1671.5,440.1,
11.1355K,1625.7,446.1,
11.3437K,1664.9,450.3,
11.2895K,1604.3,449.4,
11.2093K,1604.5,473.4,
11.2266K,1643,458.8,
11.2318K,1635,479.1,
11.2093K,1628.4,480.9,
11.0848K,1607.5,465,
276,138.655,3859,
659,350.552,10.186K,
733,351.379,10.279K,
774,359.897,10.194K,
807,355.138,10.225K,
804,351.034,10.289K,
764,351.655,10.193K,
766,349.414,10.273K,
648,349.655,10.299K,
706,352.517,10.264K,
9.253,9.085,9.104,
Null,Null,Null,
Null,Null,7.692,
7.624,8.047,8.117,
7.998,8.364,8.366,
8.248,8.616,8.65,
8.499,8.871,8.841,
8.674,8.958,9.084,
8.845,9.188,9.148,
8.961,9.323,9.336,
24.723K,2499,506,
74.237K,7804,1321,
73.97K,8129,1273,
74.211K,8117,1437,
73.587K,7910,1432,
74.237K,7959,1419,
74.606K,8056,1267,
74.549K,7885,1300,
74.028K,8011,1447,
73.8K,8088,1357,
6659,677,467,
35.939K,3198,982,
35.912K,3353,990,
35.923K,3313,1000,
35.729K,3256,979,
35.924K,3336,1001,
35.797K,3329,924,
36.06K,3323,973,
36.053K,3406,1013,
35.613K,3389,1039,
2561,130,37,
20.47K,813,77,
20.459K,837,60,
20.509K,846,42,
20.147K,807,84,
20.35K,929,75,
20.191K,829,73,
20.563K,849,69,
20.778K,913,70,
20.054K,947,58,
9518.9,969.6,207.9,
29.7674K,3007.7,516,
29.6775K,3140.2,489.3,
29.6813K,3163.7,547.7,
29.3824K,3061.8,553,
29.6134K,3135.4,565,
29.8231K,3128.5,495.4,
29.8634K,3057.6,501.8,
29.6227K,3104.1,554.9,
29.4898K,3116.8,512.8,
616,0,0,
1671,0,0,
1925,0,0,
1663,0,0,
1694,0,0,
1802,0,0,
1668,0,0,
1804,0,0,
1707,0,0,
1851,0,0,
8.704,8.813,8.395,
Null,Null,Null,
Null,Null,7.346,
6.748,7.633,7.764,
7.08,7.98,7.892,
7.34,8.112,8.137,
7.573,8.452,8.358,
7.707,8.581,8.52,
7.831,8.712,8.704,
8.022,8.813,8.893,
123.007K,13.064K,1763,
396.107K,40.507K,4950,
396.914K,40.462K,4706,
397.208K,40.607K,5047,
395.715K,40.634K,4714,
397.678K,40.684K,4802,
395.69K,40.099K,4873,
396.246K,40.255K,4851,
394.983K,40.734K,5001,
396.804K,40.409K,4725,
42.81K,5083,1962,
169.959K,16.113K,4338,
170.127K,16.036K,4292,
170.296K,16.163K,4299,
169.511K,16.251K,4358,
170.003K,16.165K,4247,
169.195K,16.067K,4381,
168.8K,16.012K,4244,
168.922K,16.11K,4364,
169.715K,16.143K,4243,
10.673K,480,75,
67.61K,2590,209,
67.712K,2503,190,
67.443K,2673,177,
67.084K,2633,192,
67.694K,2690,162,
66.785K,2619,219,
66.028K,2599,242,
66.364K,2581,190,
67.347K,2652,142,
57.0672K,6037,836.7,
178.3393K,17.8941K,2367,
178.6432K,17.8273K,2213,
178.7269K,17.9249K,2414.9,
178.1989K,17.9336K,2230.2,
179.0431K,17.9945K,2254,
178.2085K,17.772K,2282.7,
178.0327K,17.7845K,2309,
177.6432K,17.991K,2330.1,
178.4961K,17.6807K,2226.3,
3744,0,0,
8664,0,0,
8695,0,0,
8785,0,0,
8831,0,0,
8896,0,0,
8776,0,0,
8883,0,0,
9099,0,0,
8890,0,0,
8.613,8.771,8.585,
Null,Null,Null,
Null,Null,7.325,
7.206,7.682,7.678,
7.532,8.008,7.93,
7.788,8.246,8.198,
8.013,8.466,8.33,
8.188,8.622,8.448,
8.341,8.797,8.67,
8.459,8.926,8.84,
1277,289,360,
1354,406,424,
1405,356,460,
1325,401,459,
1422,426,449,
1453,388,452,
1426,350,471,
1325,393,424,
1389,390,435,
1296,391,458,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
1194.6,325.2,382.3,
1237.6,374.4,442,
1229.3,341.9,483.9,
1179.9,355.5,479.6,
1258.6,386.3,475.5,
1303.5,367.7,456.1,
1271.4,323.4,478.4,
1189.9,373.5,454.4,
1228.5,342.5,467,
1225.3,359.7,481.8,
215,137.793,3558,
247,137.862,4232,
222,142.483,4271,
251,146.828,4221,
331,145.966,4263,
261,141.345,4180,
221,145.862,4224,
274,137.241,4115,
271,145.241,4216,
278,147.345,4163,
8.009,7.846,7.889,
Null,Null,Null,
Null,Null,7.328,
7.77,7.679,7.501,
7.931,7.751,7.632,
7.973,7.874,7.755,
8.051,7.937,7.81,
8.085,7.964,7.803,
8.142,8.086,7.845,
8.107,8.095,7.917,
6437,1221,900,
8184,1734,1240,
8290,1724,1367,
8330,1666,1294,
8504,1736,1283,
8243,1680,1277,
8045,1761,1289,
8240,1796,1341,
8439,1832,1249,
8462,1732,1314,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
8421.6,1875.8,1555.7,
9895,2429.8,1887.6,
10.0897K,2430.7,2066.5,
10.2914K,2338.6,2060.6,
10.1576K,2406.6,2062.3,
10.0135K,2329,1979.2,
9917.8,2336.2,1993.7,
10.1227K,2398.8,2060.3,
10.2323K,2471.3,1976.8,
10.1392K,2394.7,2040.4,
606,0,0,
726,0,0,
566,0,0,
605,0,0,
539,0,0,
633,0,0,
668,0,0,
536,0,0,
662,0,0,
564,0,0,
7.882,7.734,7.588,
Null,Null,Null,
Null,Null,7.257,
7.581,7.474,7.376,
7.72,7.596,7.467,
7.795,7.659,7.559,
7.858,7.766,7.605,
7.909,7.814,7.67,
7.97,7.852,7.661,
8.006,7.862,7.709,
32.501K,7692,4986,
47.541K,11.243K,8003,
48.135K,11.277K,8239,
47.62K,11.107K,7819,
47.916K,11.193K,8107,
47.702K,11.124K,8064,
48.099K,11.48K,8051,
47.277K,11.069K,7707,
47.373K,10.918K,8128,
47.224K,11.485K,8259,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
49.3478K,12.7759K,10.1839K,
67.5987K,17.3118K,15.2604K,
68.9786K,17.2569K,15.5424K,
68.3824K,17.1324K,14.9584K,
68.1408K,17.3282K,15.3196K,
68.2067K,17.2172K,15.1967K,
68.3445K,17.4547K,15.2283K,
67.691K,17.0088K,14.6537K,
67.7243K,17.2869K,15.199K,
67.9719K,17.7512K,15.4466K,
3864,0,0,
4278,0,0,
4459,0,0,
4491,0,0,
4495,0,0,
4400,0,0,
4416,0,0,
4269,0,0,
4434,0,0,
4398,0,0,
7.913,7.801,7.558,
Null,Null,Null,
Null,Null,7.234,
7.602,7.512,7.354,
7.733,7.631,7.446,
7.822,7.714,7.513,
7.895,7.791,7.559,
7.939,7.833,7.586,
7.991,7.858,7.646,
8.022,7.916,7.67,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
11.069K,941,215,
6575,607,99,
6550,562,121,
6585,607,115,
6679,598,102,
6622,640,128,
6616,599,113,
6577,576,111,
6738,644,114,
6649,592,113,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
17.1256K,1579.1,415.9,
10.4937K,1069.6,201.8,
10.4965K,998.1,232.9,
10.4967K,1057.9,238.1,
10.6139K,1052.1,219.1,
10.5135K,1112.3,260.3,
10.5337K,1040.7,250.4,
10.5093K,1008.8,229.3,
10.722K,1132.8,230.8,
10.5814K,1045.4,235.6,
2708,2372.828,60.692K,
2004,1487.931,39.091K,
1918,1479.793,39.447K,
2019,1503.897,38.932K,
1811,1483.793,39.328K,
1761,1491.241,38.88K,
1913,1473.621,38.98K,
1964,1448.586,39.163K,
1940,1499.034,38.768K,
2107,1486.034,38.901K,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
45.618K,4172,792,
27.518K,2462,517,
27.663K,2403,499,
27.676K,2361,459,
27.481K,2521,510,
27.412K,2465,503,
27.387K,2559,469,
27.472K,2499,487,
27.473K,2438,546,
27.401K,2405,476,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
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66.5797K,6721.2,1467.4,
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66.9414K,6667.4,1496.4,
66.5091K,6817.6,1383.5,
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66.7025K,6558.1,1575.6,
66.8318K,6479,1414,
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108.299K,8304,1397,
108.278K,8287,1480,
108.571K,8271,1410,
107.767K,8224,1451,
107.986K,8322,1491,
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17.008K,1564,161,
17.067K,1658,186,
17.284K,1589,192,
17.149K,1628,217,
4838,494,222,
20.064K,1816,476,
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5.275,6.447,6.004,
Null,Null,Null,
Null,Null,6.403,
4.347,6.365,7.163,
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5.098,7.125,8.358,
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726.852K,63.337K,6026,
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624.796K,58.699K,15.35K,
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188.624K,6760,343,
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188.84K,6838,306,
186.835K,6464,346,
189.142K,6633,351,
188.949K,6414,349,
187.705K,6509,325,
308.5257K,24.67K,2654,
1.0639621M,85.937K,8051.2,
1.0569599M,85.9184K,7466.5,
1.0598853M,85.2567K,7588.9,
1.062079M,85.0028K,7785.7,
1.0603973M,85.2892K,8018.4,
1.0562634M,85.6547K,8120.8,
1.0682629M,85.6338K,7989.6,
1.0607085M,85.4209K,8361.1,
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7.983,8.537,8.404,
Null,Null,Null,
Null,Null,7.194,
6.511,7.358,7.484,
6.813,7.674,7.745,
7.053,7.888,8.037,
7.28,8.144,8.173,
7.427,8.274,8.321,
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Null,Null,'NAN',
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1221,189,120,
2133,404,255,
2016,410,254,
2091,375,254,
2051,392,297,
1975,394,229,
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11.5573K,2517.4,1303,
11.4513K,2335.2,1441.2,
11.3852K,2349.7,1312.9,
11.4248K,2399.6,1438.9,
11.0793K,2194.4,1328.8,
11.1319K,2288.1,1525.4,
11.3439K,2537,1475,
11.6833K,2260.9,1516.5,
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7.737,7.527,7.567,
Null,Null,Null,
Null,Null,7.232,
7.555,7.446,7.36,
7.682,7.541,7.494,
7.751,7.687,7.485,
7.814,7.691,7.563,
7.859,7.729,7.603,
7.917,7.764,7.622,
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107.512K,24.41K,13.49K,
109.66K,24.759K,13.554K,
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109.315K,25.002K,13.828K,
110.118K,24.621K,13.741K,
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508.318K,45.65K,8972,
508.856K,45.657K,9178,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
8.7504269M,849.8962K,179.4961K,
5.4971351M,524.1576K,107.2983K,
5.4897109M,524.2204K,111.4799K,
5.4852948M,520.555K,113.0743K,
5.4879575M,524.3988K,110.4937K,
5.4874935M,521.9861K,107.5848K,
5.4895708M,524.2651K,109.911K,
5.4919009M,525.8214K,109.9486K,
5.4828862M,526.8071K,107.1254K,
5.4939553M,524.1193K,110.9175K,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN'
)168,160,148,122,104,114,820,303,0,MIDM2,30,434,710,303,0,MIDM65535,52427,65534[Scen_1,Period1][Scen_1,Parameter1]Ajo 16 descAjo 16
* Kuvaus: KakkoskŠsikirjoitukseen tulevia tuloksia
* KŠytetyt oletukset
o Malliversio 1.7.8
o Scenarios, taulu 12
o Matkamatriisi, HLT
o Joukkoliikennematriisi, 3
o From: 1001..1129
o Vehicle_noch: ['d9','d8','d7','d6','d5','d4','d3','d2','d1','c9','c8','c7','c6','c5','c4','c3','c2','c1','Noch']
* Kuka otti ajaakseen: Olli
* Milloin ajo kŠynnistettiin: 20060822 16.20
* Milloin ajo valmistui: 20060823 11.03 Tulokset
* HuomautuksiaTable(Input_var1,Scen_1)(
1,0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6,
1,0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6,
7,7,7,7,7,7,7,7,7,7,
0,0,0,0,0,0,0,0,0,0,
2,2,2,2,2,2,2,2,2,2,
0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,
8,8,8,8,8,8,8,8,8,8,
4,4,4,4,4,4,4,4,4,4,
2,2,2,2,2,2,2,2,2,2,
8,1,2,3,4,5,6,7,8,9
)['Composite fraction','Guarantee level','Lim']56,160,148,122,319,378,927,328,0,MIDM2,45,69,655,554,0,MIDM52425,39321,65535[Scen_1,Input_var1]Ajo 17Table(Length1,Vehicle_type1,Zone1,Parameter1,Scen_1,Period1)(
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
1,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
19.269K,2668,8714,
6352,2437,5314,
13.16K,2634,5662,
15.474K,1474,5594,
9690,1432,5576,
12.39K,3611,5754,
10.84K,764,2406,
13.251K,2111,2426,
9742,2535,6146,
11.096K,1600,4594,
0,'NAN',63.067,
0,'NAN',37.833,
0,'NAN',60.133,
0,'NAN',49.833,
0,'NAN',42.033,
0,'NAN',48.167,
0,'NAN',33.967,
0,'NAN',40.367,
0,'NAN',38.033,
0,'NAN',48.4,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
1,0,0,
0,0,0,
0,0,0,
1,0,0,
0,0,0,
0,0,0,
0,0,1,
1,0,1,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
6,2,4,
1,2,1,
0,0,0,
6,0,2,
4,1,1,
6,3,3,
3,1,1,
3,1,1,
5,3,2,
2,2,3,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
0,0,0,
4696,236,16,
10.456K,724,160,
15.148K,1360,360,
18.924K,1888,656,
22.636K,2272,948,
25.372K,2860,1144,
28.168K,3200,1544,
30.74K,3720,1704,
32.624K,4088,2000,
0,0,1,
777,70,49,
1597,132,64,
2470,226,110,
3200,285,148,
4078,392,206,
4885,433,246,
5667,494,253,
6587,613,300,
7322,650,337,
0,0,0,
120,4,0,
544,12,0,
1228,40,0,
1828,72,8,
2660,120,28,
3332,132,16,
4096,168,32,
4980,304,40,
5752,280,40,
0,0,0,
1355.5,67.4,5.6,
3069.3,210.2,44.8,
4520.6,395.6,101.2,
5638.5,550.5,187,
6764.6,678.9,275.6,
7584.5,841.9,328.5,
8409.6,951.1,444.7,
9137.2,1104.6,487,
9686.9,1205.9,580.7,
0,'NAN',0,
136,'NAN',1017,
334,'NAN',2830,
535,'NAN',4517,
731,'NAN',6298,
868,'NAN',8143,
1185,'NAN',9959,
1226,'NAN',11.751K,
1443,'NAN',12.951K,
1632,'NAN',14.876K,
'NAN','NAN','NAN',
9.338,8.982,9.3,
9.397,9.302,8.996,
9.313,9.345,9.196,
9.291,9.356,9.175,
9.214,9.325,9.146,
9.169,9.422,9.274,
9.109,9.39,9.318,
9.044,9.262,9.319,
8.98,9.386,9.393,
0,0,0,
4176,88,0,
11.928K,516,32,
20.092K,1116,80,
27.932K,1736,312,
35.268K,2512,528,
42.38K,3348,752,
49.1K,4024,1044,
55.644K,4644,1292,
61.716K,5240,1712,
1,3,0,
3890,340,188,
7785,675,388,
11.58K,1058,548,
15.467K,1358,749,
19.502K,1710,985,
23.424K,2153,1131,
27.462K,2459,1342,
31.38K,2773,1576,
35.161K,3029,1783,
0,0,0,
96,0,0,
784,12,0,
2060,36,0,
4060,64,8,
6188,120,0,
9004,212,16,
11.556K,312,36,
14.9K,428,52,
17.764K,488,72,
0,0,0,
1413.8,28.1,0,
4312.3,172.5,9,
7529.3,397.2,21.1,
10.7751K,606.5,99.4,
13.9095K,904.3,165.7,
16.9925K,1231.6,242.1,
19.8648K,1484.8,346.9,
22.7346K,1729.6,427.8,
25.3977K,1965.5,587.9,
0,0,0,
223,0,0,
612,0,0,
925,0,0,
1241,0,0,
1627,0,0,
2015,0,0,
2306,0,0,
2543,0,0,
3138,0,0,
'NAN','NAN','NAN',
9.062,8.979,'NAN',
8.967,9.029,9.068,
8.824,9.018,9.003,
8.616,9.051,8.914,
8.463,9.03,9.094,
8.251,8.978,8.996,
8.132,8.912,8.92,
7.939,8.881,8.903,
7.828,8.884,8.963,
0,0,0,
21.584K,496,32,
69.8K,2556,200,
121.432K,5744,796,
173.936K,9748,1480,
225.128K,14.308K,2620,
271.444K,19.06K,4000,
317.728K,24.008K,5524,
361.7K,27.84K,7252,
403.376K,33.088K,9248,
26,8,12,
22.641K,1974,1141,
45.645K,3977,2247,
67.856K,6058,3274,
90.311K,7783,4446,
113.587K,9914,5534,
135.338K,11.989K,6702,
158.559K,14.043K,7842,
180.854K,15.788K,8810,
203.574K,17.96K,9999,
0,0,0,
704,8,0,
4024,36,8,
9928,184,20,
18.512K,308,48,
28.84K,628,84,
40.692K,988,120,
53.848K,1488,204,
68.324K,1984,268,
83.504K,2560,384,
0,0,0,
8609.4,191.8,12,
28.8547K,1001.5,83,
51.1103K,2293.2,316.7,
75.1506K,3883.3,583.6,
98.5797K,5803.1,1062.7,
120.5841K,7834.3,1611.3,
142.478K,9925.8,2244.8,
163.7484K,11.5872K,2944.1,
184.3976K,13.888K,3761.6,
0,0,0,
1095,0,0,
2836,0,0,
4484,0,0,
6242,0,0,
8066,0,0,
9898,0,0,
11.802K,0,0,
12.862K,0,0,
14.814K,0,0,
'NAN','NAN','NAN',
9.016,9.017,9.068,
8.973,9.125,8.697,
8.89,9.042,8.878,
8.779,9.083,8.902,
8.682,9.048,8.943,
8.566,9.014,8.991,
8.469,8.982,8.944,
8.36,8.943,8.987,
8.259,8.919,8.962,
4,0,1,
2430,421,357,
2929,591,569,
3202,698,691,
3312,741,747,
3276,808,886,
3468,829,923,
3402,838,988,
3377,826,1002,
3472,920,1077,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
9.6,0,2,
2203.1,415.3,429.1,
2502,560.8,596,
2621.9,655.6,678,
2671.9,661,737.2,
2662.6,688.8,843.7,
2835.7,727,869.7,
2735,710.7,915.6,
2730.8,722.7,895.1,
2744.3,758.3,969.1,
16,'NAN',218,
335,'NAN',3693,
353,'NAN',4059,
428,'NAN',4313,
381,'NAN',4241,
349,'NAN',4250,
374,'NAN',4101,
479,'NAN',4129,
412,'NAN',4100,
369,'NAN',4051,
7,'NAN',7,
8.119,8.023,7.787,
8.194,8.089,7.967,
8.254,8.099,8.005,
8.271,8.135,8.004,
8.292,8.172,8.045,
8.305,8.178,8.051,
8.324,8.217,8.096,
8.321,8.188,8.121,
8.348,8.248,8.13,
8,4,1,
6830,878,521,
9360,1488,1061,
10.573K,1953,1508,
11.727K,2148,1842,
12.859K,2369,2141,
13.196K,2563,2387,
14.31K,2746,2667,
14.418K,2971,2858,
14.906K,3072,3093,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
21.8,13.3,1.6,
8869.5,1309.9,1065.6,
11.5586K,2070,1753.5,
12.8008K,2544.4,2314.5,
13.6598K,2779.1,2592.7,
14.8222K,2929.6,2886,
15.1139K,3152.9,3220.9,
15.8439K,3340.1,3457.7,
15.9973K,3565.5,3663.2,
16.451K,3638.4,3984,
54,0,0,
762,0,0,
727,0,0,
792,0,0,
762,0,0,
878,0,0,
809,0,0,
936,0,0,
848,0,0,
882,0,0,
7,7,7,
7.945,7.754,7.376,
8.001,7.876,7.638,
8.032,7.949,7.751,
8.062,7.956,7.834,
8.087,7.981,7.884,
8.097,8.002,7.886,
8.128,8.018,7.94,
8.133,8.043,7.936,
8.144,8.048,7.957,
137,36,59,
52.146K,6529,3700,
73.983K,11.362K,7189,
85.75K,14.719K,9854,
93.238K,16.883K,12.816K,
98.93K,19.105K,15.336K,
102.955K,20.558K,17.638K,
106.849K,21.753K,19.855K,
108.471K,23.272K,21.201K,
110.879K,24.295K,22.991K,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
396,114.2,184.3,
78.0746K,12.195K,8786.9,
100.9885K,18.0713K,14.7029K,
113.3641K,22.0473K,18.7866K,
120.3675K,24.3573K,22.4211K,
125.9044K,26.8556K,25.4311K,
130.066K,28.1091K,28.1859K,
133.6172K,29.6235K,30.6793K,
134.9101K,31.0318K,32.0755K,
137.0684K,32.017K,33.9438K,
365,0,0,
4461,0,0,
4981,0,0,
5486,0,0,
5434,0,0,
5384,0,0,
5408,0,0,
5306,0,0,
5409,0,0,
5473,0,0,
7.009,7,7.022,
7.943,7.688,7.383,
8.055,7.867,7.576,
8.103,7.937,7.66,
8.137,7.985,7.754,
8.162,8.02,7.821,
8.179,8.048,7.858,
8.198,8.063,7.902,
8.209,8.086,7.924,
8.22,8.105,7.952,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
8183,678,430,
7248,654,331,
6417,536,310,
5697,513,275,
4950,446,206,
4142,348,197,
3185,294,144,
2356,234,120,
1538,157,73,
809,75,35,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
12.8085K,1198.4,803.5,
11.4374K,1154.8,669.3,
10.2982K,954.7,610.3,
9214.1,939,543.7,
8039.8,812.6,430,
6826.1,648.3,406.8,
5402.8,567,310.3,
4143.2,458.4,266.1,
2828.1,307.7,157.2,
1576.7,162.2,72.9,
7244,'NAN',68.38K,
6606,'NAN',63.233K,
6118,'NAN',56.453K,
4896,'NAN',50.08K,
4166,'NAN',44.581K,
4365,'NAN',38K,
3464,'NAN',31.56K,
2461,'NAN',24.484K,
1773,'NAN',17.312K,
841,'NAN',9591,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
39.016K,3511,1902,
35.385K,3087,1715,
31.554K,2738,1550,
27.709K,2341,1356,
23.566K,2099,1142,
19.488K,1724,957,
15.76K,1327,734,
11.87K,1039,535,
7776,745,373,
3772,353,203,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
92.0975K,9435.5,5502.5,
84.4844K,8447.6,4923.7,
76.3031K,7517.2,4564.3,
68.0175K,6510,4013.2,
58.9465K,5851.7,3423.3,
49.7026K,4963.1,2839.1,
41.3678K,3835.3,2180.3,
31.8305K,3042.7,1630.3,
21.6314K,2189.1,1107.4,
11.0302K,1068.1,607.1,
13.903K,0,0,
13.319K,0,0,
12.294K,0,0,
10.866K,0,0,
9712,0,0,
8212,0,0,
6729,0,0,
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226.822K,19.856K,10.976K,
203.828K,17.844K,9948,
181.031K,15.662K,8979,
158.393K,13.714K,7761,
136.076K,11.894K,6716,
113.126K,9978,5495,
90.236K,7818,4421,
67.363K,5957,3274,
44.709K,3907,2301,
22.665K,1925,1149,
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634.5532K,62.3865K,36.8673K,
576.349K,56.6234K,33.5777K,
518.9331K,50.1254K,30.3282K,
460.526K,44.3069K,26.1883K,
402.6305K,39.0065K,22.9309K,
340.0809K,32.9882K,18.8304K,
278.0774K,26.1917K,15.1653K,
213.4045K,20.0904K,11.2442K,
145.9264K,13.4493K,7911.3,
76.3374K,6517.4,3998.4,
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52.487K,0,0,
46.794K,0,0,
40.266K,0,0,
32.464K,0,0,
26.199K,0,0,
18.831K,0,0,
10.604K,0,0,
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1376,40,8,
3508,160,16,
5696,372,56,
7636,552,88,
9760,728,180,
11.872K,904,248,
13.648K,1132,320,
15.668K,1340,436,
17.664K,1448,640,
2,0,1,
2461,201,147,
4968,437,242,
7476,679,363,
9762,873,500,
12.465K,1053,603,
15.045K,1299,714,
17.358K,1490,843,
19.882K,1731,966,
22.316K,2021,1105,
0,0,0,
104,0,0,
668,8,0,
1696,44,0,
2668,64,8,
4144,120,4,
5896,176,16,
7232,236,32,
8980,308,52,
10.72K,392,56,
0,0,0,
1246.8,30.6,5,
3618.6,143.4,12.8,
6716.8,357.7,44.1,
9146,548.2,83.8,
12.5549K,731.7,154.2,
15.9048K,893.8,218.2,
18.7564K,1177.8,290,
22.2824K,1443.1,406.5,
25.7715K,1622.9,588.1,
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8.03,8.8,8.959,
7.392,8.43,8.866,
7.107,8.434,8.417,
6.616,8.196,8.916,
6.175,7.973,8.648,
5.975,7.983,8.489,
5.698,7.849,8.34,
5.504,7.595,8.593,
0,0,0,
28.776K,600,16,
103.252K,3480,192,
193.236K,8228,696,
289.592K,14.484K,1936,
385.808K,21.036K,3316,
482.288K,28.664K,5172,
580.552K,36.46K,6968,
677.264K,45.372K,9648,
775.26K,53.108K,12.068K,
207,62,79,
82.307K,7389,4194,
164.865K,14.561K,8262,
247.184K,21.802K,12.159K,
330.845K,29.041K,16.193K,
412.365K,35.995K,20.219K,
495.034K,43.457K,24.685K,
577.766K,50.661K,28.617K,
659.354K,58.353K,32.407K,
742.406K,65.212K,36.526K,
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1416,8,0,
10.02K,88,8,
26.524K,372,16,
49.808K,844,64,
78.592K,1508,172,
115.232K,2400,228,
153.736K,3564,376,
197.16K,4964,536,
243.58K,6628,708,
0,0,0,
31.699K,577.3,10.6,
124.8657K,3718.5,168.7,
246.2752K,9299,686.6,
383.6554K,16.6002K,1953.5,
527.9755K,24.5603K,3458.5,
677.9784K,34.477K,5501.1,
830.8137K,44.814K,7626.7,
987.7495K,56.3524K,10.5618K,
1.1451849M,67.0935K,13.3232K,
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8.647,8.986,8.71,
8.442,8.914,8.862,
8.249,8.856,8.883,
8.069,8.803,8.771,
7.857,8.754,8.856,
7.706,8.682,8.803,
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1624,240,160,
2081,402,265,
2405,483,404,
2771,499,500,
2948,561,547,
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6707.8,1036.7,934.6,
9031.6,1674.9,1119.7,
10.9537K,1923.1,1726.2,
12.69K,2160.2,1852.9,
13.2226K,2358.1,2188.8,
14.1325K,2595.3,2446.9,
14.4922K,2613.3,2780.7,
15.6722K,3067.4,2844.7,
15.6572K,3054.9,2809,
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8,7.905,7.712,
8.014,7.989,7.825,
8.036,7.965,7.901,
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8.087,8.005,7.947,
8.11,7.99,8.01,
8.111,8.027,7.983,
8.144,8.052,7.957,
324,103,130,
92.708K,11.007K,6047,
137.425K,19.437K,11.753K,
162.999K,25.905K,16.911K,
179.924K,30.831K,21.573K,
192.35K,34.64K,25.918K,
202.098K,38.434K,30.594K,
209.769K,41.257K,34.209K,
214.855K,43.518K,37.298K,
219.47K,45.582K,40.505K,
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3249.8,1023.5,1295.4,
421.897K,62.4063K,44.6658K,
567.2805K,95.531K,73.9453K,
650.4025K,118.1097K,95.5377K,
705.4948K,135.2987K,115.0879K,
745.7603K,148.6669K,131.4507K,
778.1162K,159.8221K,148.5641K,
804.6402K,168.5908K,162.2876K,
821.7222K,176.3391K,173.1046K,
837.0129K,182.6665K,184.9083K,
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7.899,7.631,7.3,
8.029,7.809,7.494,
8.083,7.901,7.636,
8.116,7.948,7.699,
8.143,7.984,7.76,
8.162,8.021,7.815,
8.178,8.041,7.852,
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24.762K,2226,1255,
22.447K,2007,1056,
20.133K,1697,937,
17.48K,1551,814,
14.928K,1302,761,
12.314K,1036,591,
9954,854,464,
7409,661,353,
4861,429,236,
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219.4819K,22.3027K,13.3253K,
201.4193K,19.8445K,11.4297K,
183.3406K,17.0689K,10.0302K,
162.5057K,15.4965K,8902.6,
138.4995K,13.6001K,8470.6,
117.6523K,10.9515K,6538.2,
96.4301K,9116.7,5157.9,
72.9906K,6926.6,3837.3,
49.7293K,4745.3,2540.2,
26.3688K,2650.2,1499.5,
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824.691K,72.335K,40.262K,
742.845K,64.933K,35.94K,
659.988K,58.042K,32.227K,
577.174K,50.3K,28.704K,
494.437K,43.53K,24.417K,
411.309K,36.34K,20.18K,
329.845K,28.936K,16.273K,
247.887K,21.7K,12.04K,
164.582K,14.449K,8029,
82.264K,7222,4115,
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8.2975279M,799.4336K,464.9211K,
7.5456138M,722.3074K,415.4238K,
6.7678881M,649.2187K,374.9703K,
5.9936226M,565.8136K,333.6525K,
5.199925M,494.8317K,285.1762K,
4.3956002M,416.0128K,235.7427K,
3.5800932M,333.17K,188.9586K,
2.7520976M,252.3943K,141.5849K,
1.8659971M,169.3913K,94.2799K,
955.4618K,85.2907K,48.545K,
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'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN'
)168,184,148,122,104,114,820,303,0,MIDM2,30,434,710,303,0,MIDM65535,52427,65534[Scen_1,Period1][Scen_1,Parameter1]Ajo 17 descTable(Input_var1,Scen_1)(
1,0.9,0.8,0.7,0.6,0.5,0.4,0.3,0.2,0.1,
1,1,1,1,1,1,1,1,1,1,
7,7,7,7,7,7,7,7,7,7,
0,0,0,0,0,0,0,0,0,0,
2,2,2,2,2,2,2,2,2,2,
0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,
8,8,8,8,8,8,8,8,8,8,
4,4,4,4,4,4,4,4,4,4,
2,2,2,2,2,2,2,2,2,2,
8,8,8,8,8,8,8,8,8,8
)['Composite fraction','Guarantee level','Lim']56,184,148,122,319,378,927,328,0,MIDM2,45,69,655,554,0,MIDM52425,39321,65535[Scen_1,Input_var1]Ajo 18Table(Length1,Vehicle_type1,Zone1,Parameter1,Scen_1,Period1)(
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
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0,0,0,
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0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
19.269K,2668,8714,
6352,2437,5314,
13.16K,2634,5662,
15.474K,1474,5594,
9690,1432,5576,
12.39K,3611,5754,
10.84K,764,2406,
13.251K,2111,2426,
9742,2535,6146,
11.096K,1600,4594,
0,'NAN',63.067,
0,'NAN',37.833,
0,'NAN',60.133,
0,'NAN',49.833,
0,'NAN',42.033,
0,'NAN',48.167,
0,'NAN',33.967,
0,'NAN',40.367,
0,'NAN',38.033,
0,'NAN',48.4,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
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0,0,0,
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0,0,0,
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0,0,0,
0,0,1,
1,0,1,
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0,0,0,
0,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
6,2,4,
1,2,1,
0,0,0,
6,0,2,
4,1,1,
6,3,3,
3,1,1,
3,1,1,
5,3,2,
2,2,3,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
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0,0,0,
0,0,0,
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0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
0,0,0,
4696,236,16,
10.456K,724,160,
15.148K,1360,360,
18.924K,1888,656,
22.636K,2272,948,
25.372K,2860,1144,
28.168K,3200,1544,
30.74K,3720,1704,
32.624K,4088,2000,
0,0,1,
777,70,49,
1597,132,64,
2470,226,110,
3200,285,148,
4078,392,206,
4885,433,246,
5667,494,253,
6587,613,300,
7322,650,337,
0,0,0,
120,4,0,
544,12,0,
1228,40,0,
1828,72,8,
2660,120,28,
3332,132,16,
4096,168,32,
4980,304,40,
5752,280,40,
0,0,0,
1355.5,67.4,5.6,
3069.3,210.2,44.8,
4520.6,395.6,101.2,
5638.5,550.5,187,
6764.6,678.9,275.6,
7584.5,841.9,328.5,
8409.6,951.1,444.7,
9137.2,1104.6,487,
9686.9,1205.9,580.7,
0,'NAN',0,
136,'NAN',1017,
334,'NAN',2830,
535,'NAN',4517,
731,'NAN',6298,
868,'NAN',8143,
1185,'NAN',9959,
1226,'NAN',11.751K,
1443,'NAN',12.951K,
1632,'NAN',14.876K,
'NAN','NAN','NAN',
9.338,8.982,9.3,
9.397,9.302,8.996,
9.313,9.345,9.196,
9.291,9.356,9.175,
9.214,9.325,9.146,
9.169,9.422,9.274,
9.109,9.39,9.318,
9.044,9.262,9.319,
8.98,9.386,9.393,
0,0,0,
4176,88,0,
11.928K,516,32,
20.092K,1116,80,
27.932K,1736,312,
35.268K,2512,528,
42.38K,3348,752,
49.1K,4024,1044,
55.644K,4644,1292,
61.716K,5240,1712,
1,3,0,
3890,340,188,
7785,675,388,
11.58K,1058,548,
15.467K,1358,749,
19.502K,1710,985,
23.424K,2153,1131,
27.462K,2459,1342,
31.38K,2773,1576,
35.161K,3029,1783,
0,0,0,
96,0,0,
784,12,0,
2060,36,0,
4060,64,8,
6188,120,0,
9004,212,16,
11.556K,312,36,
14.9K,428,52,
17.764K,488,72,
0,0,0,
1413.8,28.1,0,
4312.3,172.5,9,
7529.3,397.2,21.1,
10.7751K,606.5,99.4,
13.9095K,904.3,165.7,
16.9925K,1231.6,242.1,
19.8648K,1484.8,346.9,
22.7346K,1729.6,427.8,
25.3977K,1965.5,587.9,
0,0,0,
223,0,0,
612,0,0,
925,0,0,
1241,0,0,
1627,0,0,
2015,0,0,
2306,0,0,
2543,0,0,
3138,0,0,
'NAN','NAN','NAN',
9.062,8.979,'NAN',
8.967,9.029,9.068,
8.824,9.018,9.003,
8.616,9.051,8.914,
8.463,9.03,9.094,
8.251,8.978,8.996,
8.132,8.912,8.92,
7.939,8.881,8.903,
7.828,8.884,8.963,
0,0,0,
21.584K,496,32,
69.8K,2556,200,
121.432K,5744,796,
173.936K,9748,1480,
225.128K,14.308K,2620,
271.444K,19.06K,4000,
317.728K,24.008K,5524,
361.7K,27.84K,7252,
403.376K,33.088K,9248,
26,8,12,
22.641K,1974,1141,
45.645K,3977,2247,
67.856K,6058,3274,
90.311K,7783,4446,
113.587K,9914,5534,
135.338K,11.989K,6702,
158.559K,14.043K,7842,
180.854K,15.788K,8810,
203.574K,17.96K,9999,
0,0,0,
704,8,0,
4024,36,8,
9928,184,20,
18.512K,308,48,
28.84K,628,84,
40.692K,988,120,
53.848K,1488,204,
68.324K,1984,268,
83.504K,2560,384,
0,0,0,
8609.4,191.8,12,
28.8547K,1001.5,83,
51.1103K,2293.2,316.7,
75.1506K,3883.3,583.6,
98.5797K,5803.1,1062.7,
120.5841K,7834.3,1611.3,
142.478K,9925.8,2244.8,
163.7484K,11.5872K,2944.1,
184.3976K,13.888K,3761.6,
0,0,0,
1095,0,0,
2836,0,0,
4484,0,0,
6242,0,0,
8066,0,0,
9898,0,0,
11.802K,0,0,
12.862K,0,0,
14.814K,0,0,
'NAN','NAN','NAN',
9.016,9.017,9.068,
8.973,9.125,8.697,
8.89,9.042,8.878,
8.779,9.083,8.902,
8.682,9.048,8.943,
8.566,9.014,8.991,
8.469,8.982,8.944,
8.36,8.943,8.987,
8.259,8.919,8.962,
4,0,1,
2430,421,357,
2929,591,569,
3202,698,691,
3312,741,747,
3276,808,886,
3468,829,923,
3402,838,988,
3377,826,1002,
3472,920,1077,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
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0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
9.6,0,2,
2203.1,415.3,429.1,
2502,560.8,596,
2621.9,655.6,678,
2671.9,661,737.2,
2662.6,688.8,843.7,
2835.7,727,869.7,
2735,710.7,915.6,
2730.8,722.7,895.1,
2744.3,758.3,969.1,
16,'NAN',218,
335,'NAN',3693,
353,'NAN',4059,
428,'NAN',4313,
381,'NAN',4241,
349,'NAN',4250,
374,'NAN',4101,
479,'NAN',4129,
412,'NAN',4100,
369,'NAN',4051,
7,'NAN',7,
8.119,8.023,7.787,
8.194,8.089,7.967,
8.254,8.099,8.005,
8.271,8.135,8.004,
8.292,8.172,8.045,
8.305,8.178,8.051,
8.324,8.217,8.096,
8.321,8.188,8.121,
8.348,8.248,8.13,
8,4,1,
6830,878,521,
9360,1488,1061,
10.573K,1953,1508,
11.727K,2148,1842,
12.859K,2369,2141,
13.196K,2563,2387,
14.31K,2746,2667,
14.418K,2971,2858,
14.906K,3072,3093,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
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0,0,0,
0,0,0,
0,0,0,
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0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
21.8,13.3,1.6,
8869.5,1309.9,1065.6,
11.5586K,2070,1753.5,
12.8008K,2544.4,2314.5,
13.6598K,2779.1,2592.7,
14.8222K,2929.6,2886,
15.1139K,3152.9,3220.9,
15.8439K,3340.1,3457.7,
15.9973K,3565.5,3663.2,
16.451K,3638.4,3984,
54,0,0,
762,0,0,
727,0,0,
792,0,0,
762,0,0,
878,0,0,
809,0,0,
936,0,0,
848,0,0,
882,0,0,
7,7,7,
7.945,7.754,7.376,
8.001,7.876,7.638,
8.032,7.949,7.751,
8.062,7.956,7.834,
8.087,7.981,7.884,
8.097,8.002,7.886,
8.128,8.018,7.94,
8.133,8.043,7.936,
8.144,8.048,7.957,
137,36,59,
52.146K,6529,3700,
73.983K,11.362K,7189,
85.75K,14.719K,9854,
93.238K,16.883K,12.816K,
98.93K,19.105K,15.336K,
102.955K,20.558K,17.638K,
106.849K,21.753K,19.855K,
108.471K,23.272K,21.201K,
110.879K,24.295K,22.991K,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
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0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
396,114.2,184.3,
78.0746K,12.195K,8786.9,
100.9885K,18.0713K,14.7029K,
113.3641K,22.0473K,18.7866K,
120.3675K,24.3573K,22.4211K,
125.9044K,26.8556K,25.4311K,
130.066K,28.1091K,28.1859K,
133.6172K,29.6235K,30.6793K,
134.9101K,31.0318K,32.0755K,
137.0684K,32.017K,33.9438K,
365,0,0,
4461,0,0,
4981,0,0,
5486,0,0,
5434,0,0,
5384,0,0,
5408,0,0,
5306,0,0,
5409,0,0,
5473,0,0,
7.009,7,7.022,
7.943,7.688,7.383,
8.055,7.867,7.576,
8.103,7.937,7.66,
8.137,7.985,7.754,
8.162,8.02,7.821,
8.179,8.048,7.858,
8.198,8.063,7.902,
8.209,8.086,7.924,
8.22,8.105,7.952,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
8183,678,430,
7248,654,331,
6417,536,310,
5697,513,275,
4950,446,206,
4142,348,197,
3185,294,144,
2356,234,120,
1538,157,73,
809,75,35,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
12.8085K,1198.4,803.5,
11.4374K,1154.8,669.3,
10.2982K,954.7,610.3,
9214.1,939,543.7,
8039.8,812.6,430,
6826.1,648.3,406.8,
5402.8,567,310.3,
4143.2,458.4,266.1,
2828.1,307.7,157.2,
1576.7,162.2,72.9,
7244,'NAN',68.38K,
6606,'NAN',63.233K,
6118,'NAN',56.453K,
4896,'NAN',50.08K,
4166,'NAN',44.581K,
4365,'NAN',38K,
3464,'NAN',31.56K,
2461,'NAN',24.484K,
1773,'NAN',17.312K,
841,'NAN',9591,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
39.016K,3511,1902,
35.385K,3087,1715,
31.554K,2738,1550,
27.709K,2341,1356,
23.566K,2099,1142,
19.488K,1724,957,
15.76K,1327,734,
11.87K,1039,535,
7776,745,373,
3772,353,203,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
92.0975K,9435.5,5502.5,
84.4844K,8447.6,4923.7,
76.3031K,7517.2,4564.3,
68.0175K,6510,4013.2,
58.9465K,5851.7,3423.3,
49.7026K,4963.1,2839.1,
41.3678K,3835.3,2180.3,
31.8305K,3042.7,1630.3,
21.6314K,2189.1,1107.4,
11.0302K,1068.1,607.1,
13.903K,0,0,
13.319K,0,0,
12.294K,0,0,
10.866K,0,0,
9712,0,0,
8212,0,0,
6729,0,0,
5091,0,0,
3739,0,0,
2097,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
226.822K,19.856K,10.976K,
203.828K,17.844K,9948,
181.031K,15.662K,8979,
158.393K,13.714K,7761,
136.076K,11.894K,6716,
113.126K,9978,5495,
90.236K,7818,4421,
67.363K,5957,3274,
44.709K,3907,2301,
22.665K,1925,1149,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
634.5532K,62.3865K,36.8673K,
576.349K,56.6234K,33.5777K,
518.9331K,50.1254K,30.3282K,
460.526K,44.3069K,26.1883K,
402.6305K,39.0065K,22.9309K,
340.0809K,32.9882K,18.8304K,
278.0774K,26.1917K,15.1653K,
213.4045K,20.0904K,11.2442K,
145.9264K,13.4493K,7911.3,
76.3374K,6517.4,3998.4,
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246.2752K,9299,686.6,
383.6554K,16.6002K,1953.5,
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92.708K,11.007K,6047,
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421.897K,62.4063K,44.6658K,
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22.447K,2007,1056,
20.133K,1697,937,
17.48K,1551,814,
14.928K,1302,761,
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742.845K,64.933K,35.94K,
659.988K,58.042K,32.227K,
577.174K,50.3K,28.704K,
494.437K,43.53K,24.417K,
411.309K,36.34K,20.18K,
329.845K,28.936K,16.273K,
247.887K,21.7K,12.04K,
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8.2975279M,799.4336K,464.9211K,
7.5456138M,722.3074K,415.4238K,
6.7678881M,649.2187K,374.9703K,
5.9936226M,565.8136K,333.6525K,
5.199925M,494.8317K,285.1762K,
4.3956002M,416.0128K,235.7427K,
3.5800932M,333.17K,188.9586K,
2.7520976M,252.3943K,141.5849K,
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1,1,1,1,1,1,1,1,1,1,
7,7,7,7,7,7,7,7,7,7,
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8,8,8,8,8,8,8,8,8,8,
4,4,4,4,4,4,4,4,4,4,
2,2,2,2,2,2,2,2,2,2,
8,8,8,8,8,8,8,8,8,8
)['Composite fraction','Guarantee level','Lim']56,208,148,122,319,378,927,328,0,MIDM2,45,69,655,554,0,MIDM52425,39321,65535[Scen_1,Input_var1]Ajo 19Table(Length1,Vehicle_type1,Zone1,Parameter1,Scen_1,Period1)(
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6094,608,155,
6032,569,172,
6114,596,179,
6132,551,192,
6252,568,169,
6139,563,136,
6211,562,169,
6146,543,162,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
711.283K,107.581K,87.95K,
709.278K,108.779K,86.45K,
701.572K,108.247K,79.696K,
709.032K,108.343K,78.616K,
711.837K,109.122K,81.614K,
712.292K,108.095K,81.732K,
710.091K,110.946K,79.994K,
707.156K,107.403K,79.19K,
704.278K,107.969K,88.146K,
712.39K,108.093K,82.458K,
0,0,1880.733,
0,0,1865.3,
0,0,1841.233,
0,0,1872.667,
0,0,1882.1,
0,0,1882.267,
0,0,1857.667,
0,0,1852.233,
0,0,1851.133,
0,0,1859.267,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
16.551K,1525,276,
16.416K,1468,278,
16.563K,1522,267,
16.614K,1521,282,
16.4K,1498,273,
16.477K,1555,276,
16.273K,1526,252,
16.523K,1536,270,
16.502K,1468,262,
16.346K,1507,243,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
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0,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
61.413K,5105,1218,
61.428K,5133,1186,
61.384K,5023,1124,
61.385K,5224,1089,
61.232K,5182,1052,
61.698K,5202,1131,
61.556K,5029,1126,
61.341K,5104,1135,
61.621K,4988,1151,
61.261K,5150,1210,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
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0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
7532,476,43,
27.045K,4302,1242,
15.695K,2452,893,
7587,496,18,
27.434K,4133,1321,
15.707K,2551,887,
7324,496,43,
27.681K,4231,1259,
15.566K,2374,922,
7166,480,30,
8458,750,190,
8469,811,188,
8441,782,176,
8558,779,183,
8447,767,180,
8574,764,176,
8452,781,198,
8348,832,163,
8359,791,171,
8382,773,172,
7532,476,43,
7377,529,56,
7421,532,27,
7587,496,18,
7381,503,31,
7450,497,24,
7324,496,43,
7166,577,10,
7146,481,29,
7166,480,30,
2317.7,171.5,21.9,
8091.5,1307,384.2,
4883.3,772.3,291.6,
2465.8,186.8,8.8,
9209.5,1401.8,458.2,
5359.8,884.5,310.8,
2585.5,192.5,20.7,
10.6475K,1641.1,485.9,
6039.6,929.2,363,
2812.8,206.8,13.6,
431,100.897,4715,
577,299.759,8759,
573,211.103,6846,
467,109.655,4744,
687,320.483,9234,
482,218.966,6823,
431,103.345,4802,
756,340.345,9954,
543,227.31,7354,
480,109,4920,
3.093,2.761,2.472,
8.187,8.916,8.957,
6.676,8.068,8.874,
3.1,2.76,2.43,
8.113,8.828,9.045,
6.62,8.17,8.907,
3.09,2.794,2.558,
8.019,8.62,9.086,
6.608,8.031,8.784,
3.02,2.744,2.577,
20.248K,815,68,
56.655K,6367,1188,
36.897K,3978,786,
20.509K,846,42,
56.359K,6403,1246,
36.114K,4009,880,
19.881K,823,72,
56.369K,6328,1095,
36.356K,3871,789,
19.346K,921,57,
35.689K,3280,941,
35.939K,3198,982,
35.912K,3353,990,
35.923K,3313,1000,
35.729K,3256,979,
35.924K,3336,1001,
35.797K,3329,924,
36.06K,3323,973,
36.053K,3406,1013,
35.613K,3389,1039,
20.248K,815,68,
20.47K,813,77,
20.459K,837,60,
20.509K,846,42,
19.88K,798,84,
20.034K,918,73,
19.881K,823,72,
19.766K,827,67,
19.93K,885,67,
19.346K,921,57,
10.1124K,461.2,44.5,
23.6198K,2492.3,475.9,
16.7066K,1673.2,329.9,
10.6146K,500.1,27,
25.3183K,2772.1,520.3,
17.3201K,1867.4,388.6,
10.7421K,485.2,49.1,
28.1378K,3047.5,507.6,
18.936K,1931.3,360.5,
11.0503K,542.2,34.5,
1092,0,0,
1439,0,0,
1471,0,0,
1048,0,0,
1578,0,0,
1400,0,0,
1061,0,0,
1732,0,0,
1339,0,0,
1094,0,0,
2.713,2.559,2.534,
7.199,8.522,8.526,
5.754,7.839,8.414,
2.719,2.546,2.376,
7.202,8.518,8.525,
5.735,7.672,8.359,
2.714,2.531,2.515,
7.124,8.406,8.534,
5.722,7.648,8.346,
2.709,2.568,2.447,
68.238K,2613,204,
309.574K,33.819K,4431,
183.534K,19.943K,2792,
67.443K,2673,177,
304.857K,33.534K,4170,
180.59K,19.54K,2722,
65.847K,2591,217,
302.13K,32.315K,4168,
174.613K,19.201K,2713,
65.154K,2603,136,
170.626K,16.162K,4203,
169.959K,16.113K,4338,
170.127K,16.036K,4292,
170.296K,16.163K,4299,
169.511K,16.251K,4358,
170.003K,16.165K,4247,
169.195K,16.067K,4381,
168.8K,16.012K,4244,
168.922K,16.11K,4364,
169.715K,16.143K,4243,
68.238K,2613,204,
67.61K,2590,209,
67.712K,2503,190,
67.443K,2673,177,
66.254K,2591,189,
66.895K,2664,162,
65.847K,2591,217,
63.974K,2541,235,
64.213K,2504,182,
65.154K,2603,136,
42.8523K,1813.8,151.4,
143.9504K,15.1676K,2156,
93.1195K,9523.5,1387.9,
43.0737K,1829.6,136,
153.7883K,16.2987K,2135.6,
97.4538K,9958.3,1417.7,
43.5128K,1840.9,149.8,
169.3875K,17.5856K,2313.3,
102.5769K,10.6794K,1512.5,
44.9425K,1823.8,101.6,
4458,0,0,
7582,0,0,
6197,0,0,
4442,0,0,
8196,0,0,
6218,0,0,
4467,0,0,
8638,0,0,
6727,0,0,
4535,0,0,
2.637,2.523,2.45,
7.904,8.643,8.578,
6.762,8.168,8.359,
2.654,2.535,2.494,
7.885,8.615,8.596,
6.74,8.098,8.406,
2.652,2.526,2.505,
7.852,8.556,8.509,
6.716,8.099,8.358,
2.648,2.534,2.523,
926,274,147,
1993,535,453,
2010,516,498,
971,283,165,
2001,523,438,
2213,573,528,
1128,285,155,
2164,528,520,
2424,641,497,
1216,293,142,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
926,274,147,
573,102,27,
841,173,69,
971,283,165,
519,90,24,
948,175,80,
1128,285,155,
534,93,33,
1015,228,57,
1216,293,142,
949.7,339.3,231,
1725.6,467.6,476.7,
1943.4,551.8,546.2,
1000.3,360.6,260.2,
1773.4,472.6,478.7,
2144.8,578.2,557.8,
1194.1,356.8,235.6,
1896.9,490.8,545.2,
2348.2,616.9,557.3,
1299.2,357.6,245.6,
625,1432.138,21.919K,
392,260.414,6860,
701,580.724,12.639K,
704,1428,21.981K,
485,276.241,7245,
810,574.69,12.609K,
730,1438.793,22.178K,
443,284.448,7401,
679,602.793,13.066K,
720,1442.241,22.468K,
1.741,1.645,1.419,
6.426,6.968,7.512,
5.478,5.883,6.99,
1.763,1.601,1.401,
6.592,7.029,7.511,
5.42,6.149,6.955,
1.757,1.596,1.456,
6.695,7.045,7.528,
5.509,5.851,7.121,
1.779,1.623,1.309,
15.441K,2465,873,
13.328K,2100,1307,
16.976K,2830,1567,
15.414K,2467,958,
13.558K,2286,1397,
17.749K,2772,1504,
15.916K,2506,852,
14.502K,2429,1486,
18.057K,2993,1571,
16.267K,2468,982,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
15.441K,2465,873,
5427,429,111,
11.205K,1380,297,
15.414K,2467,958,
5511,480,78,
11.568K,1269,272,
15.916K,2506,852,
5811,558,108,
11.598K,1379,307,
16.267K,2468,982,
27.755K,5293.9,2221.5,
15.6316K,2817.1,1973.1,
23.6478K,4091.5,2401.8,
27.8928K,5254.3,2382.5,
16.1146K,2978.6,2178.1,
24.278K,3958.6,2401.7,
28.5147K,5216.2,2109.6,
17.0737K,3156.1,2208.3,
24.622K,4252.8,2434.7,
29.2873K,5202.8,2372.1,
3358,0,0,
1221,0,0,
2102,0,0,
3433,0,0,
1286,0,0,
2093,0,0,
3269,0,0,
1223,0,0,
2294,0,0,
3805,0,0,
1.618,1.431,1.218,
5.549,6.622,7.216,
3.864,4.842,6.541,
1.615,1.436,1.264,
5.557,6.626,7.366,
3.94,5.036,6.575,
1.63,1.458,1.237,
5.607,6.527,7.308,
4.002,5.034,6.52,
1.622,1.441,1.287,
102.388K,13.549K,3999,
73.123K,13.26K,8267,
106.674K,17.081K,8776,
102.853K,13.49K,4122,
77.963K,13.598K,8437,
109.799K,17.408K,8713,
103.348K,13.476K,4164,
82.141K,14.4K,8124,
114.24K,17.763K,8927,
104.561K,13.54K,4107,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
102.388K,13.549K,3999,
27.615K,2097,243,
63.992K,6145,868,
102.853K,13.49K,4122,
28.254K,2010,249,
65.076K,6179,922,
103.348K,13.476K,4164,
28.068K,2031,180,
66.385K,6278,948,
104.561K,13.54K,4107,
226.5264K,35.4137K,12.2189K,
99.2616K,19.8379K,15.6048K,
163.6174K,27.0544K,16.9069K,
227.88K,34.9172K,12.418K,
105.8095K,20.493K,15.7686K,
167.4418K,27.6209K,16.5906K,
229.4829K,34.8297K,12.5573K,
111.3335K,21.4265K,15.2423K,
173.6757K,28.5464K,16.7967K,
231.9405K,35.0996K,12.361K,
20.924K,0,0,
6579,0,0,
12.067K,0,0,
20.455K,0,0,
7016,0,0,
11.936K,0,0,
21.032K,0,0,
7235,0,0,
12.429K,0,0,
20.994K,0,0,
1.524,1.349,1.163,
5.741,6.95,7.456,
4.261,5.667,7.012,
1.522,1.359,1.179,
5.843,7.016,7.462,
4.31,5.696,6.983,
1.521,1.363,1.171,
5.978,7.071,7.502,
4.388,5.698,6.997,
1.522,1.357,1.179,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
6635,563,133,
6575,607,99,
6550,562,121,
6585,607,115,
6679,598,102,
6622,640,128,
6616,599,113,
6577,576,111,
6738,644,114,
6649,592,113,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
10.5083K,1009.1,268,
10.4937K,1069.6,201.8,
10.4965K,998.1,232.9,
10.4967K,1057.9,238.1,
10.6139K,1052.1,219.1,
10.5135K,1112.3,260.3,
10.5337K,1040.7,250.4,
10.5093K,1008.8,229.3,
10.722K,1132.8,230.8,
10.5814K,1045.4,235.6,
1913,1489.552,38.852K,
2004,1487.931,39.091K,
1918,1479.793,39.447K,
2019,1503.897,38.932K,
1811,1483.793,39.328K,
1761,1491.241,38.88K,
1913,1473.621,38.98K,
1964,1448.586,39.163K,
1940,1499.034,38.768K,
2107,1486.034,38.901K,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
27.379K,2553,497,
27.518K,2462,517,
27.663K,2403,499,
27.676K,2361,459,
27.481K,2521,510,
27.412K,2465,503,
27.387K,2559,469,
27.472K,2499,487,
27.473K,2438,546,
27.401K,2405,476,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
66.7516K,6878.4,1443.6,
66.5797K,6721.2,1467.4,
67.3347K,6502.8,1494.4,
67.5215K,6403.8,1369.3,
66.8191K,6753.1,1508.1,
66.9414K,6667.4,1496.4,
66.5091K,6817.6,1383.5,
66.9337K,6717.2,1437.2,
66.7025K,6558.1,1575.6,
66.8318K,6479,1414,
7091,0,0,
7410,0,0,
7658,0,0,
7288,0,0,
7646,0,0,
7305,0,0,
7561,0,0,
7044,0,0,
7551,0,0,
7212,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
130.578K,11.676K,2278,
130.13K,11.639K,2264,
129.855K,11.509K,2381,
129.691K,11.577K,2414,
130.153K,11.71K,2365,
130.322K,11.649K,2358,
129.869K,11.709K,2333,
130.288K,11.727K,2376,
130.425K,11.665K,2292,
129.805K,11.752K,2316,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
383.0858K,37.156K,7765.1,
381.9398K,37.1091K,7699.1,
380.801K,36.6291K,8138.4,
380.7955K,36.8536K,8126.1,
382.1598K,37.5434K,8002.3,
382.7675K,37.0185K,7998.8,
381.4788K,37.3668K,7896.9,
382.0664K,37.3665K,7998.1,
382.2874K,37.2088K,7802.7,
381.1567K,37.5194K,7807.6,
33.511K,0,0,
33.887K,0,0,
34.448K,0,0,
33.331K,0,0,
34.602K,0,0,
33.798K,0,0,
33.753K,0,0,
34.262K,0,0,
33.133K,0,0,
33.648K,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
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'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
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4016,337,70,
3818,330,58,
3987,336,68,
3972,350,59,
3956,352,61,
4114,343,68,
3842,330,59,
3956,347,66,
3985,358,60,
3953,358,51,
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'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
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108.19K,8281,1439,
108.299K,8304,1397,
108.278K,8287,1480,
108.571K,8271,1410,
107.767K,8224,1451,
107.986K,8322,1491,
107.844K,8212,1507,
108.323K,8255,1417,
107.845K,8060,1443,
107.613K,8242,1462,
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0,0,0,
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'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
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0,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
10.322K,470,25,
14.21K,1274,184,
11.499K,783,95,
10.356K,453,22,
14.22K,1206,140,
10.871K,790,87,
10.08K,412,9,
13.876K,1256,141,
10.867K,698,59,
9974,471,39,
19.841K,1849,508,
20.064K,1816,476,
20.24K,1872,468,
20.142K,1831,453,
19.932K,1833,488,
19.897K,1793,465,
19.925K,1807,484,
20.042K,1866,449,
19.981K,1854,431,
20.009K,1933,462,
10.322K,470,25,
10.26K,453,30,
10.534K,451,35,
10.356K,453,22,
10.293K,438,29,
9903,431,28,
10.08K,412,9,
9896,460,16,
9827,441,27,
9974,471,39,
15.7763K,759.3,33.9,
20.9559K,1631.1,189.4,
18.2633K,1092.9,111.1,
16.6212K,615.3,31.9,
22.1659K,1583.3,154.7,
17.7549K,1213.7,109.6,
16.5831K,631.7,16.3,
23.0223K,1774,169.7,
18.6893K,1061.4,91.2,
17.5316K,760,91.6,
0,0,0,
0,0,0,
0,0,0,
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0,0,0,
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0,0,0,
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0,0,0,
2.754,2.528,2.5,
4.573,6.764,7.811,
3.288,5.278,6.389,
2.76,2.645,2.413,
4.554,6.681,7.591,
3.323,5.411,6.782,
2.754,2.603,2.432,
4.587,6.669,8.109,
3.345,4.894,5.86,
2.744,2.618,2.49,
191.34K,6516,349,
591.398K,52.861K,5423,
379.247K,32.015K,2946,
188.624K,6760,343,
578.907K,51.91K,5107,
374.163K,31.373K,3332,
184.807K,6411,344,
573.999K,50.757K,5021,
365.495K,29.92K,3279,
181.794K,6390,322,
627.453K,59.309K,15.031K,
626.378K,59.286K,15.255K,
624.942K,59.244K,15.109K,
625.115K,59.177K,15.285K,
625.988K,59.019K,15.222K,
625.747K,59.158K,15.515K,
624.685K,59.057K,15.351K,
627.599K,58.939K,15.243K,
625.062K,58.986K,15.579K,
624.796K,58.699K,15.35K,
191.34K,6516,349,
188.627K,6427,356,
187.816K,6719,318,
188.624K,6760,343,
187.012K,6605,362,
186.801K,6777,306,
184.807K,6411,344,
183.327K,6476,347,
183.024K,6290,341,
181.794K,6390,322,
388.1476K,13.9352K,770.9,
908.7142K,73.7506K,7423.8,
649.2905K,49.417K,4268.9,
390.2713K,14.6659K,712.2,
950.8649K,77.7203K,7300.3,
671.1882K,50.688K,4956.7,
389.6968K,14.1682K,755.2,
1.0263593M,83.722K,7814.8,
697.9664K,51.3494K,5142,
397.7302K,14.3249K,673.5,
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0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
2.597,2.502,2.435,
7.106,8.259,8.392,
5.836,7.55,8.054,
2.603,2.496,2.451,
7.064,8.221,8.375,
5.809,7.512,8.163,
2.609,2.504,2.45,
7.043,8.174,8.374,
5.778,7.543,8.085,
2.607,2.502,2.428,
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0,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
9519,1379,483,
4536,633,295,
7830,1060,356,
9786,1378,431,
4708,667,330,
8156,1018,359,
9845,1395,475,
5020,707,322,
8273,1088,393,
10.035K,1462,423,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
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0,0,0,
0,0,0,
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0,0,0,
0,0,0,
9519,1379,483,
2655,270,33,
6142,701,111,
9786,1378,431,
2652,294,33,
6438,674,98,
9845,1395,475,
2919,252,54,
6466,692,112,
10.035K,1462,423,
80.8649K,12.3292K,4673.5,
22.1674K,3303.9,1417.9,
44.4981K,5679.9,1950.7,
82.3554K,12.5192K,4157.2,
22.4743K,3378.4,1564,
45.8946K,5116.3,1856.5,
81.8237K,12.8448K,4768.2,
23.4929K,3524.8,1690.6,
46.2444K,5569.7,1980.1,
83.2558K,12.6094K,4231.6,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
1.523,1.391,1.214,
4.488,5.289,7.036,
3.129,3.835,5.774,
1.524,1.377,1.203,
4.628,5.263,7.109,
3.108,3.858,6.059,
1.537,1.353,1.161,
4.53,5.8,6.688,
3.148,4.006,5.972,
1.532,1.42,1.216,
436.113K,52.793K,14.682K,
200.849K,31.205K,14.066K,
343.053K,44.505K,15.917K,
436.491K,52.417K,14.942K,
211.848K,32.07K,14.417K,
349.6K,44.999K,16.322K,
439.878K,52.646K,15.007K,
221.152K,33.227K,14.437K,
358.907K,46.482K,16.348K,
443.002K,52.309K,15.028K,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
436.113K,52.793K,14.682K,
94.281K,6636,576,
239.011K,20.445K,2789,
436.491K,52.417K,14.942K,
95.451K,6297,525,
239.607K,20.296K,2816,
439.878K,52.646K,15.007K,
96.372K,6273,621,
241.581K,20.619K,2938,
443.002K,52.309K,15.028K,
3.8495048M,510.4271K,156.8671K,
870.598K,149.4513K,85.352K,
1.7759878M,233.2435K,96.893K,
3.8480204M,505.478K,160.9455K,
920.9548K,154.0586K,86.369K,
1.7995433M,234.6882K,99.2196K,
3.8695413M,508.0231K,161.0948K,
953.3784K,158.7021K,86.8903K,
1.8432487M,240.8323K,99.7014K,
3.8961669M,503.0197K,160.004K,
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0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
1.444,1.302,1.137,
5.189,6.6,7.315,
3.655,5.022,6.496,
1.444,1.301,1.131,
5.303,6.696,7.353,
3.728,5.076,6.521,
1.447,1.303,1.132,
5.405,6.75,7.313,
3.806,5.13,6.473,
1.446,1.301,1.138,
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0,0,0,
0,0,0,
0,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
18.83K,1702,343,
18.861K,1769,301,
19.05K,1651,327,
18.842K,1726,322,
18.731K,1654,369,
18.766K,1635,343,
19.057K,1666,339,
18.744K,1606,331,
18.959K,1655,309,
18.778K,1730,355,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
170.0948K,16.9478K,3573.1,
170.0705K,16.9999K,3181.2,
171.2865K,16.024K,3567.6,
170.7458K,16.8158K,3416.8,
168.2475K,16.5698K,3885.1,
168.6342K,16.0233K,3782.9,
171.9225K,16.3162K,3668.2,
170.4132K,15.9605K,3373.9,
172.0685K,16.1631K,2992.7,
169.5879K,17.1949K,3649.4,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
508.328K,45.724K,9038,
509.26K,45.545K,8952,
509.274K,45.652K,9219,
508.331K,45.269K,9410,
508.588K,45.697K,9208,
508.029K,45.295K,8948,
508.947K,45.56K,9172,
508.255K,45.596K,9169,
508.318K,45.65K,8972,
508.856K,45.657K,9178,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
5.490169M,527.5326K,108.7452K,
5.4971351M,524.1576K,107.2983K,
5.4897109M,524.2204K,111.4799K,
5.4852948M,520.555K,113.0743K,
5.4879575M,524.3988K,110.4937K,
5.4874935M,521.9861K,107.5848K,
5.4895708M,524.2651K,109.911K,
5.4919009M,525.8214K,109.9486K,
5.4828862M,526.8071K,107.1254K,
5.4939553M,524.1193K,110.9175K,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN'
)168,232,148,122,104,114,820,303,0,MIDM2,30,434,710,303,0,MIDM65535,52427,65534[Scen_1,Period1][Scen_1,Parameter1]Ajo 19 descAjo 19
* Kuvaus: KakkoskŠsikirjoitukseen tulevia tuloksia
* KŠytetyt oletukset
o Malliversio 1.7.8
o Scenarios, taulu 15
o Matkamatriisi, HLT
o Joukkoliikennematriisi, 3
o From: 1001..1129
o Vehicle_noch: ['d9','d8','d7','d6','d5','d4','d3','d2','d1','c9','c8','c7','c6','c5','c4','c3','c2','c1','Noch']
* Kuka otti ajaakseen: Juha
* Milloin ajo kŠynnistettiin: 15:48, 25.8.2006 KLO 15:55 uusinta yritys
* Milloin ajo valmistui: 28.8.2006 KLO 7:15 oli jo loppunut. Tulokset
* HuomautuksiaTable(Input_var1,Scen_1)(
0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6,
0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6,
7,7,7,7,7,7,7,7,7,7,
0,0,0,0,0,0,0,0,0,0,
2,2,2,2,2,2,2,2,2,2,
0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,
9,8,8,8,7,7,7,6,6,6,
1,3,2,1,3,2,1,3,2,1,
2,2,2,2,2,2,2,2,2,2,
8,8,8,8,8,8,8,8,8,8
)['Composite fraction','Guarantee level','Lim']56,232,148,122,319,378,927,328,0,MIDM2,45,69,655,554,0,MIDM52425,39321,65535[Scen_1,Input_var1]Ajo 20Table(Length1,Vehicle_type1,Zone1,Parameter1,Scen_1,Period1)(
0,0,0,
0,0,0,
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0,0,0,
0,0,0,
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0,0,0,
0,0,0,
0,0,0,
0,0,0,
10.238K,941,270,
6287,555,164,
6094,608,155,
6032,569,172,
6114,596,179,
6132,551,192,
6252,568,169,
6139,563,136,
6211,562,169,
6146,540,153,
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0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
866.686K,120.931K,92.592K,
709.278K,108.779K,86.45K,
637.893K,84.704K,52.108K,
603.877K,72.981K,37.322K,
580.544K,63.012K,26.806K,
554.699K,56.706K,24.614K,
509.135K,47.176K,21.228K,
466.178K,38.062K,16.61K,
431.995K,33.49K,13.038K,
362.365K,24.032K,10.326K,
0,0,2358.967,
0,0,1865.3,
0,0,1688.7,
0,0,1640.867,
0,0,1579.233,
0,0,1537.733,
0,0,1402.267,
0,0,1344.533,
0,0,1251.733,
0,0,1116.5,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
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0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
27.342K,2509,455,
16.416K,1468,278,
16.436K,1503,252,
16.424K,1470,245,
16.119K,1404,233,
16.066K,1400,232,
15.594K,1314,180,
15.494K,1212,178,
15.216K,1083,148,
14.25K,1045,40,
0,0,0,
0,0,0,
0,0,0,
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0,0,0,
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0,0,0,
0,0,0,
0,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
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0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
102.644K,8475,1938,
61.428K,5133,1186,
61.155K,4926,1034,
60.664K,4987,871,
59.911K,4772,745,
59.655K,4711,688,
58.671K,4244,575,
57.508K,4032,366,
56.014K,3765,260,
52.322K,3403,148,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
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0,0,0,
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0,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
7532,1040,467,
38.037K,5586,1430,
37.704K,5421,1453,
38.477K,5652,1580,
38.556K,5460,1567,
38.38K,5552,1679,
38.611K,5772,1655,
38.455K,5788,1749,
38.499K,5840,1832,
38.377K,5835,1901,
0,0,1,
8469,811,188,
8441,782,176,
8558,779,183,
8447,767,180,
8574,764,176,
8452,781,198,
8348,832,163,
8359,791,171,
8382,776,181,
0,0,0,
7377,529,56,
7421,532,27,
7587,496,18,
7418,514,32,
7559,510,24,
7414,510,40,
7327,591,12,
7339,504,29,
7361,505,32,
2372.6,340,155.1,
11.2K,1671.5,440.1,
11.1403K,1628.5,454.1,
11.3813K,1689.7,483.1,
11.3735K,1639.5,478.2,
11.2887K,1665.8,520.8,
11.3764K,1714.6,509.6,
11.387K,1731.5,537.1,
11.3691K,1747.8,570.9,
11.2926K,1762.3,578.6,
276,138.655,3859,
659,350.552,10.186K,
737,351.69,10.295K,
772,360.828,10.224K,
811,357.552,10.265K,
789,356.483,10.361K,
822,359.586,10.378K,
793,358.172,10.473K,
681,367.276,10.571K,
752,374.31,10.672K,
9.253,9.085,9.104,
8.838,9.223,9.036,
8.818,9.171,9.162,
8.821,9.256,9.249,
8.857,9.231,9.221,
8.835,9.22,9.257,
8.866,9.273,9.203,
8.863,9.205,9.314,
8.881,9.277,9.233,
8.856,9.271,9.3,
24.723K,2499,506,
74.237K,7804,1321,
74.08K,8152,1295,
74.505K,8217,1540,
74.123K,8136,1526,
75.081K,8217,1560,
75.805K,8493,1417,
76.089K,8514,1525,
76.034K,8746,1620,
76.689K,8873,1705,
6659,677,467,
35.939K,3198,982,
36.039K,3372,1005,
36.113K,3364,1037,
36.01K,3350,1019,
36.335K,3491,1045,
36.476K,3541,996,
37.089K,3647,1065,
37.339K,3791,1127,
37.709K,3851,1242,
2561,130,37,
20.47K,813,77,
20.562K,841,60,
20.649K,862,42,
20.348K,824,93,
20.662K,956,77,
20.685K,874,77,
21.377K,966,91,
21.781K,1053,83,
21.839K,1105,100,
9518.9,969.6,207.9,
29.7674K,3007.7,516,
29.7132K,3146.2,498.9,
29.7954K,3193.6,585.3,
29.6371K,3168.3,585.4,
29.9985K,3235.1,603.4,
30.3893K,3309.5,544.3,
30.6725K,3339.5,591.4,
30.6443K,3418.8,612,
30.9691K,3431.3,647,
616,0,0,
1671,0,0,
1924,0,0,
1694,0,0,
1697,0,0,
1795,0,0,
1723,0,0,
1826,0,0,
1769,0,0,
1920,0,0,
8.704,8.813,8.395,
7.862,8.757,8.617,
7.85,8.776,8.721,
7.86,8.757,8.813,
7.873,8.79,8.609,
7.869,8.659,8.678,
7.884,8.779,8.701,
7.83,8.7,8.635,
7.791,8.666,8.717,
7.804,8.644,8.688,
123.007K,13.064K,1763,
396.107K,40.507K,4950,
397.441K,40.639K,4814,
398.493K,41.051K,5315,
398.252K,41.547K,5042,
401.187K,41.616K,5236,
401.053K,41.733K,5402,
404.028K,42.611K,5660,
404.992K,43.147K,5930,
412.581K,43.851K,5751,
42.81K,5083,1962,
169.959K,16.113K,4338,
170.356K,16.133K,4382,
171.017K,16.4K,4517,
170.832K,16.661K,4665,
172.046K,16.656K,4690,
172.08K,16.852K,4932,
172.633K,17.084K,5013,
174.529K,17.333K,5255,
178.654K,17.89K,5305,
10.673K,480,75,
67.61K,2590,209,
67.741K,2503,191,
67.602K,2693,185,
67.538K,2691,201,
68.561K,2754,193,
68.085K,2730,256,
67.924K,2768,273,
69.616K,2885,253,
73.219K,3162,242,
57.0672K,6037,836.7,
178.3393K,17.8941K,2367,
178.8981K,17.8976K,2258.1,
179.2769K,18.1158K,2539.7,
179.3357K,18.3188K,2399,
180.8682K,18.4103K,2458.4,
180.8404K,18.4955K,2517.5,
181.6853K,18.761K,2688.1,
182.4202K,19.0847K,2756.5,
186.0919K,19.2614K,2701.7,
3744,0,0,
8664,0,0,
8706,0,0,
8787,0,0,
8854,0,0,
9003,0,0,
8920,0,0,
8977,0,0,
9303,0,0,
9109,0,0,
8.613,8.771,8.585,
8.322,8.801,8.637,
8.327,8.81,8.663,
8.331,8.782,8.68,
8.332,8.791,8.655,
8.322,8.783,8.678,
8.33,8.788,8.628,
8.345,8.792,8.613,
8.318,8.778,8.649,
8.285,8.767,8.657,
1277,289,360,
1354,406,424,
1407,364,463,
1333,398,444,
1374,433,459,
1485,371,455,
1404,392,468,
1289,422,436,
1423,386,434,
1340,365,445,
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1194.6,325.2,382.3,
1237.6,374.4,442,
1230.5,347.1,477.9,
1183.5,351.9,472.8,
1233.8,385.5,490.9,
1330.3,360.4,464.7,
1250.4,366.7,470.3,
1176.6,371.9,467.6,
1270.1,343.9,459.4,
1237.2,348.6,482.7,
215,137.793,3558,
247,137.862,4232,
218,142.483,4261,
237,147.207,4191,
338,145.103,4228,
243,132.828,4030,
217,135.31,4158,
217,135.69,4041,
262,133.552,4159,
253,138.241,4072,
8.009,7.846,7.889,
8.117,8.031,7.892,
8.161,8.075,7.878,
8.139,8.082,7.848,
8.145,8.031,7.863,
8.144,7.974,7.939,
8.138,8.046,7.931,
8.113,8.102,7.846,
8.142,8.073,7.877,
8.119,7.999,7.856,
6437,1221,900,
8184,1734,1240,
8321,1737,1382,
8369,1701,1291,
8485,1744,1304,
8265,1747,1273,
8107,1799,1350,
8338,1798,1361,
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0,0,0,
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8421.6,1875.8,1555.7,
9895,2429.8,1887.6,
10.112K,2450.2,2080.5,
10.296K,2361.9,2069.7,
10.0997K,2407.1,2095,
10.009K,2397.4,1995.9,
9911.8,2391.5,2072.6,
10.176K,2405.6,2105.2,
10.2534K,2437.4,2135.1,
10.1441K,2442.1,2110.4,
606,0,0,
726,0,0,
562,0,0,
586,0,0,
555,0,0,
629,0,0,
614,0,0,
515,0,0,
752,0,0,
632,0,0,
7.882,7.734,7.588,
7.963,7.804,7.707,
7.957,7.792,7.698,
7.953,7.812,7.648,
7.98,7.811,7.661,
7.967,7.816,7.689,
7.957,7.854,7.697,
7.963,7.846,7.691,
7.966,7.861,7.691,
7.967,7.819,7.695,
32.501K,7692,4986,
47.541K,11.243K,8003,
48.091K,11.292K,8349,
47.711K,11.223K,8062,
48.032K,11.282K,8525,
47.432K,11.313K,8594,
47.946K,11.7K,8690,
47.67K,11.349K,8530,
47.515K,11.268K,8917,
47.53K,11.996K,9268,
0,0,0,
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0,0,0,
0,0,0,
49.3478K,12.7759K,10.1839K,
67.5987K,17.3118K,15.2604K,
68.897K,17.3051K,15.7577K,
68.5105K,17.2844K,15.4333K,
68.2847K,17.497K,16.1167K,
67.3234K,17.4364K,16.1396K,
67.6285K,17.6097K,16.2654K,
67.6656K,17.4475K,16.0225K,
67.4016K,17.5279K,16.5032K,
67.4594K,18.3078K,16.9208K,
3864,0,0,
4278,0,0,
4454,0,0,
4481,0,0,
4488,0,0,
4198,0,0,
4454,0,0,
4253,0,0,
4310,0,0,
4349,0,0,
7.913,7.801,7.558,
7.995,7.875,7.621,
7.989,7.885,7.63,
7.988,7.881,7.618,
7.995,7.877,7.627,
7.997,7.881,7.641,
7.999,7.899,7.643,
7.995,7.876,7.631,
7.997,7.872,7.654,
7.996,7.895,7.664,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
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0,0,0,
0,0,0,
0,0,0,
11.069K,941,215,
6575,607,99,
6550,562,121,
6585,607,115,
6679,598,102,
6622,640,128,
6616,599,113,
6577,576,111,
6738,644,114,
6649,592,113,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
17.1256K,1579.1,415.9,
10.4937K,1069.6,201.8,
10.4965K,998.1,232.9,
10.4967K,1057.9,238.1,
10.6139K,1052.1,219.1,
10.5135K,1112.3,260.3,
10.5337K,1040.7,250.4,
10.5093K,1008.8,229.3,
10.722K,1132.8,230.8,
10.5814K,1045.4,235.6,
2708,2372.828,60.693K,
2004,1487.931,39.091K,
1918,1479.793,39.455K,
2019,1503.897,38.935K,
1811,1483.793,39.326K,
1761,1491.241,38.879K,
1913,1473.621,38.98K,
1964,1448.586,39.168K,
1940,1499.034,38.768K,
2107,1486.034,38.898K,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
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45.618K,4172,792,
27.518K,2462,517,
27.663K,2403,499,
27.676K,2361,459,
27.481K,2521,510,
27.412K,2465,503,
27.387K,2559,469,
27.472K,2499,487,
27.473K,2438,546,
27.401K,2405,476,
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105.2669K,10.6975K,2272.3,
66.5797K,6721.2,1467.4,
67.3347K,6502.8,1494.4,
67.5215K,6403.8,1369.3,
66.8191K,6753.1,1508.1,
66.9414K,6667.4,1496.4,
66.5091K,6817.6,1383.5,
66.9337K,6717.2,1437.2,
66.7025K,6558.1,1575.6,
66.8318K,6479,1414,
11.212K,0,0,
7410,0,0,
7658,0,0,
7288,0,0,
7646,0,0,
7305,0,0,
7561,0,0,
7044,0,0,
7551,0,0,
7212,0,0,
'NAN','NAN','NAN',
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0,0,0,
0,0,0,
217.163K,19.385K,3799,
130.13K,11.639K,2264,
129.855K,11.509K,2381,
129.691K,11.577K,2414,
130.153K,11.71K,2365,
130.322K,11.649K,2358,
129.869K,11.709K,2333,
130.288K,11.727K,2376,
130.425K,11.665K,2292,
129.805K,11.752K,2316,
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604.2103K,59.4905K,12.757K,
381.9398K,37.1091K,7699.1,
380.801K,36.6291K,8138.4,
380.7955K,36.8536K,8126.1,
382.1598K,37.5434K,8002.3,
382.7675K,37.0185K,7998.8,
381.4788K,37.3668K,7896.9,
382.0664K,37.3665K,7998.1,
382.2874K,37.2088K,7802.7,
381.1567K,37.5194K,7807.6,
51.474K,0,0,
33.887K,0,0,
34.456K,0,0,
33.334K,0,0,
34.6K,0,0,
33.797K,0,0,
33.753K,0,0,
34.267K,0,0,
33.133K,0,0,
33.645K,0,0,
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3818,330,58,
3886,323,48,
3886,313,28,
3761,275,25,
3815,251,34,
3330,224,19,
3285,206,21,
2950,162,25,
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180.449K,13.827K,2408,
108.299K,8304,1397,
107.688K,8056,1225,
106.518K,7595,923,
104.204K,7043,748,
102.329K,6876,639,
98.132K,5875,493,
93.553K,5258,313,
88.346K,4752,283,
78.103K,3547,176,
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2562,184,28,
17.072K,1683,221,
17.326K,1691,204,
17.161K,1653,213,
17.4K,1632,185,
17.187K,1691,280,
17.444K,1707,200,
17.628K,1818,219,
18.164K,1812,231,
18.093K,1913,245,
4838,494,222,
20.064K,1816,476,
20.341K,1885,488,
20.228K,1868,484,
20.127K,1910,524,
20.196K,1885,499,
20.437K,1913,524,
20.713K,2007,494,
21.016K,2050,466,
21.221K,2171,511,
1498,73,12,
10.26K,453,30,
10.617K,460,39,
10.436K,457,22,
10.616K,474,34,
10.297K,473,28,
10.713K,438,10,
10.879K,530,31,
11.051K,563,30,
11.451K,647,44,
3393,189.1,34.5,
23.9688K,1979.3,226.4,
24.7715K,2009.9,208.4,
24.6483K,1862.5,219.2,
24.9042K,1914.5,202.4,
24.5277K,2126.1,272.4,
24.9809K,1988.4,180.3,
25.197K,2145.7,224.4,
26.2835K,2163.6,265.8,
26.248K,2371.6,296.2,
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5.275,6.447,6.004,
5.422,7.468,7.99,
5.319,7.432,7.687,
5.36,7.401,8.232,
5.351,7.296,7.721,
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5.318,7.51,8.685,
5.309,7.296,8.044,
5.356,7.17,7.981,
5.195,6.97,7.771,
214.804K,18.4K,1983,
726.86K,63.337K,6026,
722.158K,63.101K,5791,
724.695K,63.279K,6022,
727.633K,63.9K,6114,
731.814K,64.465K,6453,
736.497K,65.892K,6689,
745.322K,66.644K,6485,
748.563K,67.327K,6918,
759.571K,68.398K,6478,
215.702K,23.434K,8038,
626.386K,59.286K,15.255K,
625.538K,59.475K,15.364K,
627.162K,59.853K,15.772K,
629.549K,60.2K,15.925K,
631.399K,60.604K,16.367K,
634.411K,61.394K,16.365K,
642.367K,61.936K,16.347K,
644.578K,62.294K,16.739K,
654.279K,63.394K,16.636K,
36.876K,1253,124,
188.635K,6427,356,
187.938K,6732,322,
189.24K,6845,359,
190.436K,6812,394,
190.995K,7015,328,
191.304K,6856,398,
196.941K,7177,413,
199.683K,7153,391,
205.349K,7892,441,
308.5257K,24.67K,2654,
1.0639671M,85.937K,8051.2,
1.0581993M,86.2814K,7607.1,
1.0641922M,86.3632K,8027.9,
1.069612M,87.0469K,8245,
1.0758626M,88.0614K,8551.9,
1.0807869M,90.1352K,8954.6,
1.0984365M,90.9479K,8770.7,
1.1034169M,91.6793K,9284.4,
1.1175729M,93.1294K,8555.3,
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7.588,8.467,8.466,
7.584,8.436,8.495,
7.577,8.419,8.465,
7.572,8.433,8.448,
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7.588,8.453,8.462,
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1221,189,120,
2133,404,255,
2001,397,266,
2095,405,271,
2051,416,323,
2018,362,257,
2076,377,284,
2079,419,292,
2087,385,272,
2092,355,297,
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6977.5,1181.6,632.1,
11.5573K,2517.4,1303,
11.4217K,2305.4,1545.4,
11.4396K,2435.1,1381.7,
11.3969K,2525.1,1485,
11.2935K,2136.1,1412.9,
11.3432K,2265.6,1644.5,
11.3677K,2493.7,1539.5,
11.4681K,2359.4,1584.4,
11.3936K,2101.3,1561.9,
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7.737,7.527,7.567,
7.934,7.739,7.659,
7.907,7.819,7.608,
7.922,7.809,7.666,
7.941,7.756,7.742,
7.917,7.774,7.678,
7.932,7.789,7.583,
7.93,7.789,7.678,
7.926,7.783,7.613,
7.946,7.788,7.662,
76.746K,15.175K,7956,
107.512K,24.41K,13.49K,
109.809K,24.856K,13.809K,
109.666K,24.878K,14.083K,
109.153K,25.07K,14.377K,
107.629K,25.196K,14.581K,
108.275K,24.952K,14.462K,
108.556K,25.1K,14.351K,
108.414K,24.95K,14.409K,
108.269K,24.879K,14.687K,
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400.4095K,83.1379K,51.641K,
502.8753K,123.8325K,83.1941K,
517.7734K,126.6254K,84.5093K,
519.6268K,125.6854K,85.8659K,
518.1023K,127.3453K,86.5347K,
500.6666K,125.7609K,87.5922K,
501.8805K,124.1922K,87.0267K,
503.4K,126.6K,86.827K,
503.739K,124.4569K,87.5644K,
502.4198K,124.3814K,88.2317K,
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7.845,7.737,7.453,
7.963,7.827,7.543,
7.953,7.821,7.543,
7.949,7.826,7.551,
7.948,7.825,7.557,
7.966,7.84,7.566,
7.969,7.838,7.559,
7.967,7.837,7.556,
7.966,7.84,7.544,
7.969,7.84,7.563,
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31.213K,2826,582,
18.861K,1769,301,
19.05K,1651,327,
18.842K,1726,322,
18.731K,1654,369,
18.766K,1635,343,
19.057K,1666,339,
18.744K,1606,331,
18.959K,1655,309,
18.778K,1730,355,
0,0,0,
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267.1311K,26.6437K,6117.4,
170.0705K,16.9999K,3181.2,
171.2865K,16.024K,3567.6,
170.7458K,16.8158K,3416.8,
168.2475K,16.5698K,3885.1,
168.6342K,16.0233K,3782.9,
171.9225K,16.3162K,3668.2,
170.4132K,15.9605K,3373.9,
172.0685K,16.1631K,2992.7,
169.5879K,17.1949K,3649.4,
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'NAN','NAN','NAN',
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847.785K,76.053K,15.062K,
509.272K,45.545K,8952,
509.281K,45.652K,9219,
508.323K,45.269K,9410,
508.583K,45.697K,9208,
508.018K,45.295K,8948,
508.969K,45.56K,9172,
508.248K,45.596K,9169,
508.345K,45.65K,8972,
508.824K,45.657K,9178,
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8.7503169M,849.8962K,179.4961K,
5.4971801M,524.1576K,107.2983K,
5.4897309M,524.2204K,111.4799K,
5.4852748M,520.555K,113.0743K,
5.4879325M,524.3988K,110.4937K,
5.4874435M,521.9861K,107.5848K,
5.4896408M,524.2651K,109.911K,
5.4918759M,525.8214K,109.9486K,
5.4829812M,526.8071K,107.1254K,
5.4938503M,524.1193K,110.9175K,
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'NAN','NAN','NAN',
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'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
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)168,256,148,122,369,113,820,303,0,MIDM2,30,434,710,303,0,MIDM65535,52427,65534[Scen_1,Period1][Scen_1,Parameter1]Ajo 20 descA table of different scenarios to be studied. Each row contains the values for the input variables used for the scenario.
* Kuvaus: toistetaan version 1.0.1 laskenta niin tarkkaan kuin mahdollista
* KŠytetyt oletukset
o Malliversio 1.7.8
o Scenarios, taulu 8
o Matkamatriisi, vanha mallitettu
o Joukkoliikennematriisi: 2
o From: 1001..1129
o Vehicle_noch: ['d9','d8','d4','d3','d2','d1','c9','c8','c4','c3','c2','c1','Noch']
* Ajon suoritti: Jouni
* KŠynnistettiin: 21.8.2006 16:29
* Valmistui: 22.8.2006 8:00 (ajoaika 26388 s eli 44 min per skenaario) Tulokset
* Huomautuksia: Muistia kului noin puolet eli reilu 1 GB. Aggr_period vie nyt enemmŠn aikaa mutta vŠhemmŠn muistia kuin v. 1.7.7. Solmut joita laskettiin yli 1000 s:
Aggr_period 6483.941, Etappimatkat 5441.484, Time_shift 4801.703, Trips 2318.584, All_trips 1556.698, Route_2 1250.376Table(Input_var1,Scen_1)(
1,0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6,
1,0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6,
7,7,7,7,7,7,7,7,7,7,
0,0,0,0,0,0,0,0,0,0,
2,2,2,2,2,2,2,2,2,2,
0,0,5,10,15,20,30,40,50,70,
0,0,0,0,0,0,0,0,0,0,
8,8,8,8,8,8,8,8,8,8,
4,4,4,4,4,4,4,4,4,4,
2,2,2,2,2,2,2,2,2,2,
8,8,8,8,8,8,8,8,8,8
)['Composite fraction','Guarantee level','Lim']56,256,148,122,78,192,927,328,0,MIDM2,45,69,655,554,0,MIDM52425,39321,65535[Scen_1,Input_var1]Ajo 21Table(Length1,Vehicle_type1,Zone1,Parameter1,Scen_1,Period1)(
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
6214,561,157,
6287,492,83,
6094,537,4,
6032,489,0,
6006,460,0,
6000,422,0,
5425,454,0,
5336,200,0,
5043,198,0,
5022,185,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
339.224K,21.049K,4682,
298.912K,17.608K,3764,
267.177K,14.242K,1112,
228.784K,9712,0,
160.642K,5236,0,
132.978K,4240,0,
100.735K,2642,0,
88.593K,1389,0,
74.649K,1095,0,
47.619K,561,0,
0,0,1056,
0,0,927.767,
0,0,838.8,
0,0,692.333,
0,0,553,
0,0,463.9,
0,0,346,
0,0,322.967,
0,0,242.833,
0,0,204.633,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
14.166K,1045,31,
13.383K,849,26,
12.895K,801,0,
12.412K,789,0,
11.529K,682,0,
11.13K,649,0,
10.434K,447,0,
10.442K,238,0,
10.192K,266,0,
9599,259,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
51.117K,3203,114,
48.152K,2858,74,
45.55K,2310,33,
42.483K,2154,0,
35.253K,1344,0,
30.175K,917,0,
26.543K,684,0,
24.346K,607,0,
22.382K,377,0,
16.403K,115,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
39.1K,6024,1954,
38.701K,6269,1941,
38.635K,6153,2005,
39.289K,6298,2056,
50.866K,8112,2460,
51.199K,8074,2671,
39.629K,6236,2062,
39.378K,6546,2003,
51.354K,8379,2695,
51.213K,8309,2636,
8458,758,196,
8469,874,269,
8441,853,327,
8558,859,355,
8555,903,359,
8706,893,368,
9279,895,367,
9151,1195,299,
9527,1155,340,
9506,1131,334,
7463,472,43,
7377,550,95,
7421,577,110,
7587,552,106,
7519,603,121,
7709,598,137,
8267,588,138,
8227,920,75,
8577,867,108,
8573,832,112,
11.5231K,1794.9,600.6,
11.3653K,1864.5,585.5,
11.3821K,1843.4,616.7,
11.5682K,1887.5,626.3,
14.8214K,2406.4,740.4,
14.922K,2392.7,819.7,
11.6728K,1857.1,640.5,
11.6439K,1955.2,616.1,
15.0282K,2474.9,820.2,
14.953K,2455.3,804.8,
774,368.034,10.825K,
737,378.586,10.835K,
819,385.897,11.054K,
821,395.724,11.004K,
927,402.862,11.228K,
973,400.862,11.408K,
903,410.034,11.45K,
877,410.448,11.591K,
815,419.759,11.565K,
947,426.483,11.67K,
8.875,9.358,9.239,
8.876,9.301,9.076,
8.868,9.248,9.031,
8.864,9.292,9.084,
9.322,9.552,9.186,
9.307,9.539,9.134,
8.771,9.255,8.977,
8.749,8.995,9.148,
9.197,9.38,9.251,
9.188,9.392,9.231,
77.306K,8708,1662,
78.034K,8746,1724,
78.413K,9093,1696,
79.302K,9224,1830,
95.813K,11.08K,2276,
97.026K,11.392K,2349,
81.246K,9716,1626,
81.637K,9774,1745,
98.25K,11.849K,2231,
98.197K,11.832K,2186,
38.074K,3760,1186,
38.972K,3817,1234,
39.58K,4074,1257,
40.125K,4045,1282,
40.6K,4072,1252,
41.271K,4242,1277,
41.636K,4408,1176,
42.141K,4621,1243,
42.363K,4608,1275,
42.36K,4637,1282,
22.004K,983,93,
23.227K,1128,104,
23.861K,1126,117,
24.343K,1205,79,
24.77K,1250,159,
25.358K,1400,128,
25.666K,1500,123,
26.209K,1691,133,
26.678K,1694,113,
26.545K,1731,109,
31.1063K,3377.2,623,
31.6802K,3401.7,648.4,
31.933K,3528.5,652.4,
32.2311K,3610.2,694.9,
37.8074K,4263.7,872.5,
38.2293K,4372.1,902.3,
33.0111K,3846.3,626.1,
33.2698K,3870,687.6,
38.7674K,4590.3,834.5,
38.8291K,4546.4,830.7,
1863,0,0,
1807,0,0,
1964,0,0,
1773,0,0,
1893,0,0,
1965,0,0,
1919,0,0,
2048,0,0,
1940,0,0,
2040,0,0,
7.806,8.725,8.713,
7.712,8.612,8.65,
7.677,8.671,8.601,
7.668,8.612,8.751,
8.109,8.853,8.652,
8.096,8.773,8.752,
7.599,8.467,8.588,
7.569,8.35,8.54,
8.023,8.67,8.806,
8.034,8.64,8.813,
415.552K,44.969K,5924,
420.113K,45.076K,6091,
424.334K,45.796K,5831,
428.757K,46.451K,6241,
420.772K,45.198K,5387,
430.876K,45.736K,5678,
450.875K,48.092K,6051,
454.746K,48.464K,6088,
437.689K,46.513K,5805,
446.353K,46.416K,5475,
180.922K,18.064K,5307,
183.235K,18.388K,5450,
185.961K,18.749K,5383,
189.198K,19.233K,5388,
195.49K,20.089K,5410,
201.526K,20.45K,5378,
204.208K,20.412K,5507,
205.795K,20.509K,5379,
208.161K,20.721K,5515,
214.573K,21.178K,5453,
74.679K,3117,315,
76.98K,3243,363,
78.793K,3339,328,
81.146K,3700,281,
86.821K,4230,324,
91.612K,4553,277,
93.632K,4515,352,
94.07K,4445,321,
96.354K,4700,301,
102.431K,5224,251,
187.7983K,19.8258K,2791.1,
189.987K,19.9721K,2907.6,
192.2402K,20.2112K,2732.2,
194.3214K,20.5547K,2953.3,
193.0897K,20.329K,2583.4,
197.4664K,20.6275K,2722.4,
205.0354K,21.4863K,2817.1,
206.2671K,21.3834K,2900,
200.9107K,20.9828K,2744.3,
205.0422K,20.8289K,2622.3,
9361,0,0,
9221,0,0,
9439,0,0,
9505,0,0,
9665,0,0,
9902,0,0,
9757,0,0,
9960,0,0,
9953,0,0,
9851,0,0,
8.272,8.775,8.572,
8.248,8.761,8.533,
8.232,8.754,8.579,
8.21,8.713,8.631,
8.063,8.591,8.528,
8.032,8.558,8.6,
8.105,8.632,8.57,
8.114,8.645,8.589,
7.986,8.548,8.583,
7.928,8.492,8.628,
1403,429,451,
1404,408,428,
1434,372,471,
1365,398,494,
1355,389,513,
1377,379,470,
1447,403,478,
1287,421,486,
1461,373,444,
1421,406,454,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
1257,380.5,434.3,
1264.5,368.9,455.9,
1258.6,356.8,504.1,
1230.5,365.3,509.5,
1245.1,352.3,530.2,
1224.7,365.7,475.6,
1313.3,383.3,486.9,
1179.8,368,479.6,
1284.6,339.2,472.9,
1259,362.1,464.9,
248,138.414,4026,
212,136.138,4292,
263,137.69,4214,
244,140.724,4249,
423,138.138,4354,
210,138.552,4228,
245,136.276,4239,
226,136.966,4212,
364,140.241,4308,
257,140.793,4335,
8.134,8.073,7.945,
8.141,8.047,7.864,
8.17,8.05,7.837,
8.141,8.057,7.867,
8.126,8.046,7.888,
8.157,8.01,7.938,
8.136,8.03,7.891,
8.117,8.148,7.906,
8.167,8.061,7.848,
8.168,8.112,7.912,
8426,1841,1411,
8199,1834,1349,
8465,1948,1473,
8426,1817,1419,
8562,1922,1463,
8586,1826,1390,
8086,1835,1446,
8416,1889,1401,
8501,1909,1498,
8573,1889,1480,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
10.2248K,2529.5,2167.5,
10.0305K,2523.3,2044.3,
10.216K,2603.1,2170.1,
10.2566K,2431.2,2212.8,
10.2113K,2538.2,2230.5,
10.2855K,2517.4,2193.8,
9909.1,2432,2151.5,
10.1199K,2471.9,2129.3,
10.2231K,2482.7,2227.7,
10.1666K,2502.2,2171.6,
614,0,0,
707,0,0,
577,0,0,
578,0,0,
596,0,0,
622,0,0,
648,0,0,
574,0,0,
712,0,0,
656,0,0,
7.967,7.832,7.684,
7.961,7.834,7.707,
7.97,7.843,7.718,
7.96,7.859,7.665,
7.99,7.851,7.699,
7.984,7.805,7.646,
7.959,7.851,7.724,
7.98,7.872,7.709,
7.975,7.872,7.717,
7.987,7.867,7.723,
48.106K,11.689K,8798,
47.763K,11.857K,8946,
48.156K,11.756K,9250,
48.009K,11.67K,8766,
48.742K,11.958K,9081,
49.08K,12.056K,9016,
48.077K,12.015K,9091,
47.765K,11.745K,8789,
49.465K,11.673K,9101,
48.64K,12.359K,9399,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
67.7212K,18.0522K,16.4199K,
67.537K,17.8307K,16.7016K,
67.9744K,17.7237K,17.1002K,
67.9884K,17.6918K,16.6117K,
68.3293K,18.1288K,17.1363K,
69.1217K,18.2551K,16.9087K,
67.7933K,17.9798K,16.8724K,
67.7314K,17.8128K,16.402K,
69.0896K,17.9734K,17.017K,
68.6607K,18.7023K,17.2967K,
4357,0,0,
4475,0,0,
4437,0,0,
4603,0,0,
4542,0,0,
4619,0,0,
4551,0,0,
4445,0,0,
4570,0,0,
4740,0,0,
8.001,7.881,7.638,
8.001,7.905,7.638,
8.002,7.904,7.649,
8.002,7.895,7.626,
8.002,7.89,7.625,
8.004,7.896,7.632,
8.008,7.91,7.649,
8.006,7.901,7.636,
8.013,7.876,7.642,
8.004,7.897,7.654,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
6635,563,133,
6575,607,99,
6550,562,121,
6585,607,115,
6679,598,102,
6622,640,128,
6616,599,113,
6577,576,111,
6738,644,114,
6649,592,113,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
10.5083K,1009.1,268,
10.4937K,1069.6,201.8,
10.4965K,998.1,232.9,
10.4967K,1057.9,238.1,
10.6139K,1052.1,219.1,
10.5135K,1112.3,260.3,
10.5337K,1040.7,250.4,
10.5093K,1008.8,229.3,
10.722K,1132.8,230.8,
10.5814K,1045.4,235.6,
1913,1489.552,38.852K,
2004,1487.931,39.091K,
1918,1479.793,39.447K,
2019,1503.897,38.932K,
1811,1483.793,39.328K,
1761,1491.241,38.88K,
1913,1473.621,38.98K,
1964,1448.586,39.163K,
1940,1499.034,38.768K,
2107,1486.034,38.901K,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
27.379K,2553,497,
27.518K,2462,517,
27.663K,2403,499,
27.676K,2361,459,
27.481K,2521,510,
27.412K,2465,503,
27.387K,2559,469,
27.472K,2499,487,
27.473K,2438,546,
27.401K,2405,476,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
66.7516K,6878.4,1443.6,
66.5797K,6721.2,1467.4,
67.3347K,6502.8,1494.4,
67.5215K,6403.8,1369.3,
66.8191K,6753.1,1508.1,
66.9414K,6667.4,1496.4,
66.5091K,6817.6,1383.5,
66.9337K,6717.2,1437.2,
66.7025K,6558.1,1575.6,
66.8318K,6479,1414,
7091,0,0,
7410,0,0,
7658,0,0,
7288,0,0,
7646,0,0,
7305,0,0,
7561,0,0,
7044,0,0,
7551,0,0,
7212,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
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18.464K,1907,267,
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0,0,0,
0,0,0,
0,0,0,
0,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN'
)168,280,148,122,48,184,476,2242,104,114,820,303,0,MIDM2,30,434,710,303,0,MIDM65535,52427,65534[Scen_1,Period1][Scen_1,Parameter1]Ajo 21 descAjo 21
* Kuvaus: KakkoskŠsikirjoitukseen tulevia tuloksia
* KŠytetyt oletukset
o Malliversio 1.7.8
o Scenarios, taulu 17
o Matkamatriisi, HLT
o Joukkoliikennematriisi, 3
o From: 1001..1129
o Vehicle_noch: ['d9','d8','d7','d6','d5','d4','d3','d2','d1','c9','c8','c7','c6','c5','c4','c3','c2','c1','Noch']
* Kuka otti ajaakseen: Juha
* Milloin ajo kŠynnistettiin: 24.08.2006 KLO 16:00
* Milloin ajo valmistui: 25.08.2006 KLO 7:40 oli jo loppunut. Tulokset
* HuomautuksiaTable(Input_var1,Scen_1)(
0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6,
0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6,
7,7,7,7,7,7,7,7,7,7,
0,0,0,0,0,0,0,0,0,0,
2,2,2,2,2,2,2,2,2,2,
80,100,120,150,200,250,300,350,400,500,
0,0,0,0,0,0,0,0,0,0,
8,8,8,8,8,8,8,8,8,8,
4,4,4,4,4,4,4,4,4,4,
2,2,2,2,2,2,2,2,2,2,
8,8,8,8,8,8,8,8,8,8
)['Composite fraction','Guarantee level','Lim']56,280,148,122,102,90,476,4972,319,378,927,328,0,MIDM2,45,69,655,554,0,MIDM52425,39321,65535[Scen_1,Input_var1]Ajo['11: 1.0.1 toisinto','12: Perus; car_fr a','13: Perus; car_fr b','14: Perus; Pub_fr a','15: Perus; Pub_fr b','16: Perus; Drop p a','17: Perus; Drop p b','18: Perus; Max-min a','19: Perus; Max-min b','20: Perus; Pub_level a','21: Perus; Pub_level b']504,232,148,12Descarray(ajo,[
Ajo_11_desc,
Ajo_12_desc,
Ajo_13_desc,
Ajo_14_desc,
Ajo_15_desc,
Ajo_16_desc,
Ajo_17_desc,
Ajo_18_desc,
Ajo_19_desc,
Ajo_20_desc,
Ajo_21_desc])56,336,148,242,385,32,868,303,0,MIDM[Scen_1,Ajo][Input_var1,5,Ajo,1,Scen_1,1]Ajotarray(ajo,[
Ajo_11,
Ajo_12,
Ajo_13,
Ajo_14,
Ajo_15,
Ajo_16,
Ajo_17,
Ajo_18,
Ajo_19,
Ajo_20,
Ajo_21])168,336,148,242,152,162,868,303,0,MIDM[Scen_1,Ajo]Erkki's testsjtue12. syyta 2008 16:09 48,24368,344,148,24ERAC-palvelinkoeTable(Parameter1,Zone1,Vehicle_type1,Length1,Scen_1,Period1)(
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
7607,897,449,
13.73K,1570,550,
19.07K,2237,734,
23.18K,2632,791,
26.85K,3373,886,
29.04K,3913,1000,
30.84K,4286,1036,
33.37K,4566,1241,
34.7K,5078,1339,
35.74K,5360,1381,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
1346,324,315,
1335,302,327,
1383,334,319,
1439,383,351,
1285,354,431,
1431,369,406,
1468,390,433,
1416,462,420,
1358,383,421,
1472,415,466,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
24.6K,2552,506,
30.56K,3201,582,
38.61K,4023,694,
46.49K,4870,743,
53.55K,5537,1058,
60.16K,6225,1064,
66.71K,6977,1258,
73.58K,7537,1309,
78.81K,8586,1317,
84.67K,9390,1539,
2564,192,15,
6187,487,60,
9121,826,124,
12.03K,1073,127,
14.25K,1346,163,
16.85K,1698,197,
19.32K,1975,254,
21.71K,2065,336,
24.84K,2295,343,
27.47K,2427,413,
6309,1320,903,
6557,1266,920,
7330,1389,1001,
7561,1494,1042,
7986,1592,1084,
8210,1577,1141,
8279,1629,1200,
8398,1759,1341,
8397,1785,1394,
8486,1834,1372,
1235,203,133,
1524,273,163,
1705,292,167,
1825,310,225,
1965,322,233,
1979,346,255,
2071,377,277,
2097,399,296,
1955,423,304,
2021,458,278,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
123.2K,12.77K,1795,
178.8K,17.92K,2499,
232.2K,23.25K,3013,
278.4K,28.39K,3603,
324.2K,33.11K,4071,
367.3K,38.47K,4666,
409.2K,43.92K,5500,
447.4K,48.2K,6291,
484.9K,53.33K,7279,
520.6K,57.98K,7698,
215.3K,18.39K,1975,
324.5K,26.99K,2716,
438K,36.44K,3512,
557.1K,48K,4227,
663.3K,59.36K,5263,
768.6K,69.56K,6743,
868.6K,79.75K,7611,
971.8K,89.92K,8806,
1.066M,99.91K,10.09K,
1.163M,108.9K,11.52K,
31.94K,7606,5015,
40.28K,9261,5721,
43.76K,10.17K,6316,
45.82K,10.49K,6706,
46.84K,11.14K,7219,
47.87K,11.25K,7603,
48.22K,11.73K,8158,
48.9K,12.02K,8617,
48.34K,12.04K,8826,
49.12K,12.15K,9224,
76.29K,14.92K,7984,
91.62K,18.5K,9758,
97.64K,21.36K,11.02K,
96.92K,21.67K,12.46K,
98.99K,22.72K,13.3K,
101.1K,23.39K,14.02K,
101.9K,24.51K,14.78K,
103.5K,24.99K,16.2K,
105.8K,25.97K,16.74K,
107.7K,26.68K,17.37K,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
10.27K,983,276,
10.4K,951,285,
10.17K,954,234,
10.21K,924,296,
10.32K,954,241,
10.25K,913,238,
10.2K,941,285,
10.25K,968,269,
10.24K,953,310,
10.32K,961,269,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
1092,93,12,
2183,209,36,
3291,261,58,
4314,397,81,
5570,523,97,
6548,583,116,
7707,675,132,
8810,822,150,
9806,876,181,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
11.13K,991,194,
9949,889,178,
9108,774,158,
7728,653,129,
6798,589,121,
5489,501,98,
4464,392,81,
3268,288,54,
2156,176,39,
1076,116,19,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
27.42K,2488,494,
27.12K,2509,418,
27.52K,2519,437,
27.22K,2520,467,
27.05K,2557,426,
27.61K,2483,446,
27.33K,2548,414,
27.46K,2515,441,
27.55K,2529,455,
27.17K,2516,455,
6743,554,116,
6757,608,109,
6664,553,94,
6602,592,96,
6596,580,117,
6627,542,110,
6596,552,120,
6641,590,96,
6745,571,101,
6618,543,97,
6833,716,490,
11.24K,1093,562,
15.86K,1497,641,
20.46K,1919,656,
25.07K,2299,841,
29.57K,2613,891,
33.89K,2980,975,
38.93K,3469,1083,
42.95K,3933,1138,
47.82K,4363,1167,
4816,494,187,
7999,773,254,
11.08K,1043,326,
14.37K,1301,363,
17.45K,1642,439,
20.58K,1969,525,
23.82K,2226,534,
26.79K,2516,624,
30.1K,2759,640,
32.93K,2930,700,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
45.48K,4059,799,
41.12K,3664,722,
36.8K,3212,620,
31.85K,2850,562,
27.32K,2438,503,
22.81K,2046,420,
18.09K,1684,319,
13.57K,1200,242,
9093,774,180,
4604,360,92,
31.31K,2938,614,
28.1K,2452,504,
25.18K,2300,446,
21.66K,1959,403,
18.84K,1720,346,
15.56K,1389,260,
12.59K,1177,216,
9389,823,157,
6264,570,104,
3181,282,54,
102.6K,8714,1982,
101.9K,8679,1805,
102.1K,8428,1862,
102.7K,8490,1916,
102.6K,8365,1922,
102.1K,8496,1964,
102.3K,8360,1925,
101.9K,8439,1955,
102K,8497,1909,
102.1K,8515,1924,
180.3K,13.98K,2457,
180.4K,13.65K,2404,
179.5K,13.91K,2423,
180.3K,13.58K,2370,
180.3K,13.73K,2544,
180.1K,14.02K,2453,
179.8K,13.8K,2369,
180.9K,13.68K,2553,
180K,13.68K,2499,
180.4K,13.66K,2478,
42.39K,5023,1975,
64.45K,7120,2449,
85.52K,8958,2859,
107.4K,10.94K,3225,
128.6K,12.8K,3546,
150.2K,14.75K,3889,
172.7K,16.76K,4418,
194.1K,18.68K,4738,
215.5K,20.79K,5100,
238.1K,22.6K,5489,
215.4K,23.14K,8061,
298.8K,30.48K,9849,
384.4K,38.46K,11.33K,
469K,45.53K,12.83K,
553K,53.72K,14.24K,
638.6K,61.01K,15.85K,
722.5K,68.97K,17.12K,
809.1K,76.55K,19.03K,
892.7K,84.49K,20.49K,
977.3K,91.83K,21.91K,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
216.9K,19.3K,3911,
195.3K,17.34K,3499,
173.6K,15.49K,3098,
151.5K,13.64K,2810,
130.2K,11.82K,2383,
108.3K,9685,1945,
87.11K,7800,1572,
65K,5660,1114,
43.32K,3931,766,
21.85K,1929,390,
847.9K,75.88K,15.19K,
763.7K,68.17K,13.4K,
678.5K,60.9K,12.02K,
593.8K,53.24K,10.77K,
508.6K,45.89K,9041,
423.8K,37.83K,7521,
338.5K,30.16K,6070,
254.5K,22.84K,4570,
169.6K,15.37K,3063,
84.63K,7627,1511,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
292,0,0,
1172,52,0,
2210,73,4,
3185,181,4,
4457,285,5,
5496,309,19,
6612,421,25,
7832,565,43,
8834,560,25,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
2601,138,41,
3178,147,28,
5058,202,49,
7646,299,36,
10.93K,401,60,
14.49K,542,65,
18.24K,681,105,
22.56K,970,103,
26.32K,1269,87,
31.35K,1478,99,
1498,96,5,
2085,98,10,
3686,188,16,
5975,203,29,
8020,356,39,
10.51K,569,34,
13.15K,677,36,
15.57K,813,50,
18.44K,1022,63,
21.23K,1114,111,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
10.38K,509,84,
13.86K,658,112,
20.16K,811,109,
29.48K,1183,144,
40.82K,1757,156,
54.47K,2277,197,
70.45K,2899,239,
86.2K,3779,272,
103.5K,4561,323,
121.5K,5423,351,
36.32K,1249,109,
49.61K,1641,107,
72.59K,2441,193,
104.8K,3607,227,
145K,5340,250,
192.7K,6869,441,
244.2K,9109,516,
300.1K,12.23K,583,
358K,14.9K,816,
418.2K,17.85K,923,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
880.7K,119.9K,93.99K,
869.5K,120.1K,95.54K,
872.7K,121.4K,92.33K,
873.2K,121K,91.03K,
871.4K,119.4K,93.22K,
870.9K,121.9K,92K,
867.7K,121.2K,90.35K,
864.3K,119.6K,94.2K,
872.2K,119.8K,92.33K,
870.6K,120.3K,94.56K,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
2381,294.7,149.5,
4114,478.2,177.8,
5743,689.3,238.4,
6941,797.9,259.8,
8033,1033,282.9,
8665,1189,326.7,
9191,1291,335.9,
9929,1381,401.6,
10.34K,1538,431.4,
10.61K,1605,435.2,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
1251,345.8,380.6,
1286,309.9,371.6,
1285,335.9,404.4,
1315,368.3,408.6,
1227,357.9,464.5,
1288,340.1,443.3,
1296,367.9,482.8,
1243,412,451.1,
1234,379.4,449.9,
1267,394,471.3,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
17.22K,1658,393.3,
15.48K,1501,345.3,
14.24K,1324,338.9,
12.11K,1134,265.8,
10.77K,1057,250,
8898,885.3,212,
7326,718.2,174.9,
5565,553.2,116,
3807,356.3,82.8,
2066,243.7,41.3,
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0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
9456,964.9,218.4,
11.57K,1246,232,
14.8K,1519,270.4,
18.12K,1860,290,
21.08K,2138,434.4,
23.99K,2383,428.1,
26.84K,2712,512.7,
29.59K,2942,511,
31.94K,3370,488.5,
34.55K,3724,608.2,
3242,242.6,21.9,
7127,542.1,69.4,
11.24K,920.2,126.8,
15.62K,1140,144.1,
18.95K,1520,173.7,
23.43K,1967,204.5,
27.67K,2276,282.4,
31.21K,2421,339.3,
36.61K,2748,384.9,
41.6K,3060,488.2,
8365,1893,1501,
8962,1902,1502,
9699,2098,1606,
9788,2173,1674,
10.07K,2190,1782,
10.07K,2276,1867,
10.19K,2265,1917,
10.13K,2421,2098,
9968,2459,2184,
10.11K,2478,2136,
6838,1216,739.1,
9171,1539,883.3,
9782,1626,880.7,
10.09K,1793,1135,
10.68K,2008,1147,
10.51K,2000,1212,
10.84K,2156,1279,
10.91K,2352,1440,
10.66K,2523,1493,
10.75K,2539,1535,
104.8K,10.27K,2339,
95.79K,9538,2093,
86.96K,8453,1799,
76.39K,7620,1653,
66.67K,6534,1463,
56.41K,5589,1244,
45.97K,4679,928.6,
35.55K,3413,717.8,
24.8K,2243,517,
13.15K,1072,276,
269.4K,27.84K,6192,
243.2K,23.38K,5496,
219.6K,22.29K,4762,
192.5K,19.21K,4317,
170.3K,16.78K,3806,
143K,14.06K,2628,
119K,11.99K,2262,
90.63K,8241,1690,
62.27K,5867,1219,
32.84K,3165,573.8,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
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0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
56.99K,5965,855.1,
81.22K,8210,1199,
104.9K,10.57K,1469,
124.5K,12.64K,1697,
145.7K,14.86K,1900,
165.5K,17.12K,2166,
185.7K,19.45K,2599,
203.5K,21.46K,2913,
221.2K,23.65K,3417,
237.8K,25.71K,3596,
308.9K,24.88K,2663,
458.5K,36.49K,3632,
620.9K,49K,4666,
796.9K,65.08K,5667,
961.6K,80.57K,7062,
1.13M,94.88K,9005,
1.294M,109.8K,10.3K,
1.461M,124.9K,12K,
1.617M,139.9K,13.68K,
1.773M,152.6K,15.62K,
49.02K,12.73K,10.3K,
62.4K,14.99K,11.62K,
66.26K,16.29K,12.76K,
67.79K,16.52K,13.18K,
67.97K,17.33K,14.11K,
69.03K,17.59K,14.5K,
68.88K,17.99K,15.37K,
69.01K,18.1K,16.21K,
68.09K,18.14K,16.42K,
68.7K,18.12K,16.93K,
399.3K,83.56K,51.84K,
450.9K,97.86K,62.14K,
472.6K,109.7K,68.14K,
453.7K,108K,75.6K,
457.6K,113K,79.6K,
461.3K,114.6K,83.58K,
462.4K,118K,88K,
466.2K,119.4K,93.8K,
477.1K,122.8K,97.38K,
479K,126.7K,99.22K,
603.8K,59.14K,13.02K,
549.1K,53.78K,11.74K,
494.4K,48.46K,10.42K,
437.9K,43.05K,9522,
380.8K,37.55K,8118,
323.3K,31.33K,6665,
266.1K,25.61K,5315,
203.6K,18.63K,3787,
139.6K,13.26K,2592,
72.99K,6564,1355,
8.745M,847.6K,180.9K,
7.954M,765.7K,160.6K,
7.15M,689.5K,144K,
6.328M,607.6K,130.7K,
5.487M,530.5K,109.2K,
4.648M,438.2K,90.59K,
3.764M,352.3K,72.75K,
2.891M,271.9K,55.64K,
1.97M,184.3K,37.36K,
1.01M,91.66K,18.5K,
0,0,2404,
0,0,2319,
0,0,2346,
0,0,2401,
0,0,2393,
0,0,2372,
0,0,2378,
0,0,2307,
0,0,2352,
0,0,2407,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
232,140.4,3851,
347,178.1,5095,
429,214.7,6391,
605,266.1,7692,
508,304.5,8951,
606,342,10.31K,
695,382.8,11.58K,
756,430.7,12.73K,
857,469.9,13.92K,
891,511.4,15.09K,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
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225,138,3534,
393,139.8,4183,
283,141,4187,
324,129.3,3939,
264,131.1,3964,
222,128.3,4133,
202,129.5,4092,
349,128.9,4096,
297,122.4,4093,
321,121.5,4181,
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0,0,0,
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0,0,0,
0,0,0,
0,0,0,
2903,2361,60.82K,
2649,2162,55.16K,
2370,1927,49.99K,
2473,1705,44.61K,
1701,1486,39.11K,
1672,1231,33.36K,
1386,998.7,27.71K,
1211,755.2,21.87K,
761,532.7,15.73K,
442,258,8955,
0,0,0,
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674,0,0,
877,0,0,
1041,0,0,
1283,0,0,
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1882,0,0,
2031,0,0,
2573,0,0,
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666,0,0,
612,0,0,
712,0,0,
633,0,0,
717,0,0,
699,0,0,
634,0,0,
686,0,0,
643,0,0,
607,0,0,
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10.87K,0,0,
10.46K,0,0,
9577,0,0,
8130,0,0,
7444,0,0,
5995,0,0,
5256,0,0,
4151,0,0,
3323,0,0,
2036,0,0,
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3592,0,0,
4780,0,0,
5787,0,0,
6849,0,0,
7883,0,0,
9114,0,0,
10.03K,0,0,
11.16K,0,0,
11.96K,0,0,
13K,0,0,
0,0,0,
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3764,0,0,
4397,0,0,
4302,0,0,
4169,0,0,
4069,0,0,
4192,0,0,
4341,0,0,
4303,0,0,
4435,0,0,
4419,0,0,
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0,0,0,
0,0,0,
52.14K,0,0,
47.23K,0,0,
42.39K,0,0,
38.29K,0,0,
34.32K,0,0,
29.11K,0,0,
24.57K,0,0,
19.99K,0,0,
14.49K,0,0,
8674,0,0,
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'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
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'NAN','NAN','NAN',
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9.268,9.055,9.075,
9.442,9.249,9.094,
9.339,9.267,9.07,
9.216,9.31,9.129,
9.119,9.247,9.175,
8.943,9.189,9.14,
8.822,9.218,9.116,
8.727,9.141,9.118,
8.57,9.043,9.088,
8.458,9.103,9.217,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
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'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
8.027,7.888,7.76,
8.002,7.919,7.812,
8.05,7.935,7.716,
8.102,7.97,7.764,
8.045,7.946,7.849,
8.127,8.025,7.848,
8.141,8.039,7.82,
8.145,8.098,7.848,
8.11,8.016,7.851,
8.209,8.026,7.887,
'NAN','NAN','NAN',
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'NAN','NAN','NAN',
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'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
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'NAN','NAN','NAN',
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'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
8.681,8.863,8.385,
8.771,8.905,8.562,
8.64,8.948,8.49,
8.451,8.889,8.624,
8.23,8.861,8.598,
8.009,8.797,8.579,
7.819,8.753,8.453,
7.621,8.569,8.569,
7.456,8.46,8.641,
7.232,8.428,8.626,
5.296,5.654,6.509,
7.018,7.68,7.675,
6.634,7.593,7.943,
6.062,7.873,7.328,
5.661,7.424,7.367,
5.283,6.996,7.843,
4.918,7.003,7.993,
4.718,6.677,8.012,
4.577,6.359,7.729,
4.394,6.278,7.233,
7.88,7.799,7.603,
7.843,7.748,7.648,
7.873,7.756,7.664,
7.888,7.769,7.638,
7.919,7.828,7.632,
7.944,7.792,7.63,
7.95,7.81,7.639,
7.969,7.819,7.677,
7.981,7.845,7.671,
7.992,7.847,7.681,
7.759,7.556,7.479,
7.758,7.709,7.528,
7.818,7.742,7.548,
7.881,7.711,7.572,
7.9,7.688,7.617,
7.916,7.771,7.651,
7.957,7.78,7.659,
7.952,7.769,7.673,
7.925,7.781,7.705,
7.938,7.828,7.592,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
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8.624,8.737,8.542,
8.726,8.814,8.569,
8.723,8.855,8.63,
8.665,8.866,8.628,
8.569,8.824,8.645,
8.456,8.816,8.675,
8.323,8.812,8.642,
8.212,8.749,8.685,
8.1,8.724,8.68,
7.987,8.693,8.687,
8.002,8.533,8.441,
8.145,8.6,8.572,
8.113,8.605,8.488,
8.019,8.585,8.489,
7.852,8.522,8.549,
7.669,8.498,8.451,
7.489,8.422,8.449,
7.328,8.294,8.468,
7.165,8.235,8.386,
7.025,8.151,8.408,
7.902,7.79,7.555,
7.897,7.822,7.57,
7.923,7.839,7.574,
7.943,7.852,7.592,
7.962,7.865,7.603,
7.976,7.866,7.62,
7.99,7.886,7.64,
8.007,7.905,7.639,
8.012,7.911,7.653,
8.023,7.923,7.666,
7.842,7.719,7.46,
7.874,7.755,7.488,
7.9,7.79,7.519,
7.934,7.813,7.544,
7.958,7.825,7.555,
7.973,7.847,7.565,
7.987,7.866,7.568,
7.998,7.877,7.599,
8.005,7.888,7.6,
8.018,7.899,7.622,
'NAN','NAN','NAN',
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'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN'
)88,112,164,242,359,406,820,303,0,MIDM2,30,434,710,303,0,MIDM65535,52427,65534[Scen_1,Period1][Scen_1,Parameter1]Ajo 25-koeTable(Length1,Vehicle_type1,Zone1,Parameter1,Scen_1,Period1)(
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
10.266K,983,276,
10.398K,951,285,
10.171K,954,234,
10.21K,924,296,
10.324K,954,241,
10.252K,913,238,
10.196K,941,285,
10.25K,968,269,
10.236K,953,310,
10.315K,961,269,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
880.727K,119.867K,93.992K,
869.506K,120.126K,95.536K,
872.715K,121.354K,92.328K,
873.161K,120.974K,91.032K,
871.402K,119.394K,93.224K,
870.866K,121.867K,92K,
867.721K,121.207K,90.348K,
864.292K,119.595K,94.2K,
872.246K,119.774K,92.328K,
870.583K,120.281K,94.56K,
0,0,2403.8,
0,0,2318.767,
0,0,2346.267,
0,0,2401.167,
0,0,2393.367,
0,0,2372.233,
0,0,2378.167,
0,0,2306.667,
0,0,2352.433,
0,0,2406.5,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
27.42K,2488,494,
27.12K,2509,418,
27.523K,2519,437,
27.219K,2520,467,
27.05K,2557,426,
27.61K,2483,446,
27.33K,2548,414,
27.455K,2515,441,
27.548K,2529,455,
27.166K,2516,455,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
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0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
102.57K,8714,1982,
101.919K,8679,1805,
102.125K,8428,1862,
102.716K,8490,1916,
102.628K,8365,1922,
102.142K,8496,1964,
102.272K,8360,1925,
101.89K,8439,1955,
102.041K,8497,1909,
102.12K,8515,1924,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
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0,0,0,
0,0,0,
0,0,0,
0,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
7607,897,449,
13.73K,1570,550,
19.068K,2237,734,
23.182K,2632,791,
26.853K,3373,886,
29.038K,3913,1000,
30.843K,4286,1036,
33.372K,4566,1241,
34.7K,5078,1339,
35.743K,5360,1381,
0,0,0,
1092,93,12,
2183,209,36,
3291,261,58,
4314,397,81,
5570,523,97,
6548,583,116,
7707,675,132,
8810,822,150,
9806,876,181,
0,0,0,
292,0,0,
1172,52,0,
2210,73,4,
3185,181,4,
4457,285,5,
5496,309,19,
6612,421,25,
7832,565,43,
8834,560,25,
2381.1,294.7,149.5,
4113.5,478.2,177.8,
5743.1,689.3,238.4,
6940.9,797.9,259.8,
8033.1,1032.5,282.9,
8664.9,1189.3,326.7,
9191.4,1290.9,335.9,
9929.4,1381.4,401.6,
10.3381K,1538.4,431.4,
10.6093K,1604.8,435.2,
232,140.448,3851,
347,178.138,5095,
429,214.69,6391,
605,266.103,7692,
508,304.483,8951,
606,342,10.305K,
695,382.759,11.579K,
756,430.69,12.725K,
857,469.897,13.916K,
891,511.448,15.086K,
9.268,9.055,9.075,
9.442,9.249,9.094,
9.339,9.267,9.07,
9.216,9.31,9.129,
9.119,9.247,9.175,
8.943,9.189,9.14,
8.822,9.218,9.116,
8.727,9.141,9.118,
8.57,9.043,9.088,
8.458,9.103,9.217,
24.604K,2552,506,
30.561K,3201,582,
38.609K,4023,694,
46.493K,4870,743,
53.547K,5537,1058,
60.156K,6225,1064,
66.714K,6977,1258,
73.583K,7537,1309,
78.807K,8586,1317,
84.671K,9390,1539,
6833,716,490,
11.244K,1093,562,
15.857K,1497,641,
20.458K,1919,656,
25.074K,2299,841,
29.565K,2613,891,
33.888K,2980,975,
38.926K,3469,1083,
42.951K,3933,1138,
47.816K,4363,1167,
2601,138,41,
3178,147,28,
5058,202,49,
7646,299,36,
10.933K,401,60,
14.494K,542,65,
18.24K,681,105,
22.56K,970,103,
26.319K,1269,87,
31.35K,1478,99,
9455.7,964.9,218.4,
11.5725K,1246,232,
14.8009K,1518.9,270.4,
18.1178K,1860.4,290,
21.0811K,2137.8,434.4,
23.9874K,2382.9,428.1,
26.8434K,2711.7,512.7,
29.5854K,2941.6,511,
31.9444K,3369.8,488.5,
34.5544K,3724.5,608.2,
674,0,0,
877,0,0,
1041,0,0,
1283,0,0,
1579,0,0,
1707,0,0,
1882,0,0,
2031,0,0,
2573,0,0,
2701,0,0,
8.681,8.863,8.385,
8.771,8.905,8.562,
8.64,8.948,8.49,
8.451,8.889,8.624,
8.23,8.861,8.598,
8.009,8.797,8.579,
7.819,8.753,8.453,
7.621,8.569,8.569,
7.456,8.46,8.641,
7.232,8.428,8.626,
123.181K,12.774K,1795,
178.755K,17.917K,2499,
232.225K,23.25K,3013,
278.429K,28.387K,3603,
324.244K,33.112K,4071,
367.345K,38.471K,4666,
409.163K,43.918K,5500,
447.425K,48.199K,6291,
484.909K,53.331K,7279,
520.645K,57.981K,7698,
42.386K,5023,1975,
64.446K,7120,2449,
85.516K,8958,2859,
107.399K,10.944K,3225,
128.618K,12.8K,3546,
150.24K,14.753K,3889,
172.692K,16.761K,4418,
194.136K,18.677K,4738,
215.516K,20.785K,5100,
238.064K,22.6K,5489,
10.382K,509,84,
13.862K,658,112,
20.155K,811,109,
29.484K,1183,144,
40.82K,1757,156,
54.474K,2277,197,
70.454K,2899,239,
86.204K,3779,272,
103.455K,4561,323,
121.524K,5423,351,
56.9894K,5964.8,855.1,
81.2176K,8209.8,1198.7,
104.8814K,10.5701K,1468.9,
124.5071K,12.638K,1697.2,
145.7249K,14.8602K,1899.6,
165.4836K,17.1238K,2165.6,
185.6896K,19.4538K,2598.6,
203.4662K,21.4551K,2913,
221.1515K,23.6453K,3417,
237.8124K,25.7088K,3596.1,
3592,0,0,
4780,0,0,
5787,0,0,
6849,0,0,
7883,0,0,
9114,0,0,
10.033K,0,0,
11.163K,0,0,
11.959K,0,0,
12.997K,0,0,
8.624,8.737,8.542,
8.726,8.814,8.569,
8.723,8.855,8.63,
8.665,8.866,8.628,
8.569,8.824,8.645,
8.456,8.816,8.675,
8.323,8.812,8.642,
8.212,8.749,8.685,
8.1,8.724,8.68,
7.987,8.693,8.687,
1346,324,315,
1335,302,327,
1383,334,319,
1439,383,351,
1285,354,431,
1431,369,406,
1468,390,433,
1416,462,420,
1358,383,421,
1472,415,466,
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1251.3,345.8,380.6,
1286.3,309.9,371.6,
1285.2,335.9,404.4,
1314.6,368.3,408.6,
1226.8,357.9,464.5,
1288.3,340.1,443.3,
1295.8,367.9,482.8,
1243.4,412,451.1,
1233.7,379.4,449.9,
1266.9,394,471.3,
225,138,3534,
393,139.793,4183,
283,140.966,4187,
324,129.31,3939,
264,131.103,3964,
222,128.31,4133,
202,129.483,4092,
349,128.862,4096,
297,122.414,4093,
321,121.517,4181,
8.027,7.888,7.76,
8.002,7.919,7.812,
8.05,7.935,7.716,
8.102,7.97,7.764,
8.045,7.946,7.849,
8.127,8.025,7.848,
8.141,8.039,7.82,
8.145,8.098,7.848,
8.11,8.016,7.851,
8.209,8.026,7.887,
6309,1320,903,
6557,1266,920,
7330,1389,1001,
7561,1494,1042,
7986,1592,1084,
8210,1577,1141,
8279,1629,1200,
8398,1759,1341,
8397,1785,1394,
8486,1834,1372,
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8365.2,1893.1,1500.8,
8962.4,1902,1502.1,
9699,2097.7,1605.6,
9788.4,2172.9,1673.7,
10.0702K,2189.8,1782.2,
10.0679K,2276.4,1867.3,
10.1934K,2264.6,1917,
10.1265K,2421.2,2098.3,
9967.7,2458.5,2183.6,
10.1099K,2477.5,2135.5,
666,0,0,
612,0,0,
712,0,0,
633,0,0,
717,0,0,
699,0,0,
634,0,0,
686,0,0,
643,0,0,
607,0,0,
7.88,7.799,7.603,
7.843,7.748,7.648,
7.873,7.756,7.664,
7.888,7.769,7.638,
7.919,7.828,7.632,
7.944,7.792,7.63,
7.95,7.81,7.639,
7.969,7.819,7.677,
7.981,7.845,7.671,
7.992,7.847,7.681,
31.937K,7606,5015,
40.283K,9261,5721,
43.759K,10.166K,6316,
45.821K,10.493K,6706,
46.843K,11.137K,7219,
47.87K,11.253K,7603,
48.222K,11.734K,8158,
48.902K,12.018K,8617,
48.343K,12.036K,8826,
49.118K,12.145K,9224,
0,0,0,
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0,0,0,
0,0,0,
49.0204K,12.7348K,10.3006K,
62.4035K,14.9862K,11.6228K,
66.2646K,16.292K,12.7573K,
67.7874K,16.5218K,13.1751K,
67.9674K,17.3295K,14.1054K,
69.028K,17.5915K,14.4987K,
68.8817K,17.9931K,15.3729K,
69.014K,18.1024K,16.2114K,
68.0859K,18.1364K,16.4188K,
68.7019K,18.1162K,16.9269K,
3764,0,0,
4397,0,0,
4302,0,0,
4169,0,0,
4069,0,0,
4192,0,0,
4341,0,0,
4303,0,0,
4435,0,0,
4419,0,0,
7.902,7.79,7.555,
7.897,7.822,7.57,
7.923,7.839,7.574,
7.943,7.852,7.592,
7.962,7.865,7.603,
7.976,7.866,7.62,
7.99,7.886,7.64,
8.007,7.905,7.639,
8.012,7.911,7.653,
8.023,7.923,7.666,
0,0,0,
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0,0,0,
0,0,0,
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0,0,0,
0,0,0,
0,0,0,
11.13K,991,194,
9949,889,178,
9108,774,158,
7728,653,129,
6798,589,121,
5489,501,98,
4464,392,81,
3268,288,54,
2156,176,39,
1076,116,19,
0,0,0,
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0,0,0,
0,0,0,
0,0,0,
0,0,0,
17.2222K,1658.4,393.3,
15.4806K,1500.8,345.3,
14.2388K,1323.5,338.9,
12.1125K,1133.9,265.8,
10.7718K,1057.1,250,
8897.8,885.3,212,
7326,718.2,174.9,
5564.9,553.2,116,
3806.7,356.3,82.8,
2066.1,243.7,41.3,
2903,2361.483,60.818K,
2649,2162.483,55.163K,
2370,1927.207,49.989K,
2473,1705.103,44.613K,
1701,1485.862,39.108K,
1672,1230.793,33.361K,
1386,998.724,27.714K,
1211,755.172,21.869K,
761,532.655,15.732K,
442,257.966,8955,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
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45.483K,4059,799,
41.12K,3664,722,
36.795K,3212,620,
31.853K,2850,562,
27.319K,2438,503,
22.811K,2046,420,
18.089K,1684,319,
13.569K,1200,242,
9093,774,180,
4604,360,92,
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104.7828K,10.274K,2339.1,
95.7943K,9537.7,2092.5,
86.9563K,8452.7,1799,
76.3851K,7619.5,1652.8,
66.6694K,6534,1463.3,
56.4102K,5588.9,1244.4,
45.9702K,4678.8,928.6,
35.5482K,3412.9,717.8,
24.7994K,2243,517,
13.1517K,1072,276,
10.87K,0,0,
10.458K,0,0,
9577,0,0,
8130,0,0,
7444,0,0,
5995,0,0,
5256,0,0,
4151,0,0,
3323,0,0,
2036,0,0,
'NAN','NAN','NAN',
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'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
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0,0,0,
0,0,0,
216.908K,19.302K,3911,
195.283K,17.338K,3499,
173.627K,15.49K,3098,
151.544K,13.642K,2810,
130.171K,11.816K,2383,
108.299K,9685,1945,
87.111K,7800,1572,
64.996K,5660,1114,
43.323K,3931,766,
21.846K,1929,390,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
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0,0,0,
0,0,0,
0,0,0,
603.8496K,59.1446K,13.0239K,
549.0861K,53.7773K,11.7351K,
494.3848K,48.4616K,10.4183K,
437.8861K,43.0488K,9522.5,
380.8194K,37.5503K,8117.7,
323.3328K,31.3302K,6665,
266.0624K,25.6072K,5315.4,
203.5839K,18.6265K,3786.6,
139.5707K,13.2596K,2592.1,
72.9901K,6564.1,1355.3,
52.143K,0,0,
47.226K,0,0,
42.385K,0,0,
38.289K,0,0,
34.321K,0,0,
29.107K,0,0,
24.566K,0,0,
19.994K,0,0,
14.489K,0,0,
8674,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
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0,0,0,
0,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
6743,554,116,
6757,608,109,
6664,553,94,
6602,592,96,
6596,580,117,
6627,542,110,
6596,552,120,
6641,590,96,
6745,571,101,
6618,543,97,
0,0,0,
0,0,0,
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0,0,0,
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0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
180.314K,13.983K,2457,
180.406K,13.65K,2404,
179.546K,13.912K,2423,
180.286K,13.577K,2370,
180.3K,13.731K,2544,
180.1K,14.023K,2453,
179.84K,13.797K,2369,
180.897K,13.677K,2553,
180.035K,13.684K,2499,
180.364K,13.661K,2478,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
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0,0,0,
0,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
0,0,0,
0,0,0,
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0,0,0,
0,0,0,
0,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
2564,192,15,
6187,487,60,
9121,826,124,
12.03K,1073,127,
14.245K,1346,163,
16.848K,1698,197,
19.318K,1975,254,
21.705K,2065,336,
24.842K,2295,343,
27.471K,2427,413,
4816,494,187,
7999,773,254,
11.084K,1043,326,
14.373K,1301,363,
17.445K,1642,439,
20.575K,1969,525,
23.821K,2226,534,
26.79K,2516,624,
30.101K,2759,640,
32.93K,2930,700,
1498,96,5,
2085,98,10,
3686,188,16,
5975,203,29,
8020,356,39,
10.508K,569,34,
13.146K,677,36,
15.567K,813,50,
18.439K,1022,63,
21.231K,1114,111,
3242.2,242.6,21.9,
7127,542.1,69.4,
11.2356K,920.2,126.8,
15.6154K,1139.9,144.1,
18.9538K,1519.5,173.7,
23.4319K,1966.6,204.5,
27.6687K,2276.1,282.4,
31.2129K,2420.6,339.3,
36.6088K,2748.1,384.9,
41.6029K,3060.3,488.2,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
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0,0,0,
5.296,5.654,6.509,
7.018,7.68,7.675,
6.634,7.593,7.943,
6.062,7.873,7.328,
5.661,7.424,7.367,
5.283,6.996,7.843,
4.918,7.003,7.993,
4.718,6.677,8.012,
4.577,6.359,7.729,
4.394,6.278,7.233,
215.337K,18.393K,1975,
324.525K,26.989K,2716,
438.041K,36.44K,3512,
557.083K,47.998K,4227,
663.349K,59.358K,5263,
768.603K,69.559K,6743,
868.646K,79.754K,7611,
971.783K,89.924K,8806,
1.0664M,99.913K,10.094K,
1.162758M,108.889K,11.52K,
215.384K,23.141K,8061,
298.829K,30.482K,9849,
384.371K,38.459K,11.334K,
468.986K,45.525K,12.832K,
552.98K,53.724K,14.241K,
638.622K,61.01K,15.846K,
722.489K,68.971K,17.115K,
809.075K,76.552K,19.034K,
892.721K,84.49K,20.493K,
977.346K,91.825K,21.909K,
36.318K,1249,109,
49.614K,1641,107,
72.591K,2441,193,
104.832K,3607,227,
145.027K,5340,250,
192.654K,6869,441,
244.201K,9109,516,
300.129K,12.231K,583,
357.99K,14.898K,816,
418.185K,17.854K,923,
308.8714K,24.884K,2663.1,
458.5064K,36.4868K,3632,
620.9306K,48.9962K,4665.5,
796.9213K,65.0752K,5666.7,
961.6295K,80.5691K,7061.8,
1.1295947M,94.8796K,9005.2,
1.2935968M,109.7771K,10.2951K,
1.460925M,124.911K,12.0002K,
1.6169778M,139.9195K,13.6806K,
1.7732973M,152.6383K,15.6236K,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
8.002,8.533,8.441,
8.145,8.6,8.572,
8.113,8.605,8.488,
8.019,8.585,8.489,
7.852,8.522,8.549,
7.669,8.498,8.451,
7.489,8.422,8.449,
7.328,8.294,8.468,
7.165,8.235,8.386,
7.025,8.151,8.408,
0,0,0,
0,0,0,
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0,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
1235,203,133,
1524,273,163,
1705,292,167,
1825,310,225,
1965,322,233,
1979,346,255,
2071,377,277,
2097,399,296,
1955,423,304,
2021,458,278,
0,0,0,
0,0,0,
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0,0,0,
0,0,0,
0,0,0,
0,0,0,
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0,0,0,
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0,0,0,
0,0,0,
0,0,0,
6837.5,1215.5,739.1,
9170.9,1538.7,883.3,
9782.4,1625.8,880.7,
10.0891K,1793.1,1135,
10.6751K,2008.3,1146.8,
10.514K,2000.1,1211.6,
10.8393K,2156.1,1278.9,
10.9114K,2351.8,1440.2,
10.6631K,2522.6,1492.5,
10.749K,2539,1535.3,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
7.759,7.556,7.479,
7.758,7.709,7.528,
7.818,7.742,7.548,
7.881,7.711,7.572,
7.9,7.688,7.617,
7.916,7.771,7.651,
7.957,7.78,7.659,
7.952,7.769,7.673,
7.925,7.781,7.705,
7.938,7.828,7.592,
76.294K,14.922K,7984,
91.616K,18.502K,9758,
97.635K,21.355K,11.016K,
96.921K,21.669K,12.463K,
98.988K,22.717K,13.295K,
101.076K,23.39K,14.022K,
101.942K,24.513K,14.776K,
103.453K,24.988K,16.195K,
105.813K,25.974K,16.736K,
107.742K,26.676K,17.373K,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
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0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
399.3404K,83.5555K,51.8361K,
450.9244K,97.8623K,62.141K,
472.5998K,109.7131K,68.1449K,
453.6909K,107.9517K,75.5959K,
457.6445K,112.9751K,79.5992K,
461.3479K,114.5531K,83.578K,
462.3945K,117.9729K,87.9989K,
466.2295K,119.3523K,93.7979K,
477.1101K,122.7744K,97.3811K,
478.9995K,126.6905K,99.2178K,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
7.842,7.719,7.46,
7.874,7.755,7.488,
7.9,7.79,7.519,
7.934,7.813,7.544,
7.958,7.825,7.555,
7.973,7.847,7.565,
7.987,7.866,7.568,
7.998,7.877,7.599,
8.005,7.888,7.6,
8.018,7.899,7.622,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
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0,0,0,
0,0,0,
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0,0,0,
0,0,0,
0,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
31.311K,2938,614,
28.104K,2452,504,
25.176K,2300,446,
21.656K,1959,403,
18.843K,1720,346,
15.561K,1389,260,
12.592K,1177,216,
9389,823,157,
6264,570,104,
3181,282,54,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
269.3634K,27.8412K,6191.8,
243.1714K,23.3786K,5495.6,
219.6202K,22.2856K,4761.5,
192.5248K,19.2098K,4316.6,
170.2823K,16.781K,3805.7,
142.9969K,14.0614K,2627.7,
118.9999K,11.9936K,2261.8,
90.6335K,8240.9,1689.5,
62.2743K,5867.4,1218.8,
32.8423K,3164.8,573.8,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
847.903K,75.883K,15.193K,
763.705K,68.17K,13.399K,
678.545K,60.899K,12.02K,
593.786K,53.24K,10.773K,
508.615K,45.888K,9041,
423.798K,37.828K,7521,
338.501K,30.157K,6070,
254.47K,22.837K,4570,
169.558K,15.373K,3063,
84.626K,7627,1511,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
8.745471M,847.5714K,180.9068K,
7.9542778M,765.7267K,160.5986K,
7.1501717M,689.4596K,144.0333K,
6.3277158M,607.5996K,130.7229K,
5.4871379M,530.5113K,109.2346K,
4.6478793M,438.2119K,90.5883K,
3.763958M,352.3456K,72.7455K,
2.8913776M,271.9001K,55.6405K,
1.9700504M,184.3362K,37.3562K,
1.0102443M,91.6614K,18.5013K,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
0,0,0,
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN',
'NAN','NAN','NAN'
)80,48,148,242,102,90,476,2242,410,111,820,303,0,MIDM2,30,434,710,303,0,MIDM65535,52427,65534[Scen_1,Period1][Scen_1,Parameter1]index temp:= ['Length1','Vehicle_type1','Zone1','Period1','Parameter1','Result'];
index k:= 1..size(Length1)*size(Vehicle_type1)*size(Zone1)*size(Period1)*size(Parameter1);
index i:= 1..size(scen_1)*size(ajo);
var a:= mdarraytotable(Ajot,k,temp);
a:= concatrows(a,ajo,scen_1,i);
var b:= concatrows(Desc,ajo,scen_1,i);
index j:= concat(input_var1,temp);
concat(b,a,input_var1,temp,j)320,240,148,242,677,21,476,5302,107,199,1022,395,0,MIDM[Sys_localindex('I'),Sys_localindex('J')][1,1,1,0][Sys_localindex('I'),5,Sys_localindex('J'),1,Sys_localindex('K'),1]var a:= array(va4.i, 1/size(va4.i));
a:= chancedist(a, va4, a.i);
index j:= a.j[@.j=12..17];
a:= a[.j=j];
mdtable(a,a.k, a.j)320,160,148,242,776,26,465,340,0,MIDM[Zone1,Parameter1][1,1,1,0][Length1,1,Vehicle_type1,2,Zone1,1,Period1,1,Parameter1,1,Run,1]var a:= va5[parameter1='Vehicle km'];
sum(sum(sum(a)))296,96,148,242,553,320,476,2242,785,71,416,303,1,PDFP[Zone1,Period1,Undefined,Undefined,1][1,0,0,0][Length1,1,Period1,2,Vehicle_type1,1,Zone1,1]456,272,148,24Choose_ajoChoice(Ajo,5,False)64,232,148,12[Formnode Choose_ajo1]52425,39321,65535['item 1']Cost elements1248,312,148,241,0,0,1,1,1,0,,0,Cost_elementsDistance dataktluser26. heita 2006 6:5348,24480,64,148,241,40,147,327,256,17Links altvar a:= etapit(Route_matrix);
a:= a&','&a[.etappi=a.etappi+1];
index row:= 1..size(a);
index col:= ['From','To1','.Etappi','Link'];
a:= mdarraytotable(a,row,col);
a:= a[col='Link'];
var d:= bus_links;
index row2:= 1..size(d);
index col2:= ['Time_of_day1','.B','Link'];
d:= mdarraytotable(d,row2,col2);
d:= d[col2='Link'];
index row3:= 1..size(row)+size(row2);
a:= concat(a,d,row,row2,row3);
a:= if a=null then 'xxxx' else a;
a:= mirror(a,a.row3);
a:= if evaluate(selecttext(a,1,4))>=evaluate(selecttext(a,6,9)) then 'xxxx' else a;
index b:= unique(a,a.a);
a:= a[.a=b];
a:= a[.b=sortindex(a,a.b)];
index row4:= (1..size(a)-1);
a:= slice(a,a.b,row4)184,160,148,242,11,29,300,615,0,MIDMBus route lengthvar a:= if findintext(selecttext(link1,1,4),Active_routes)>0 and
findintext(selecttext(link1,6,9),Active_routes)>0 then link_length1 else 0;
a:= sum(a,link1);
if a=null then 0 else a
{a:= sum((if time_of_day_by_time= time_of_day1 then a else 0),time_of_day1);
}184,88,148,242,248,258,756,512,0,MIDM[I,Time_of_day1]DistanceskmThe length of each origin-destination trip.var x:= 1;
var a:= 0;
var b:= 0;
while x<=size(link1) do (
var c:= slice(link1,x);
var d:= slice(link_length1,link1,x);
var e:= if findintext(c,route_matrix)>0 then d else 0;
a:= a+e;
e:= if findintext(c,Bus_matrix&' ')>0 then d else 0;
b:= b+e;
x:= x+1);
a:= array(mode1, [a,a,b]) + in_area_distance[area1=From] + in_area_distance[area1=To1];
if a=null then 0 else a184,32,148,242,32,14,476,5212,27,18,883,552,0,MIDM[To1,From]In-area distancekmThe distance that is travelled within an area collecting people before the actual trip to another area starts.
Distances are rough estimates measured with a string and a ruler. This approach was considered exact enough, as the road structure is the same in all scenarios considered.
Note that although not quite realistic, this value is the same for both composite and car traffic.Table(Area1)(
1,1,0.6,0.6,0.6,1,1,0.1,1,1,1,1,1.5,1.5,1,1,0.6,0.6,1,1,0.6,0.6,1,1.5,1.5,2.5,1.5,1.5,1.5,2.5,1.5,1.5,1.5,1.5,2.5,1.5,1.5,1.5,1.5,1,1.5,1.5,1.5,2.5,1.5,1,2.5,1.5,1.5,2.5,1.5,1,4,1.5,1.5,2.5,1.5,1.5,1,2.5,1.5,1.5,1.5,2.5,1.5,0.6,0.6,1.5,1.5,1.5,1.5,2.5,2.5,2.5,2.5,2.5,2.5,1.5,2.5,2.5,0.6,1.5,1.5,1.5,1.5,1,1.5,2.5,2.5,2.5,4,4,4,8,2.5,4,4,4,8,2.5,1.5,2.5,2.5,2.5,4,8,4,1.5,1.5,1.5,1,2.5,1.5,1.5,1.5,1.5,1.5,2.5,2.5,1.5,1.5,2.5,1.5,4,2.5,2.5,4,4,4,0)64,32,148,242,148,93,416,561,0,MIDM65535,52427,65534Data based on a map of Helsinki Metropolitan area (YTV liikenne: Pääkaupunkiseudun joukkoliikennekartta 11.8.2002).Link lengthkmThe distance between two areas.
Distances are rough estimates measured with a string and a ruler. This approach was considered exact enough, as the road structure is the same in all scenarios considered.Table(Link1)(
0.8,1,0.6,1.7,1.7,1.1,4.3,1.1,2.2,1.2,1.5,1.7,1.2,0.8,0.8,0.8,2.1,2.9,2.2,1.1,0.8,3,2.4,1.1,1.3,1.2,3.3,1.7,2.8,3,5.4,2.8,2.8,3,3.6,3,1.2,1.9,2,2.6,1,1.6,1.6,1.2,1.4,1,1.1,1,3.7,1.7,1,3,3.3,4.2,2.3,2.2,1.1,1.6,1.8,1.8,4.4,1.1,1.4,1.2,1,1.1,1.1,1.7,0.8,0.9,2.4,1.6,2,2.3,0.4,1.8,2.4,1.9,2.5,3.2,3.2,4,3.3,0.8,5,5.6,5.8,4.7,1.3,3,3.7,11.2,3,2.8,3.8,1.8,3.4,2.7,3.8,1.9,2.5,2.2,2.4,2.1,2.8,2.9,3.4,3.9,4.3,5.6,4.7,1.4,2.7,1.7,3.2,1.8,1.8,0.8,2.7,2.6,2.5,3.9,2.7,1.2,6.8,2.2,4.2,5.1,2.8,4.6,5.3,1.9,1.3,1.4,2.3,2,2,4.9,4.1,4.3,9.5,6.9,1.4,1.2,4.6,14.5,3.2,1.7,1.8,4,2.9,2.5,2.2,5,2.4,3.6,1.4,2.4,1.6,3.8,3,3.2,6.4,2.6,1.2,2,3.2,2.7,1.5,1.5,3.6,2.1,1.2,2.3,1.1,2.6,1.7,2.1,2.6,4,1,2.2,1.3,2.6,3.6,2.7,2.6,1.9,1.1,2.5,4,2,3,3,3.8,1.3,2.7,3.2,2.6,1.7,1.1,2,0.9,2.8,1.6,2.4,1.5,2.8,2.1,1.7,3.8,3.6,2.7,1.3,3.3,4.2,1.5,3,2.8,2.7,6.1,5,2.2,3.8,5.2,3.8,4.2,3.4,5.2,5.3,4.7,1.1,5.5,3.3,3.6,1,0.9,1.2,3,2.9,3.9,5.5,1.1,4.4,2.8,2.3,3.3,3,3,2.1,6.3,6.8,1.5,4.1,3.2,2,1.7,6.3,4.4,2,5.3,4.2,1.4,3.2,4.8,3.6,5.8,6.8,2.3,5.2,8.7,4.2,1.6,4.5,3.4,3,5.4,4.1,3.6,5.9,5.4,3.6,2,6.8,1.6,3,6.2,3.5,8,2.1,4.2,6.8,5.2,1.4,4.1,2.3,4,3.6,2.8,3.5,3.4,2,3.8,1.8,3.4,1.8,2,2.6,3.2,1.5,1.2,3.2,3.2,4.8,1.7,2.5,2.5,5.4,2.5,4.8,4.1,3,4,3,2.9,2.3,2.2,1.8,2,1.6,2.4,3.5,2.8,2,0.9,4.7,3.6,2.8)64,88,148,242,0,0,229,665,0,MIDM2,288,18,177,576,0,MIDM65535,52427,655341,D,4,2,0,0Data based on a map of Helsinki Metropolitan area (YTV liikenne: Pääkaupunkiseudun joukkoliikennekartta 11.8.2002).Link['1001,1002','1001,1004','1001,1005','1001,1011','1001,1012','1001,1015','1001,1017','1001,1018','1001,1020','1002,1003','1002,1004','1002,1009','1002,1010','1002,1011','1003,1004','1003,1007','1003,1008','1003,1009','1003,1010','1004,1005','1004,1007','1004,1009','1004,1010','1005,1006','1005,1007','1005,1018','1005,1066','1009,1010','1010,1011','1010,1026','1010,1067','1011,1012','1011,1013','1012,1013','1012,1014','1012,1015','1012,1019','1012,1021','1013,1014','1013,1015','1013,1027','1014,1015','1014,1016','1014,1029','1014,1030','1015,1016','1015,1017','1015,1021','1015,1029','1016,1017','1016,1025','1016,1029','1016,1030','1016,1036','1017,1019','1017,1020','1017,1021','1017,1022','1017,1023','1017,1025','1017,1037','1018,1019','1018,1020','1019,1020','1019,1021','1019,1022','1019,1023','1020,1021','1020,1022','1020,1023','1020,1052','1021,1022','1021,1023','1021,1025','1022,1023','1022,1025','1023,1024','1024,1025','1024,1042','1025,1036','1025,1037','1025,1040','1026,1067','1027,1028','1027,1067','1027,1068','1027,1083','1027,1084','1028,1029','1028,1031','1028,1084','1029,1030','1029,1031','1029,1032','1030,1031','1030,1034','1030,1035','1030,1036','1030,1037','1031,1032','1031,1034','1031,1081','1032,1033','1032,1034','1032,1035','1032,1082','1032,1083','1032,1100','1032,1102','1032,1104','1033,1034','1034,1035','1034,1036','1034,1038','1035,1038','1035,1102','1036,1037','1036,1038','1037,1038','1037,1040','1037,1041','1037,1043','1038,1039','1038,1040','1038,1109','1039,1040','1039,1048','1039,1109','1040,1041','1040,1048','1040,1109','1041,1042','1041,1043','1041,1044','1041,1047','1042,1043','1042,1045','1042,1051','1042,1056','1042,1059','1042,1117','1042,1128','1043,1044','1043,1045','1043,1050','1043,1125','1044,1045','1044,1046','1044,1047','1045,1050','1045,1051','1045,1059','1045,1060','1045,1128','1046,1047','1046,1048','1046,1049','1046,1050','1047,1048','1047,1109','1048,1049','1049,1050','1049,1112','1049,1113','1049,1117','1050,1051','1051,1128','1051,1129','1052,1055','1053,1055','1053,1056','1054,1055','1054,1056','1054,1057','1054,1058','1054,1059','1055,1056','1056,1058','1056,1059','1056,1061','1057,1058','1057,1060','1057,1061','1057,1063','1057,1064','1058,1059','1058,1060','1058,1061','1059,1060','1060,1061','1060,1062','1061,1062','1062,1063','1062,1065','1062,1129','1063,1064','1063,1065','1064,1065','1067,1068','1067,1069','1067,1073','1068,1069','1068,1070','1068,1084','1069,1070','1069,1071','1069,1073','1070,1071','1070,1072','1070,1073','1070,1084','1071,1072','1071,1073','1071,1074','1071,1075','1072,1075','1072,1085','1073,1074','1074,1075','1074,1076','1074,1079','1074,1085','1075,1076','1075,1079','1075,1085','1076,1077','1076,1078','1076,1079','1077,1078','1077,1080','1078,1079','1078,1080','1079,1080','1079,1091','1080,1095','1081,1082','1081,1083','1082,1083','1082,1086','1082,1087','1082,1100','1082,1101','1083,1084','1083,1085','1083,1086','1083,1087','1084,1085','1084,1086','1085,1086','1085,1090','1085,1092','1085,1093','1086,1087','1086,1090','1087,1088','1087,1089','1088,1089','1088,1093','1088,1100','1089,1090','1089,1093','1090,1091','1091,1092','1092,1093','1092,1095','1093,1094','1093,1095','1093,1097','1095,1096','1097,1098','1097,1099','1097,1104','1100,1101','1100,1102','1100,1104','1101,1102','1101,1103','1101,1104','1102,1103','1102,1107','1102,1108','1103,1104','1103,1105','1103,1106','1103,1107','1103,1108','1105,1106','1105,1107','1106,1107','1107,1108','1107,1110','1107,1111','1108,1109','1109,1110','1109,1111','1109,1112','1109,1113','1109,1117','1110,1111','1110,1112','1112,1113','1112,1114','1112,1115','1112,1123','1113,1114','1113,1115','1113,1116','1113,1117','1114,1115','1114,1123','1115,1116','1115,1120','1115,1122','1115,1123','1116,1118','1116,1120','1117,1118','1117,1127','1117,1128','1118,1119','1118,1120','1118,1127','1119,1120','1119,1121','1119,1126','1119,1127','1120,1121','1120,1122','1120,1127','1121,1122','1121,1124','1121,1125','1122,1124','1124,1125','1125,1126','1125,1127','1127,1128','1128,1129']64,120,148,12['1001,1002','1001,1004','1001,1005','1001,1011','1001,1012','1001,1015','1001,1017','1001,1018','1001,1020','1002,1003','1002,1004','1002,1009','1002,1010','1002,1011','1003,1004','1003,1007','1003,1008','1003,1009','1003,1010','1004,1005','1004,1007','1004,1009','1004,1010','1005,1006','1005,1007','1005,1018','1005,1066','1009,1010','1010,1011','1010,1026','1010,1067','1011,1012','1011,1013','1012,1013','1012,1014','1012,1015','1012,1019','1012,1021','1013,1014','1013,1015','1013,1027','1014,1015','1014,1016','1014,1029','1014,1030','1015,1016','1015,1017','1015,1021','1015,1029','1016,1017','1016,1025','1016,1029','1016,1030','1016,1036','1017,1019','1017,1020','1017,1021','1017,1022','1017,1023','1017,1025','1017,1037','1018,1019','1018,1020','1019,1020','1019,1021','1019,1022','1019,1023','1020,1021','1020,1022','1020,1023','1020,1052','1021,1022','1021,1023','1021,1025','1022,1023','1022,1025','1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model info
URN:NBN:fi-fe20051439DC-attribute with refinement Scheme (if any) Value
Title Composite traffic model 1.0.1
Creator Tuomisto, Jouni
Creator Tainio, Marko
Subject Trip aggregation
Subject Urban traffic
Subject Public transportation
Description.abstract Background Traffic congestion is rapidly becoming the most important obstacle to urban development. In addition, traffic creates major health, environmental, and economical problems. Nonetheless, automobiles are crucial for the functions of the modern society. Most proposals for sustainable traffic solutions face major political opposition, economical consequences, or technical problems. Methods We performed a decision analysis in a poorly studied area, trip aggregation, and studied decisions from the perspective of two different stakeholders, the passenger and society. We modelled the impact and potential of composite traffic, a hypothetical large-scale demand-responsive public transport system for the Helsinki metropolitan area, where a centralised system would collect the information on all trip demands online, would merge the trips with the same origin and destination into public vehicles with eight or four seats, and then would transmit the trip instructions to the passengers' mobile phones. Results We show here that in an urban area with one million inhabitants, trip aggregation could reduce the health, environmental, and other detrimental impacts of car traffic typically by 50-70 %, and if implemented could attract about half of the car passengers, and within a broad operational range would require no public subsidies. Conclusions Composite traffic provides new degrees of freedom in urban decision-making in identifying novel solutions to the problems of urban traffic.
Publisher Kansanterveyslaitos (KTL; National Public Health Institute)
Date.issued W3C-DTF 2005-11-30
Type DCMIType Software
Format IMT text/xml
Format.medium computerFile
Format 836 kB
Identifier http://www.ktl.fi/risk
Identifier URN URN:NBN:fi-fe20051439
Language ISO639-2 en
Rights Copyright Kansanterveyslaitos, 2005088,432,180,212,105,198,476,49965535,54067,19661Cost assumptions and outputsktluser12. heita 2005 22:5148,24248,424,148,281,0,0,1,1,1,0,,0,1,274,10,420,441,17Table 11172,100,1156,121,0,0,1,0,0,0,72,0,1Table_1_pressuresFigure 3.top1172,196,1156,121,0,0,1,0,0,0,72,0,1Fig_5a_societal_costFigure 3.middle1172,220,1156,121,0,0,1,0,0,0,72,0,1Fig_5b_subsidiesFigure 3.bottom1172,244,1156,121,0,0,1,0,0,0,72,0,1Fig_5c_expandingFigure 21172,172,1156,121,0,0,1,0,0,0,72,0,1Fig_4_cost_variationFigure 11172,124,1156,121,0,0,1,0,0,0,72,0,1Fig_2_tripsCost by type to stakeholder1172,148,1156,121,0,0,1,0,0,0,72,0,1Fig_3_cost_by_sourceFig 6A Passenger VOI1172,268,1156,121,0,0,1,0,0,0,72,0,1Fig_6a_passenger_voiFig 6B Societal VOI1172,292,1156,121,0,0,1,0,0,0,72,0,1Fig_6b_societal_voiLog 1.220.6.2006 Jouni Tuomisto
Nyt kun pitäisi tosissaan ruveta tekemään uutta yhdistelmäliikennemallia, herää kysymys, mitä mallia/malleja pitäisi käyttää pohjana. Tässä siis ensimmäisenä kuvaus nyt olemassaolevista malleista (esitelty aikajärjestyksessä).
Lopputulos on, että kehitys aloitetaan yhdistämällä olennaiset osat versioista 1.1, 1.0.5, ja sup1_metro. Muut siirretään Old-kansioon. Näistä otetaan peruskehityksen kohteeksi 1.0.5, ja muiden muutokset siirretään ja kopioidaan siihen. Versionumeroksi annetaan 1.2. Alustavasti tämä onnistuikin, ja kaikki muiden versioiden olennaiset ominaisuudet siirrettiin 1.2:een. Näitä ovat
* Eri skenarioajojen väliltä valitseminen (epäaktiivinen koodi vain)
* uusi liukuvasti aggregoiva Trips-solmu 1.1-versiosta ja tähän liittyen uusi Vehicle-indeksi.
* Autotyyppikohtaiset autotiedot erotuksena vanhaan, jossa koka vehicle-indeksin riville annettiin oma tieto.
* Passiivinen koodi metron mukaanotosta malliin, sisältäen Metro_matrixin ja Va1-solmun.
Seuraavaksi tehtäviä hommia ovat
* rakentaa joukkoliikenteen matkamatriisi
* syöttää sisään joukkoliikennereitit (Virpi tekee)
* Mieti, mitkä olisivat järkevät skenaariot laskettavaksi.
* tehdä solmu, joka pistää public fraction:in verran väkeä joukkoliikenteeseen jos se on tarjolla, ja yhdistelmäliikenteeseen loput; tämän pitää olla Tripsin ylävirrassa.
* Muuttaa vehicle balance- ja muita solmuja siten, että ne eivät ole ajoneuvoriippuvaisia.
* Tehdä solmu jolla valitaan, millainen auto on N henkilön kuljetuksessa käytössä. Tätä pitää pystyä vaihtelemaan Scenario_inputilla.
*Korjata Tripsia, koska nyt se antaa korkeintaan 1:n yhdistelmäliikennemuodoille, ja loput menevät autoihin.
* Tarkistaa tarvitaanko Vehicle_balance1-solmua johonkin ja poistaa se jos ei.
* Miettiä onko tarpeen kerätä matkatiedot nyt suunnitellulla Vehicle-indeksin tarkkuudella. Jos auton kokoluokka on määritelty erikseen (ks 3 palloa ylös), ei ole väliä montako ihmistä siinä on. Senhän voinee laskea jälkeenpäin, tosin vain keskiarvon (?) Tämä voi olla kriittinen asia muistin kannalta, koska nyt vehicle-indeksi on paljon isompi kuin ennen.
29.6.2006 Jouni Tuomisto
Tehtiin seuraavat muutokset:
- Luovutaan kokonaan termistä composite fraction, koska nyt osuus voidaan laskea joko automatkoista tai joukkoliikennematkoista, eikä se ole siis yksiselitteinen. Niinpä aletaan käyttää nimiä car fraction ja public fraction jotka ovat ne osuudet nykyisestä liikenteestä jotka S€ILYV€T alkuperäisessä moodissa. Nykytila on siis car 100% public 100%.
Tämä muutos aiheuttanee muutoksia useampaan solmuun, mutta niitä ei ruveta tekemään systemaattisesti nyt.
-Korjattiin All_trips uuden systeemin mukaiseksi, ja näyttää toimivan.
Lisättiin indeksiin Input_var rivi Public level kuvaamaan sitä, miten laajaa joukkoliikennettä on tarjolla eli paljonko supistetaan siitä mitä on nyt olemassa. Tämä on vasta rivinä indeksissä, eikä sen operationalisointia ole rakennettu.
-Korjattiin Tripsiä, ja nyt näyttää toimivan. Ongelmana oli, että y&'c'=Vehicle_noch on ERI asia kuin (y&'c')=Vehicle_noch eli sulkeet tarvitaan kertomaan että kyseessä on yksi tekstimuuttuja.
-Mietittävä indeksit Vehicle, Vehicle_noch, Vehicle_type. Nyt ajattelen niin, että Outputsiin pistetään Vehicle_type ja Mode1. Se on epäselvää, kannattaako Nochange-lukua kuljettaa mukana ollenkaan, kun ei taida olla kovin olennainen. Olisi se toisaalta kuitenkin mukava, joten pitää miettiä.
- Joitakin Outputsiin johtavia solmuja muutettiin ja kaikkien toimivuus tarkastettiin. Kuitenkaan indeksejä ei alettu muuttaa, koska asia on vielä päättämättä (ks. edellinen ranskalainen viiva).
- On myös mietittävä, miten lasketaan ylimääräinen noukkimiseen kuluva aika. Tällöin tarvitaan tietoa a) montako autoa ajaa kyseisten paikkojen väliä (jotta voidaan vähentää pysähtymispisteiden määrää) b) montako henkeä on autossa (jotta voidaan laskea todennäköisten pysähdysten määrä). Tämä on vielä miettimättä, mutta analyyttinen ratkaisu ongelmaan on keksitty.
30.6.2006 Jouni Tuomisto
Outputs-solmun ylävirtaa siivottiin, ja Waitingia lukuunottamatta ne saatiin oikeaan formaattiin. Waiting pitää miettiä kokonaan uudestaan.
1.7.2006 Jouni Tuomisto
Waiting mietittiin uusiksi. Nyt se huomioi pudotuspisteiden määrän alueella, yhtäaikaisten autojen yhteistyön, ja vaihtoajan, joka on nyt vakio 6 min koska usean auton tilanteessa hyöty otetaan pudotuspisteiden vähentämisestä. Mikä mukavinta, ajat eivät näytä pahoilta näillä testinumeroilla, mikä kyllä johtuu siitä että mukana on vain ydinkeskusta ja vaihtoja ei tarvita.
2.7.2006 Jouni Tuomisto
Kaikki Outputsiin tulevat solmut järjestettiin, aggragointifunktiot yhdenmukaistettiin ja indeksit muutettiin järjestelmällisiksi. Nyt Output1-indeksissä on vain 6 riviä mutta 8 muuttujaa. Tämä tehtiin siten, että total_vehicle_need, link_intensity ja areal_vehicle_peak yhdistettiin yhdeksi muuttujaksi period-indeksin riveinä tässä järjestyksessä. Kaikki muut muuttujat muutettiin siten, että niissä on indeksit Zone, Length, Period ja Vehicle_type. Teknisiä vikoja en enää näistä solmuista löytänyt, joten periaatteessa malli on nyt ajokunnossa.
3.7.2006 Jouni Tuomisto
Täydennettiin Public level malllin toimivaksi osaksi. Tein Public_martix-solmun, jota tässä hyödynnetään. Se on kuitenkin pelkkä dummy, koska oli Virpin ja Hannan homma kehittää joukkoliikennereittimatriisia, joten siihen ei puututa. Nyt voisi testata, antaako malli samoja tuloksia kuin edellinen malli.
Tuo edellinen lause havahdutti huomaamaan, ettei mallilla pystykään laskemaan samoja skenaarioita. Trips kun on rakennettu siten, että matkaryhmän kokoa pienennetään yksi kerrallaan, ja vanhoissa skenaarioissa hypättiin neljän välein. Tämä ongelma vaati koko päivän taustapohdintaa sekä Tripsin uudelleenmiettimistä. Ongelma ratkaistiin siten, että nyt yhdistely tehdään Vehicle_noch-indeksillä rivi kerrallaan, eikä peräkkäisten rivien tarvitse sisältää peräkkäisiä numeroita, kunhan ovat alenevassa järjestyksessä. Sen ei siis myöskään tarvitse olla yhtä suurempi kuin Vehicle-indeksi kuten aiemmin. Merkintätapaa indekseissä muutettiin siten, että ensimmäinen merkki on d=direct tai c=change ja sitten tulee ryhmän koko. Tämä vaati muutaman tarkistusrivin lisäämisen joihinkin solmuihin, jotta vältetään virheilmoitukset tapauksessa joissa vehicle_noch:ssa on vähemmän rivejä kuin vehicle:ssä.
Scenarios-solmu tarkistettiin ja päivitettiin. Nyt siinä on kolme skenaariota vanhasta tutkimuksesta, ja ainakin 16 alueen minimallilla ne toimivat hyvin. Nyt pistän tämän ajamaan yöksi 129 alueella.
9.7.2006 Jouni Tuomisto
Muisti tökkäsi 129 alueen kanssa, mutta 64 aluetta ajautui kutakuinkin siivosti. Ongelmana on Waiting-solmu, jossa on kovin monta indeksiä yhtäaikaisesti pyörimässä, ja sitten vielä lisätään Waiting_time. Yritin ideoida erilaisia ratkaisuja:
- Ei summatakaan tietyn odotusajan matkojen lukumääriä, vaan lasketaan keskiarvoja. Tämä ei onnistu siksi, että pitäisi pystyä laskemaan keskiarvot ennen esim. From-indeksin romauttamista Zoneksi , jolloin en keksinyt hyvää tapaa ilman että tauluun siunaantuu nollia jotka pilaavat keskiarvoistuksen.
- Yritin tehdä while-do-luuppeja joihin yhdistetään slice-funktio, mutta tulos johti vain tosi hitaaseen laskentaan verrattuna alkuperäiseen. En jaksanut selvittää oliko syynä bugi vain onko slicaaminen vain sen verran hidasta.
-Yritin myös semmoista, että Waiting timen lisäksi tehdään for- luuppi timelle, mutta sekin näytti pidentävän laskenta-aikaa. Tämä kyllä olisi mahdollinen ratkaisu, mutten nyt jaksa testailla laskenta-aikoja. Voisin ehkä tehdä sitä huomenna töissä sivukoneella.
10.7.2006 Jouni Tuomisto
En enää palannut tuohon Waitingin muistiongelmaan, vaan uskon sen olevan siedettävä kun mallia ajetaan BBU:lla. Tänään sen sijaan yhdistin Virpin, Hannan ja Ollin muokkaaman Bussireittimatriisin ja HLT-matkamatriisin perusmalliin. Lisäsin myös scenario_inputiin mahdollisuuden vaihtaa matkamatriisia skenaariosta toiseen.
Bussireittimatriisi toimii muuten hyvin, mutta aikaulottuvuutta en saa toimimaan. Syynä on se, että jossain vaiheessa Mirror-funktiossa tulee m..n indeksi, jossa m tai n eivät olekaan skalaareja. Yritin ratkaista tätä käyttämällä for w[]:= luuppia koko kaavan ympärillä, mutten saanut sitä toimimaan.
Huomasin, että Si_pi-funktio on iänikuinen Ana 2.0-koodia sisältävä. Päivitin sen, jolloin poistui myös tarve erilliselle normitus-funktiolle.
Nimi vaihdettiin versioksi 1.3.
056,248,148,122,676,125,500,41865535,54067,19661Log 1.311.7.2006 Jouni Tuomisto
Tein muutamia skenaarioita ja ajattelin testata systeemiä BBU:ssa. Joukkoliikennematriisi ei toimi vielä tyydyttävällä tavalla, joten käytän oletusta että joukkoliikenne on kaikkialla (missä on matkojakin).
13.7.2006 Jouni Tuomisto
Hip hurraa! Onnistuin ratkaisemaan Waiting-ongelman siten, että laskenta vieläkin nopeutui noin varttituntiin ja muistia vaaditaan pahimmillaan 1.2 GB. Ajoin koko mallin Output1:een asti kannettavalla, ja aikaa meni 75 min. Kuitenkin silloin huomasin, että matkojen kokonaismäärä on 3* liian suuri, ja siitä päädyin huomaamaan, että matkamatriisit eivät ihan vielä ole johdonmukaisesti rakennettuja. Koska HLT2005 sisältää myös kulkutapatietoa, sitä pitäisi käyttää. Mutta jos käytetään, adjusted_trip_rate joka skaalataan henkilöautomatkoihin on väärin. Tämä pitää siis vielä miettiä ja korjata.
Se olikin aika helppo ja on nyt korjattu.
Mutta vielä tuli isompi kysymys: miten lasketaan joukkoliikenteen lopputulemat? Nythän kaikki Tripsistä lähtevä on vain yhdistelmäliikennettä, eikä joukkoliikennettä malliteta ollenkaan.
Koska mallissa ei huomioida joukkoliikenteen ajoneuvojen kokoa, ei myöskään pystytä laskemaan ajoneuvokilometrejä, eikä siksikään etteivät vuorovälit ole realistisia. Ajoneuvotyypit eivät myöskään ole tiedossa, joten matkojen määrä eri aikoina, pituuksilla ja alueilla on riittävä tieto. Odotusajat olisi kyllä kiva tietää, mutta koska joukkoliikennettä ei mallinneta ajoneuvon tarkkuudella, tämä ei ole mahdollista. Vaihtojahan ei oleteta olevan ollenkaan eli jos suoraa yhteyttä ei ole, matka siirtyy yhdistelmäliikenteeseen.
Yhteenvetona siis voi sanoa, että joukkoliikennematkojen määrä on riittävä tieto vaikutusten arvioimiseen. Tähän vaikuttaa joukkoliikennematriisi, joka riippuu joukkoliikennematkoista seuraavasti:
- Jos matkojen määrä tietyllä välillä on suurempi tai yhtä suuri kuin Public level, niin reitti kuljetaan. Jos Public level siis on 0, kuljetaan kaikki olemassaolevat reitit.
Public trips per linkin avulla pitäisi piirtää joukkoliikennekartta siitä, mitä reittejä on mukana missäkin skenaariossa ja kuvata nämä sitten yhdessä yhdistelmäliikenteen kustannusten kanssa. Tämä ei riipu skenaarioista, joten sen voi tehdä milloin vain.
Poistin Felxible fractionin toiminnasta, koska sitä en ole koskaan tarvinnut, ja toteutustapa tuntuu nyt huonolta. Ehkä koodinkin voisi poistaa kokonaan, mutten sitä vielä tee. Teinpäs kumminkin sen, että pooistin sen Input_var-indeksistä, jotta sen rakennetta ei tarvitse mennä muuttelemaan sitten, kun skenaarioita ehkä on ajettu.
Nyt siitä oli lisähaitta, että kun Scenario_inputia muutti, Public trips per link:n arvot unohtuivat, ja se on hidas laskea, melkein puoli tuntia. Nyt se lasketaan vain kerran, vaikka skenaariot vaihtuisivatkin. Eipäs muuten olekaan, ylävirrassa valitaan matkamatriisi, joten kaikki unohtuu joka tapauksessa. Tätä pitäisi ehkä miettiä, saisiko sen laskettua muuten. Joko on tehtävä niin, että matkamatriisia ei muutella skenaarioiden välillä, tai sitten Public_trips_per_link lasketaan vakiomatriisista. Ensimmäinen on järkevämpi, ja sen mukaisesti siis muutetaan. Lisätään yksi choice väliin, niin muistaa paremmin että sitä voi muutella.
Tulipa muuten mieleen, että voisi ottaa käytännöksi sen, että ajaisi skenaarioita aina jonkun vakiomäärän, semmoisen minkä yhdessä tai kahdessa yössä ajaa. Sitten olisi helpompi vaihdella skenaariopaketteja.
14.7.2006 Jouni Tuomisto
Huomasin kumman ominaisuuden vai liekö bugi:
a:= if a=0 or isnan(a) then 100u else a; antaa isnan-funktion arvoksi false vaikka a=0/0, mutta kun sama erotetaan kahdelle riville, ei tule virhettä.
15.7.2006 Jouni Tuomisto
Olen nyt päivittänyt kustannuspuolta. Keksin muuttaa käytettävät indeksit dynaamisiksi sten, että ainoastaan skenaarioissa käytetyt arvot tulevat indeksiin. Näin säästyy palson muistia, eikä tarvita ollenkaan Choose_var-solmujja.
Koko Cost-puolen koodaus ja indeksit on käytävä läpi. Vanhassa versiossa on ollut vehicle_noch- tai vehicle_indeksi käytössä, ja näitä löytyy vähän joka koodista. Toisaalta Park rush veh-rivi on pistänyt asioita uusiksi. Esim. Cars needed-solmu on laskettava uusiksi park rushveh-riviltä. Muutenkin on hankala ymmärtää mitä vanha koodi teki, mutta kai siinä järkion.
Bussimatriisin etäisyydet pitää laskea erikseen, koska reitit eivät ole identtiset autojen kanssa. tämä onnistuukin mutta on tehtävä staattinen indeksi links_pub kuten links_1. Tätä ei kuitenkaan kannata tehdä staattiseksi ennen kuin on lopullinen joukkoliikennematriisi. Niinpä nyt lasketaan se staattisesti.
17.7.2006 Jouni Tuomisto
Linkki 1011,1012 puuttuu normaalista tiestöstä, mutta bussit ajavat sitä. Niinpä se on lisättävä Link_length-tauluun. Tein sen jo, mutta linkin pituus on tarkastettava kartasta. Sama pätee pareille 1014,1015; 1010,1011; 1001,1011; 1001,1015. Lisäksi näitä pareja tulee lisää, kun From määritellään isommaksi.
Aggr_length sisältää nyt oletuksen, että Mode1 otetaan mukaan (koska ylävirran Distance sisältää tuon indeksin). Tuohan on niin kuin pitää, mutta kaikki solmut on katsottava läpi, että tulokset ovat oikein, tai sitten vain scisattava muut vasteen tuin yhdistelmäliikenne pois. Nythän pointtina on vertailla tuloksia vanhan tutkimuksen kanssa.
18.7.2006 Jouni Tuomisto
Vehicle_km tehdään siten, että Vehicle_typeen tehdään yksi uusi rivi Bus, joka on 50 hengen bussi. Bussikilometrit lasketaan erikseen sen perusteella, onko linja toiminnassa vai ei perustuen joukkoliikenteen Distanceen. Oletetaan että busseja lähtee vakioaikavälein ajamaan reittiä vakionopeudella, jolloin tarvittavien bussien määrä on distance/vehicle_speed/0.5 h jos vuoroväliksi halutaan puoli tuntia. Mutta nyt iso kysymys kuuluu, miten määrätään se, miten bussi ajaa tai ei aja. Jos monta bussivuoroa ajaa samaa reittiä, mitkä busseista ajetaan ja mitkä ei? Bussimatriisi pitäisi pystyä palauttamaan jotenkin takaisin bussilijoiksi, jolloin nähtäisiin, mitkä vuorot ovat olemassa, ja sitten voitaisiin laskea, tuo autojen tarve. Tämä kuitenkin vaatii melkoista pohdintaa, eikä valmiita ideoita ole.
20.7.2006 Jouni Tuomisto
Pitää pohtia se, miten Time_cost oikeasti lasketaan. Onko siis keskimääräinen odotusaika laskettu jokaiselle Composite_tripsille (jotka voivat oikeasti olla matkan puolikkaista) vai jokaiselle alkuperäiselle matkalle (Trips_per_period). Tämä varmaan vaikuttaa lopputulokseen aika lailla. Toinen kysymys on se, että Trips_per_period on indeksoitu Mode1:llä, mikä ei kustannuslaskentapuolella ole yleistä ja aiheuttaa siten harmia.056,272,148,122,797,145,476,22465535,54067,19661Log 1.420.7.2006 Jouni Tuomisto
Otin käyttöön uuden versionumeron, koska päätin kasvattaa Vehicle_type-indeksiä yhdellä rivillä eli Bus. Tämä vaikuttaa niin moneen asiaan, että oli syytä vaihtaa numeroa. Tämä liittyy Trips_per_period-korjaukseen, jonka avulla mode1 voidaan päätellä vaikka sitä indeksiä ei käytettäisikään. Eli Bus=Public, Minibus ja Car (d)=Composite ja Car (g)=Car.
Tämän takia on korjattava ainakin Emission_factor, Fuel consumption, Vehicle_lifetime, Vehicle_price.
Laskentaan tuli ongelmia: Kun yritetään laskea mean tai sample, alkaa Car_fr-indeksi herjata. Huomasin tämän vasta nyt, koska kaikki tähänastiset solmut ovat laskeneet odotusarvoisesti mid. Pistin Luminaan meilin, ja sieltä ehkä jotain kuuluu.
Uusien bussilinkkien Distances on tarkastettava.
Mirror2 voisi nimetä Mirror.
Public matrix alkaa olla liian kova pala tälle illalle. Idea on se, että lasketaan joka linkille matkustajasuorite ja katsotaan täyttyykö kriteeri. Sitten katsotaan jokainen bussireitin etappi ja katsotaan, onko tuolla etapilla riittävästi matkustajia. Jos on, bussi ajaa reitin. Jos taas ei ole, reittiä supistetaan distaalisesti kunnes tulee ensimmäinen etappi, jossa on kriteerin täyttävä matkasuorite. Ajattelin toteuttaa tämän cumulate-funktiolla, koska muuten on hankalaa ajaa semmoiset etapit, jotka ovat keskellä reittiä mutta jostain syystä hiljaiset. Kuitenkin homma meinasi jo tökätä siiten, etten saanut verrattua matkakriteeriä eri etapeilla. Nyt annan tämän olla, koska huomenna pitäisi olla malli joka ajaa jotakin.
Teen siis viisi skenaariota ja pistän ne yöksi ajamaan. Virpi sanoi että kymmenellä oli loppunut muisti, mutta se on minusta kummallista, ja itse sain ajettua yhden skenaarion 1.2 GB:llä, mikä ei ole lähelläkään ylärajaa. Nyt kun on vielä Matkamatriisisolmuja siistitty, on toivottavasti säästetty laskenta-aikaa rutkasti. Tämä ei kyllä taida muistiin auttaa.
Viiden skenaarion ajo onnistui, ja muistia meni noin 1.4 GB. Jos tuo lievä lisääntyminen johtuu skenaarioiden määrästä, on tässä ongelma, muttei se ole selvää. Ajankäyttö oli seuraavanlaista:
TextTable Value Timing_profile
Objects
Time_shift 3532.710
Trips 2751.996
All_trips 1907.084
Vehicle_by_type 1365.712
Route_matrix 1219.333
Aggr_period 1117.199
Iterator 884.382
Links2 576.078
Vehicles_per_link1 178.697
Aggr_zone 134.897
Vehicle_balance 86.675
Public_matrix 69.347
Transfer_point 44.925
Flow 44.714
Distances 31.245
Public_trips_per_lin 26.549
Aggr_length 24.594
Trips_by_hour 23.224
Vehicle_km 22.604
Trips_per_link_bau1 21.671
Unadjusted_trip_rate 18.627
Trips_by_type 15.242
Region_explode 10.575
Hlt_trips_by_hour 7.340
Mirror2 6.369
Adjusted_trip_rate 4.877
Bus_route_list 3.716
Link_intensity 3.706
Si_pi 2.573
Outputs1 2.236
Road_mirror 1.603
Descendant_objects 1.128
All_trips2 1.071
Bus_matrix 1.021
Bus_links 0.671
Composite_trips 0.541
Hlt_trips 0.421
Link 0.421
Total_vehicle_need 0.381
Delay 0.320
Cumulative_balance 0.280
Clean_rows 0.240
Areal_vehicle_peak 0.170
Drop_points 0.151
Zones 0.110
Vehicle_type 0.080
In_area_distance 0.060
Link_length1 0.040
Hlt2004_05 0.030
Route_list1 0.020
Links_1 0.020
Drop_length 0.010
Place_weight_by_hour 0.010
Rows 0.010
Trip_activity 0.010
To1 0.010
Routes_inside 0.010
All_bus_routes 0.010
Vehicle_types 0.001
Vehicle_size 0.001
Place1 0.001
Place 0.001
Guaranteed_areas 0.001
Scenario_input 0.001
Total_trips 0.001
Trips_municipality 0.001
Trips_place 0.001
Trips_place_mode 0.001
Municipality 0.001
Municipality1 0.001
Time_of_day 0.001
Time_in_traffic 0.001
Car_trips 0.001
Time_of_day_by_hour 0.001
Inhabitants 0.001
Workplaces 0.001
Municipality_info 0.001
Trips_place_munic 0.001
Municipality_info_hl 0.001
Mista 0.001
Mihin 0.001
Klo 0.001
Inhabitants1 0.001
Workplaces1 0.001
Hour 0.001
Zone 0.001
Traffic_speed 0.001
Time_unit 0.001
From 0.001
Area1 0.001
Region 0.001
Vehicle_noch 0.001
Area2 0.001
R_t 0.001
Bus_routes_special 0.001
Bus_route_ends 0.001
In3 0.001
In4 0.001
Roads 0.001
Routes_outside 0.001
Route_list 0.001
Regions 0.001
Time_of_day_by_time 0.001
Scenarios 0.001
Scenario 0.001
Time_stat 0.001
Period 0.001
Scen_ind 0.001
Scenario1 0.001
Seuraavat hommat:
- Ajaa erilaisia määriä skenaarioita, jotta voidaan testata muistin riittävyys.
- korjata Public matrix niin, että se oikeasti huomioi matkamäärät.
- Tehdä solmu, joka laskee joukkoliikennematkoille lopputulemat: vehicle_km, park rush veh, waiting.
-Tarkistaa kustannuslaskentapuoli ja yrittää saada se laskemaan myös epävarmuuksia.
-Lisätä semmoinen lopputulema, joka kertoo montako matkustajaa jäi ilman haluttua joukkoliikennettä.
21.7.2006 Jouni Tuomisto
Kustannuslaskentapuolelta löytyi oikea bugi (ks Dale Ricen meili eilen), mutta se onnistuttiin korjaamaan.
Sain ratkaistuksi myös bussimatriiseja vaivaavan ongelman, ettei sitä voinut laskea jos mukaan otettiin aikaulottuvuus. Nyt pitäisi miettiä, mitä oikeastaan halutaan. Bussimatriisin räjäyttäminen Timella voi teettää arvaamattomia muistiongelmia. Toisaalta koko bussivaikutusten laskeminen on vielä tekemättä eikä tämä välttämättä ole mikään ongelma. Ehkä kuitenkaan Distancea ei kannata indeksoida Timella.
Nyt on mallissa kaksi Time_of_dayta. Tarvitaanko eri luokittelua toisaalta matkoille ja toisaalta reiteille on epäselvää, mutta toistaiseksi säilytetään jaottelu koska sitä ehkä tarvitaan.
26.7.2006 Jouni Tuomisto
Mitkä ovat siis ne bussimatriisit, jotka halutaan ulos tästä mallista?
1) Sama matriisi kuin reittimatriisi eli bussit ajavat missä vain
2) Laajin nykyreitistöön perustuva eli päiväaikayhteydet voimassa aina
3) 2) mutta vain nykyvuoroilla
4) 2) mutta rajoitettuna matkustajakriteerillä
5) 2) mutta rajoitettuna sekä 3) että 4)056,296,148,122,539,68,477,22465535,54067,19661Iterator1140,140,1132,121,0,0,1,0,0,0,72,0,1IteratorLog 1.526.7.2006 Jouni Tuomisto
Otin käyttöön Public matrix-rivin Scenario inputissa, jolloin rakenne meni uusiksi ja tuli perustelluksi vaihtaa versionumero 1.5:ksi. Nyt on siis 5 eri mahdollista Public matrixia:
1) Sama matriisi kuin reittimatriisi eli bussit ajavat missä vain
2) Laajin nykyreitistöön perustuva eli päiväaikayhteydet voimassa aina
3) 2) mutta vain nykyvuoroilla
4) 2) mutta rajoitettuna matkustajakriteerillä
5) 2) mutta rajoitettuna sekä 3) että 4)
Matriisi valitaan numerolla Scenario inputista, joten sitä voi muuttaa kesken ajon.
Ohessa on tulos, joka kertoo montako reittiä on tarjolla eri matriiseissa (maksimi näillä oletuksilla 30.72k eli tuo ensimmäinen): 3.072e+004
1.908e+004
1.232e+004
3330
3060
Matriisit 4 ja 5 ovat noin pieniä, koska volyymirajoitus on 1000 eli vahva kriteeri. siis aika kriittinen tekijä, ja yllättävän vahva. Pitää miettiä, ettei olisi virhettä jossain.
Pohdinnan ja parin testauksen jälkeen päädyin siihen, ettei laskennan perusteella ole enää virheitä. Yhden korjasin, kun Bus_routes_used_tod_vol käytti All_bus_routes:ia eik All_bus_routes_tod:ia kuten piti.
Nyt tulee mieleen semmoinen ajatus, että Bus_links ehkä kannattaisi indeksoida Etapilla, niin silloin olisi mahdollista seurata esim. matkustajamääriä ilman muita kaavamuutoksia. En ole varma, tulisiko tästä jotain sivuvaikutuksia.
Nyt on hankala hahmottaa, mitä oikein olen tekemässä, ja onko minulla ollenkaan oikea solmujaottelu Public matricesissa. Olisi helpompaa, jos tod/ei-tod ja vol/ei-vol toteutettaisiin sitä mukaan kun asiat tulevat vastaan, niin ei tarvitsisi niin paljon haaroa tätä prosessia. Mutta toinen kysymys on se, voisiko tätä yksinkertaistaa niin, ettei tarvitsisi muuttaa from-to-lähestymistavan ja bus_routes-indeksin välillä edestakaisin vaan vain yhteen suuntaan eli käytännössä matkat muutettaisiin bus_routesiin. Tämän pitäisi tapahtua seuraavasti:
-Perusrunkona on bus_routes*etappi, jonka avulla voidaan määritellä reitit pysäkin tarkkuudella.
-From*to muutetaan reitinpituussäännön mukaisesti oikealle bussireitille eli pisimmälle joka on sopiva.
-Tällöin katoaa tieto siitä kuka on menossa minnekin, mutta jokaisen linkin aktiviteettitieto säilyy.
-Tämä voidaan summata periodiin, zoneen ja distanceen kunhan nämä määritellään, joten raportoinnissa ei tule ongelmia.
-Eli ensimmäisenä määritetään reitistö.
- Toiseksi tätä rajoitetaan sen mukaaan, käytetäänkö vuorokaudenaikakriteeriä vai ei
-Sitten muunnetaan Adjusted trip rate tähän muotoon käyttämällä tätä alustavaa reitistöä.
-Verrataan matka-aktiviteettia Public level-kriteeriin
-Jos kriteeri täyttyy, ko. etappi otetaan varsinaiseen reitistöön
-All trips muutetaan tähän muotoon käyttämällä lopullista reitistöä.
-Katsotaan, paljonko matkoja mihinkin tulee, ja millä busseilla mitäkin ajetaan.
-Kysymys: pitääkö bussireitistö mirrorata heti alussa? Tämä olisi sikäli järkevää, että vastakkaisiin suuntiin menevät matkat eivät mene sekaisin. Toisaalta olisi hyödyllistä tarkastella näitä yhtenä kokonaisuutena kun reitistöstä päätetään. Joutuuhan bussi palaamaan jos se on jonnekin mennyt, eli reitistön pitää olla symmetrinen vaikkeivät matkat olisi. Tähän taitaa olla ratkaisuna varhainen mirrorointi mutta myös alusta asti tehdään joku aputaulu, jonka avulla puoliskot voidaan yhdistää yhdeksi reitiksi.
-Ongelma: clean_rows olisi hyödyllinen, mutta pystyy siivoamaan vain yksiulotteisia tauluja. Olisikin hyödyllistä muuttaa tämä moniulotteiseksi ominaisuudeksi siten, että kaikkien muiden ulottuvuuksien suhteen jos ruudut ovat tyhjät, rivi poistetaan kiinnostavan ulottuvuuden suhteen. Tämä ei kuitenkaan ole ratkaisevaa systeemin toimivuudelle joten jätetään hautumaan.
Tämä versio ei ehkä pysty laskemaan, koska en ole testannut muistinkulutusta ja laskennan yhteensopivuutta loppuun asti.
30.7.2006 Jouni Tuomisto
Viimeisin sana on va6.
31.7.2006 Jouni Tuomisto
Nykyisessä lähestymistavassa on seuraavia ideioita ja ongelmia:
-Etsitään se reitti, jossa on mainittu sekä lähtöpaikka että määränpää.
-> Samat paikat voidaan mainita usealla eri reitillä, eikä voida tietää mikä niistä on oikea.
->Jos lähtöpaikka ja määränpää on sama, matka monistuu myös epämääräisiä kertoja.
--> Ratkaisu palautuu taas siihen, että etukäteen on määrättävä mikä route kuuluu kullekin from/to:lle.
-Public trips per link ei ota huomioon sitä, että eri bussilinjoja voi ajaa samoja linkkejä pitkin. Eli Linkkiaktiivisuuksia ei voi palauttaa reitistöaktiivisuudeksi.
Uusi ehdotus Va8:
-Selected routes kertoo reitistön.
- Bus route # kertoo mitä bussireittiä käytetään milloinkin.
- Route activity kertoo aktiivisuuden linkeittäin:
--Otetaan yksi from/to matkamäärä kerrallaan
--Valitaan se reitti, jolle se kuuluu
--Etsitään se linkkijono, jolle se kuuluu
--Lisätään matkat siihen.
-Tehdään joka from/to:lle ja samalla summataan tulosta.
Nyt sain tämän toimimaan, sikäli kuin pystyn ymmärtämään solmun toimintaa tähän aikaan yöstä.
3.8.2006 Jouni Tuomisto
Tein prosessin kulun itselleni helpommaksi siten, että tein useita funktioita jotka tekevät bussireittimatriisille erilaisia muunnoksia. Funktioiden avulla homma saadaan modularisoitua ilman solmuvyyhtejä, ja asioiden hahmottaminen on helpompaa. Nyt homma näyttää toimivan, jjoskin lopullinen koodisto on huolellisesti tarkastettava. Jätän tämän kuitenkin seuraavaan versioon 1.6, ja nyt tallennan viimeisen alaversion 1.5.4.
056,320,148,122,549,50,476,22465535,54067,19661Log 1.63.8.2006 Jouni Tuomisto
Tein prosessin kulun itselleni helpommaksi siten, että tein useita funktioita jotka tekevät bussireittimatriisille erilaisia muunnoksia. Funktioiden avulla homma saadaan modularisoitua ilman solmuvyyhtejä, ja asioiden hahmottaminen on helpompaa. Nyt homma näyttää toimivan, jjoskin lopullinen koodisto on huolellisesti tarkastettava. Jätän tämän kuitenkin seuraavaan versioon 1.6, ja nyt tallennan viimeisen alaversion 1.5.4.
Versiomuunnoksessa tuli taas häsää solmujen siirtelyn takia, mutta sen sain kyllä korjattua aika helposti.
6.8.2006 Jouni Tuomisto
Koodia on viilailtu ja saatu se aika mukavasti pyörähtämään. Bussien osalta luovuttiin Time-resoluutiosta ja käytetään vain time_of_day1-resoluutiota. Syy tähän on laskenta-aika mutta myös se, että bussien kulkuaikoja ei voi koko ajan muutella vaan ihmisten on pystyttävä muistamaan, montako kertaa ja mihin aikoihin bussin tunnin aikana kulkevat - oli matkustajia tai ei.
Jotta tämä toimisi, on time_of_day1 ja period täsmättävä siten, että luokat saadaan menemään mukavasti, tai sitten on tehtävä jakoalgoritmi bussivuorojen jakamiseksi Periodiin. Jälkimmäinen toteutettiin.
7.8.2006 Jouni Tuomisto
Malli toimii nyt muuten hyvin ja laskee bussisuoritteen myös mutta ongelmaksi tuli se, että laskenta-ajat venyvät kohtuuttomiksi, koska Etappimatkojen laskenta pitenee eksponentiaalisesti kun alueiden määrä lisääntyy. Tämä johtuu siitä, että bussireitti-indeksi pitenee myös tyhjillä reiteillä. Nämä pitää siis saada pois. Tämän takia editoidaan Etappimatkat ja Active_routes. Tämä onnistuikin, mutta tulos oli laihempi kuin odotin.
Nyt älysin, että ongelmana eivät ole pelkästään tyhjät rivit, koska Etappimatkat-funktiota käytetään myös laskettaessa Active_routesia, jolloin tyhjiä matkoja ei vielä ole. Tällöinhän erotellaan potentiaalisista bussireiteistä ne, joilla on oikeasti matkustajia. Tämä on kiperä ongelma, koska jotta tämän voi tehdä oikein, on pystyttävä laskemaan matkustajat bussireiteittäin ja etapeittain meno- ja paluusuunta erikseen eri vuorokaudenaikoina. Ja tämä on vielä laskettava kaikista from/to-pareista (joissa matkustajamäärä>0), joten mitään oikotietä en ole tähän keksinyt. Kuitenkin helpotusta tuo asiaan se, että nuo hurjat laskenta-ajat tulevat Public_matrix=1:llä, joka on koko tiestöstä laskettu bussimatriisi. Siinä versiossa on muistaakseni 6000 bussireittiä, ja tämä aiheuttaa hurjan laskentatarpeen. Kun kuitenkin public_matrix=2 tai 3, niin bussireittejä on parisataa, mikä on ihan inhimillistä. Ainakin 50 kaupunginosaa pystytään laskemaan läpi 330 s/skenaario, mikä on nopeammin kuin versiolla 1.0. Public_matrix=1:llä laskin yli 40000 s yhtä skenaariota, ja sitten kyllästyin, joten en edes tiedä kauanko sen ajamiseen menisi. Jos ei tule ikäviä yllätyksiä 129 kaupunginosalla, taidamme jättää tämän ongelman omaan arvoonsa, ja laskea public_matrix=1:llä vain pari harvaa ja valittua skenaariota jossain vaiheessa, kun muita tuloksia on jo mietittäväksi.
Bussireitistössä huomasin virheitä: pilkkujen paikalla on pisteitä, ja löytyi ainakin yksi alue 11118. Nämä pitää oikolukea ennen varsinaisia ajoja. Tähän ei kannata korjata, koska Hanna on tekemässä uusia reitistöjä.
Lisäksi pitää tarkastaa, että Link length sisältää myös kaikille bus_linkseille pituustiedon.
Etappimatkat-funktion siistiminen on tosi hankalaa, koska laskentaa taitaa yksinkertaisesti olla niin paljon. Ainakaan mitään turhia indeksejä ei laskennasta löytynyt, niitä on tarkoituskin olla aika monta. Nyt laskenta-ajat ovat kuitenkin siedettäviä, kun pakotin public_matrix=2, jolloin käytetään yksinkertaisinta bussireitistöä. Pakotuksen etuna on se, että reitistö lasketaan vain kertaalleen kaikille skenaarioille, koska se ei voi muuttua. Näin voidaan jättää siistiminen myöhemmäksi ja aloittaa tuotantoajot mallilla.
Seuraavaksi poistelen turhia solmuja ja otan käyttöön version 1.7.
056,344,148,122,650,22,476,24865535,54067,19661Log 1.78.8.2006 Jouni Tuomisto
Tällä versiolla pitäisi nyt pystyä ajamaan tuotantoajoja siten, että myös bussien kustannukset ovat mukana. Pistetään malli ajamaan johonkin joutilaaseen koneeseen.
Yritin ymmärtää Car capital valuation ja Willingness to drive -moduleita, mutta päätelmä on, että Cab_variab_2 ja Drive_variab_2 ovat ihan turhia: ensin satunnaisluku järjestetään ja sitten sekoitetaan uudestaan. Korjaan tämän mutta jätän nuo kaksi solmua selityksineen jäljelle.
Cost strength variabilityssä oli määritelmässä -Drive_variation. En ymmärrä, miksi oli tuo etumerkki, koska kai kustannus otetaan kustannuksena, vaikka se olisi negatiivinenkin (eli siis tuloa).
Nyt älysin, mistä johtuu 30 e kustannus/matka henkilöautoilla pääomakuluina: trips_per_periodia käytetään ikään kuin se olisi indeksoitu vehicle_typellä, mutta oikeastihan se on all trips, joka on indeksoitu mode1:llä, ja tämä on muutettava takaisin oikeaan indeksiin. Tämä aiheuttaa arvaamattoman paljon korjaamista, mutta paras vain aloittaa.
Löysin bussireittien pituuden laskennasta semmoisen virheen, että bussit käyttävät mittaamattomia linkkejä. Niihinhän aikanaan pistin 9999 km pituudeksi, että jos niitä jostain syystä käytetään niin sen huomaa; ja niin huomasikin. Linkkien pituudet siis täytyy tarkastaa, kunhan bussireitit ovat lopullisia ja kaikki linkit tiedossa. Tässä samassa yhteydessä päivitin linkinlaskentasolmun, ja vanhat tosi hitaat solmut voi poistaa kokonaan.
Nyt ymmärsin, miksi Cab_variab_2-solmu on olemassa: se sorttaa kaikki vaihtelujakaumat pienimmästä suurimpaan eli tietty fraktiili on kaikissa samassa iteraatiossa. Sitten nämä sotketaan, ettei tulisi johdonmukaisuutta iteraatiosta toiseen. Tällä tavalla vaihtelu pysyy suurena ja epävarmuus pienenä, koska vaihtelun odotusarvohan on eri vaihtelujakaumien keskiarvo; jos näitä ei järjestettäisi, vaihtelujakauman odotusarvo alkaisi kaventua sitä enemmän, mitä enemmän olisi eri vaihtelujakaumia tarjolla - riippumatta siitä, paljonko ne poikkeavat toisistaan. Eli alkuparäinen oli oikein, ja nyt pitää keksiä, miten se lasketaan ilman indeksiherjaa.
Mutta kumma juttu on, etten saa nyt toistettua eilen tullutta virhettä. Onnistunpas: se tulee Cap:lla mutta ei Drive:lla, ja hassua on, että Cost-Strnegth on laskettu Capin avulla ja se kuitenkin toimii. Ehkä kyseessä on väärin aktivoituva virheilmoitus, eikä se vaikuta laskentaan.'
Kustannuslaskennassa on häsää, koska eri kustannuslajien indeksit ovat erilaiset eikä niitä ole yhdenmukaistettu. Nyt päätetään, että heti alussa summataan zone ja length pois, koska bussikilometreissä ei näitä ole mielekästä erotella. Näin vältytään ikäviltä harmeilta myöhemmin. Jos näitä joskus halutaan erikseen tarkastella, pitää miettiä miten se bussien osalta toteutettaisiin.
Nyt koodit ovat ojennuksessa, mutta näyttää siltä että laskenta tuottaa yllättävän suuria kustannuksia. Pitää perehtyä siihen, onko kustannus oikeasti noin iso vain onko versioon 1.0.1 jotain muutosta. Luvut ovat kuitenkin moninkertaiset esim. vehicle-kustannuksen osalta. Tämän tarkastuksen tulos oli odotettu: joitakin indeksejä summattiin kahteen kertaan, jolloin kustannukset tietysti moninkertaistuivat. Nyt olen katsonut tulokset ja solmut Cost to stakeholder-solmuun asti, ja näyttää järkevältä. Odotusaika tulee nyt huomattavan kalliiksi, melkein 1.5 euroa yhdistelmäliikenteessä. Tämä ei ole sinänsä yllättävää, olennaista on katsoa miten siihen voi vaikuttaa. Bussikustannuksia ei voi vielä arvioida, koska data on laskettu virheellisellä mallilla.
10.8.2006 Jouni Tuomisto
Korjasin 1.7.2-versioon Distances-solmua, mutta se näköjään meni pieleen koska ajossa 61000/62000 sekuntia ajettiin Distancea ennen kuin muisti loppui. Alkuperäinen on Distances1, yritelmä on Distances2 ja korjattu Distances, jonka nyt pitäisi toimia ja testissä laskee nopeasti.
Eilen tein myös Links_alt-solmun, jolla voi laskea kaikki linkit, niin tavalliset kuin bussitkin. Tämän avulla pitäisi tehdä uusi linkkilistaus sitten, kun bussireitit on päivitetty.
056,368,148,122,806,43,476,24865535,54067,19661Outputs1140,188,1132,121,0,0,1,0,0,0,72,0,1Outputs1Scenarios0140,44,1132,121,0,0,1,0,0,0,72,0,152425,39321,65535ScenariosSelect trip matrix0140,68,1132,121,0,0,1,0,0,0,72,0,152425,39321,65535Select_trip_matrixChoose pub matrix0140,92,1132,121,0,0,1,0,0,0,72,0,152425,39321,65535Choose_pub_matrixVehicle_noch0140,116,1132,121,0,0,1,0,0,0,72,0,1Vehicle_nochLog 1.822.8.2006 Jouni Tuomisto
Pikainen kertaus versioista 1.7.2-1.7.8:
Mallissa oli useita muisti- ja ajoaikaongelmia, joita viilattiin eri versioissa kun toisaalta yritettiin ajaa järkeviä tuloksia. Ainakin tämmöisiä muutoksia tehtiin:
- Waiting-solmua viilattiin taas. Kokeilin myös sen pilkkomista kolmeen osaan, ja tämä kyllä onnistuikin. Mutta kuitenkaan mitään muistiongelmaa tämä ei näyttänyt ratkaisevan, joten jätettiin se ennalleen. Nykyisellään solmu on kuitenkin siedettävä laskenta-ajaltaan, eikä muistitarve kaada konetta.
- Aggr_period-funktio on hankala, koska sillä lähdetään isointa arrayta summaamaan, ja summaus tilapäisesti suurentaa muistitarvetta. Tämä ratkaistiin siten, että sen sijaan että olisi tehty for x[]:= period, niin tehtiinkin slice(solmu,time,x) ja x juoksutettiin yli ajan. Tämä kyllä säästi jonkin verran muistia, mutta se kostautui laskenta-ajassa. Tämä ei kuitenkaan ole kriittistä, koska 10 skenaariota ajautuu helposti yön aikana, ainakin minun pöytäkoneellani.
-Vehicle_by_type nousi vähän yllättäen muistisyöpöksi solmuksi, koska se ei ole erityisen vaikeaa laskentaa. Tämä ratkaistiin while-do-luupilla, ja muistitarve saatiin painettua siedettäväksi.
Sain tänään valmiiksi ajon 11, ja kopioin tulokset kustannuslaskentapuolelle. Siellä huomasin yllätykseksi, että Park rush veh-outputin rush-osio tuottaa pelkkää NANia. Tämän syy ei ole selvinnyt, koska nyt kun testasin asiaa, muuttuja laskee oikein. (Välillä tosin Time-indeksi määritelmä unohtui ohjelmalta tuntemattomasta syystä, mutta tämä ei voinut olla ongelmana yöllisessä laskennassa.) Pitää laskea tämä uudestaan ensi yönä ja toivoa, että ongelma on korjautunut. Kuitenkin tein myös semmoisen muutoksen, että Outputsissa kaikki tyhjät ruudut muutetaan nolliksi siten, ettei yhtään NANia normaalitilanteessa pitäisi olla lopputuloksessa. Tämä siis helpottaa mahdollisten ongelmien huomaamista.
Ollin juuri laskemassa Ajossa 13 Parkrushveh-tulos näyttää ihan oikealta, eikä Rush anna virheilmoituksia. Erona on se, että Ajosta 11 puuttuvat Vehicle_nochista d7, d6, d5, c7, c6, c5. VOisiko tämä olla selitys? (Toivottavasti, sillä silloin muita ei tarvitse ajaa uuelleen).
28.8.2006 Jouni Tuomisto
Imuroin tähän mennessä lasketut ajot 11, 12, 13, 16, 19, 21 ulkoiseen moduliin Scenario_results_jtt.ana. Sitten tein solmun, jolla valitaan mikä ajo otetaan tarkasteluun, ja tämä virtaviivaisti tulosten tarkastelua. Seuraavaksi annan tulokset Virpille ja Hannalle ihmeteltäväksi. Tuleeko tolkkua, missä on laskentavirhettä?
12.9.2008 Jouni Tuomisto
Perehdyin eri malliversioihin ja päädyin siihen, että <a href="http://ytoswww/yhteiset/Huippuyksikko/Tutkimus/R79_CompositeTraffic2/Mallit/Composite_traffic_1_8.ANA">versio 1.8</a> on kaikkein uusin ja paras, ja siitä pitää kehitystyötä jatkaa. Julkaistua malliversiota toki voi käyttää julkaisussa olevan datan syöttämiseen esim tulostietokantaan. Niinpä tehtiin 1.8.1-versio, johon tehtiin seuraavaa:
* Muutettiin ä ja ö nykyisen Analytican mukaisiksi.
* Poistettiin kaikki argumentti- ym kuvailusolmut lokeja lukuunottamatta.
* Scenario_results, joka oli ulkoinen moduli, muutettiin sisäiseksi, jotta tiedostoversiointi ei menisi sekaisin. Ajettu datahan pitäisi joka tapauksessa erottaa mallista, joten tämän käytännön toteutus pitää ratkaista ennen mallin lataamista wikiin. Ehkä tulokset pitäisi laittaa "tiedostopalvelimelle" eli N:lle Excel-tiedostoina ja linkata OLE:lla? Onko sen kummempi kuin ulkoinen modulikaan? Molemmissa on ongelmana linkin katkeaminen.
056,392,148,122,732,59,476,57665535,54067,19661Choose_ajo0152,212,1144,121,0,0,1,0,0,0,177,0,152425,39321,65535Choose_ajoTähän mennessä ajettu:
11
12
13
16
19
21160,312,-148,78New:URN:NBN:fi-fe200809121937120,472,1112,16Opasnet base connectionInterface for uploading data to and downloading from the Opasnet Base.
<a href="http://en.opasnet.org/w/Image:Opasnet_base_connection.ANA">Wiki description</a>Jouni Tuomisto9. maata 2008 10:42jtue2. huhta 2009 17:12 48,24352,424,048,291,0,0,1,1,1,0,0,0,01,26,12,750,596,172,102,90,476,224Arial, 15100,1,1,1,1,9,2970,2100,1,0This module saves model results into the Opasnet Base. You need your Opasnet username and password for that. You must fill in all tables before the process is completed. Fill in the data below from top to bottom.
If an object with the same Ident already exists in the Opasnet Base, the information will be added to that object.
Before you start, make sure that you have created an object page in the Opasnet wiki for each object (study or variable) you want to upload.304,76,-1296,68Username0320,156,1160,121,0,0,1,0,0,0,142,0,152425,39321,65535Opasnet_usernamePassword0320,180,1160,121,0,0,1,0,0,0,142,0,152425,39321,65535Opasnet_passwordNumber of indices0156,380,1140,121,0,0,1,0,0,0,72,0,152425,39321,65535N_indicesNumber of observations0156,428,1140,121,0,0,1,0,0,0,72,0,152425,39321,65535N_observationsObservations0156,452,1140,121,0,0,1,0,0,0,72,0,152425,39321,65535ObservationsStudy or variable info0292,525,1140,131,0,0,1,0,0,0,90,0,152425,39321,65535Object_info1Upload a data table. Indices are determinants of your study objects, such as sex or observation year. Parameters are those that are measured, such as body weight or pollutant concentration.156,356,-1148,1161,0,0,1,0,1,0,,0,2,693,146,476,224# of observed parameters0156,404,1140,121,0,0,1,0,0,0,72,0,152425,39321,65535N_parametersBased on your choice, fill in the data about either a data table or an Analytica model.304,216,-1296,16Finally, fill in the name (a description that may be longer than an identifier) and the unit of measurement. Then press the button Upload data.304,492,-1296,20Study Ident0156,357,1140,131,0,0,1,0,0,0,158,0,152425,39321,65535Study_identUpload a modelktluser1. Aprta 2009 9:38 48,24456,384,148,241,1,0,1,1,1,0,,0,1,883,39,373,370,17Additional partsThis module contains nodes that have been developed for a particular detail, like managing the Sett and Item tables. However, these tasks are not very important for the basic functionalitites, so we leave the development of them later. You must come back to these when there is more time.ktluser22. Marta 2009 22:43 48,24296,384,148,241,1,0,1,1,1,0,,0,1,679,19,567,227,17IndicesThis makes a list of all indices (including decision nodes) that are used by the variables in Object1.0{index a:= indexnames(evaluate(Objects_excl_indices));
a:= if a='Object1' or a='Objects_excl_indices' then 0 else 1;
subset(a)}232,168,148,132,102,90,476,4642,32,349,416,303,0,MIDM[Objects_excl_indices]['Age','Country','Year','Sex']W SettMakes a list of sets for the Sett table. There are three major kinds of sets: Indices belonging to an assessment, variables belonging to an assessment, and variables belonging to a run. Indices belonging to a dimension are NOT created with this node.index i:= ['Assessment','Assessment','Run'];
index j:= ['id','Obj_id','Sty_id'];
array(j,[
(Cardinals[table1='Sett']+@i)&'',
findid(Objects1[Object_all=i, .j='Ident'], Obj, 'Ident'),
array(i,[3,4,9])])200,24,148,162,740,132,495,4442,661,16,416,340,0,MIDM65535,45873,39321[Sys_localindex('J'),Sys_localindex('I')]2,D,4,2,0,0,4,0,$,0,"ABBREV",0[Index Table1, Variable Cardinals, Function Findid, Variable Obj]100,1,1,1,1,9,2970,2100,15,0[]W ItemMakes a list of items of sets into the Item table. This node does NOT handle indices of a dimensions, but they must be described elsewhere. For types of sets, see Write_sett.index j:= ['id','Sett_id','Obj_id','Fail'];
index k:= types(1);
index L:= types(6);
var c:= if sett.j='Obj_id' then sett&'+'&sett[.j='Sty_id'] else sett;
c:= findid(W_sett[.j='Obj_id']&'+'&W_sett[.j='Sty_id'], c, 'Obj_id');
var a:= array(j,k, [0, slice(c,1), k, 0]);
var b:= array(j,L,[0, slice(c,2), L, 0]);
index m:= 1..(size(k)+size(L));
a:= concat(a,b,k,l,m);
b:= array(j,k, [0, slice(c,3), k, 0]);
index i:= 1..(size(m)+size(k));
a:= concat(a,b,m,k,i);
if j='id' then cardinals[table1='Item']+@i else a;
200,72,148,162,80,84,476,4732,921,13,345,638,0,MIDM65535,45873,39321[Sys_localindex('J'),Sys_localindex('I')]2,D,4,2,0,0,4,0,$,0,"ABBREV",0100,1,1,1,1,9,2970,2100,15,0[][Self,1,Sys_localindex('J'),1,Sys_localindex('K'),1]Is the data probabilistic0152,120,1140,131,0,0,1,0,0,0,72,0,152425,39321,65535Is_the_data_probabilObjects excl indices['Morbidity__diseases_']368,136,148,242,958,152,321,4812,328,338,416,361,0,MIDM52425,39321,65535['Morbidity__diseases_']Is the data probabilisticChoice(Self,1,False)96,48,148,24[Formnode Is_the_data_probabi1]52425,39321,65535['No','Yes']Object allList of variables, indices, assessment, and run to be stored into the Opasnet Base. Assessment is not included in the simpler version.concat(['Object'],Indices1)&''312,80,148,131,1,1,1,1,1,0,0,0,02,49,109,558,5272,200,210,688,358,0,MIDM[Self]['Object','Age','Country','Year','Sex','Morbidity']Indicesobservations[@observation=1, @field1=1..size(field1)]304,48,148,12[0,1,1,0]['Age','Country','Year','Sex','Morbidity']Te11Upload an Analytica model. First, switch the choice "Data table or model?" to Analytica model. Note! You can insert several variables at the same time. Each variable or study MUST have at least one index. Analytica identifiers are used to find the right nodes. Ident is the identifier of the description page from the Opasnet wiki. If Probabilistic? is 1, a sample of the distribution is stored; if it is 0, the mean or the point estimate is stored.168,190,-5152,1371,0,0,1,0,1,0,,0,Dependency graphktluser29. Decta 2008 21:51 48,2464,456,148,241,13,30,902,527,1792,1,1,0,2,9,2970,2100,15,0Cardinals:
all tables0192,56,148,2439325,65535,39321Objects:
identifier
id
ident
Name
Unit
Typ_id
etcCardinals__all_table320,120,148,76Obj:
id
Ident
Name
Unit
Typ_id
etcObjects__192,184,148,6765535,45873,39321Sett:
id
Obj_id
Typ_idObj__id_ident_name_u192,328,148,4065535,45873,39321Item:
id
Sett_id
Obj_id
FailSett__id_obj_id_typ_56,329,148,4965535,45873,39321Inf:
id
Begin
End
Who
UrlObj__id_ident_name_u56,186,148,5865535,45873,39321Loc:
id
Obj_id_d
Location
DescriptionObj__id_ident_name_u320,336,148,5265535,45873,39321Inp_locres:
Locres_id
Location
Res_id
Roww_id
Vident
Obj_id_v
Obj_id_r
Mean
NLoc__id_obj_id_d_loc456,336,148,92Locres:
id
Res_id
Roww_idInp_locres__locres_i592,424,148,4065535,45873,39321Res:
id
Obj_id_v
Obj_id_r
Mean
NInp_locres__locres_i592,312,148,5865535,45873,39321Sam:
id
Res_id
Sample
ResultSample__id_res_id_sa592,120,148,5265535,45873,39321Descr:
id
DescrSample__id_res_id_sa592,216,148,3165535,45873,39321The arrows only show sequential dependencies. This means that e.g. Cardinals is a parent to many other nodes as well, but the critical values in Cardinals only change before Objects is defined, and there is no need to update Cardinals during the writing process.
Orange nodes are actual Tables in Opasnet Base. Green nodes are SQL queries from Opasnet Base. Blue nodes are computed in Analytica.752,168,-1112,152Sample:
id
Res_id
Sample
Result
DescrObjects__456,121,148,58R Objects192,176,-156,801,0,0,1,0,1,0,,0,R Structure256,332,-1120,681,0,0,1,0,1,0,,0,R Cardinals192,48,-156,401,0,0,1,0,1,0,,0,Writerjtue24. maata 2009 9:36 48,24184,384,148,241,566,38,555,442,17W LocMakes a table to be written to the Loc table.index j:= ['id','Obj_id_i','Location','Roww','Description'];
var a:= Locations[.j=j];
var b:= a[j='Obj_id_i'];
array(j,[textify(cardinals[table1='Loc']+a), findid(b,Obj,'Ident'), a, textify(a), a])464,296,148,122,156,83,476,2452,642,68,515,278,0,MIDM65535,45873,39321[Sys_localindex('J'),Sys_localindex('I')]2,D,4,2,0,0,4,0,$,0,"ABBREV",0100,1,1,1,1,9,2970,2100,15,0[][Sys_localindex('I'),16,Sys_localindex('I'),1,Sys_localindex('J'),1]W LoccellSlices fields that are needed in the Locres table from Inp_locres.index j:= ['id','Cell_id','Loc_id'];
var a:= Loccells;
var b:= textify(findid(a[.j='Loc_id'], Obj, 'Ident'));
var c:= textify(a[.j='Location']);
b:= findid(b&'+'&c, (if Loc.j='Obj_id_i' then Loc&'+'&Loc[.j='Location'] else Loc), 'Obj_id_i');
a:= a[.j=j];
a:= array(j,[(a+cardinals[table1='Loccell']),(a+cardinals[table1='Cell']), b]);
textify(a)464,192,148,162,791,179,476,3872,632,155,618,303,0,MIDM65535,45873,39321[Sys_localindex('J'),Sys_localindex('I')]2,I,4,2,0,0,4,0,$,0,"ABBREV",0[]W CellSlices the fields that are needed in the Res table. Removes duplicate rows.
WHY IS THIS CODE SO SLOW? There is no apparent reason to that. Is the findid function so time-consuming?index j:= ['id','Obj_id_v','Obj_id_r','Mean','N'];
var a:= Loccells;
var b:= a[.j='Cell_id']+cardinals[table1='Cell'];
a:= a[.j=j];
var c:= findid(a[@j=2], Obj, 'Ident');
var d:= w_obj[.j='Ident'];
d:= findid(d, Obj, 'Ident')[@.i=size(w_obj.i)];
a:= array(j, [textify(b),c, d, a, textify(a)]);
index i:= unique(a,a.i);
a:= a[.i=i];
if a=null then '' else a464,160,148,162,792,52,476,3792,85,231,505,368,0,MIDM65535,45873,39321[Sys_localindex('J'),Sys_localindex('I')]2,I,4,2,0,0,4,0,$,0,"ABBREV",0[]WikisNames of different wikis used.Table(Self)(
'Op_en','Op_fi','Heande','En','Fi','Erac','Beneris','Intarese','Piltti','Kantiva','Bioher','Heimtsa')[1,2,3,4,5,8,9,10,11,13,14,15]344,24,148,1665535,52427,65534[Self]Object typesTypes of different objects that may exist in Analytica or Opasnet Base. Types that have the same number are treated equally in these systems.Table(Self)(
'Variable','Dimension','Method','Model','Class','Index','Nugget','Encyclopedia article','Run','Chance','Decision','Objective','Constant','Determ','Module','Library','Form')[1,2,3,4,5,6,7,8,9,1,10,1,1,1,4,4,4]64,328,148,202,56,132,476,2242,674,34,416,606,0,MIDM2,636,151,416,390,0,MIDM65535,52427,65534W ObjSelects relevant information for the Obj table from Objects1 node.index j:= ['id','Ident','Name','Unit','Objtype_id','Page','Wiki_id'];
var a:= Objects;
var b:= if a[.j='Ident'] = 0 then -1 else a[.j='Ident'];
b:= findid(b, Obj, 'Ident');
b:= if b='0' then cardinals[table1='Obj']+a[.j='id'] else b;
a:= if a.j='id' then textify(b) else a;
a:= a[.j=j];
a:= if j='Ident' and a[j='Ident']='' then a[j='id'] else a;
a&''464,104,148,162,510,359,476,2872,14,54,1023,259,0,MIDM65535,45873,39321[Sys_localindex('J'),Sys_localindex('I')]2,I,4,2,0,0,4,0,$,0,"ABBREV",0[][]W ResinfoIf the result is not a number, then the actual result text can be written into the Description field of the Descr table. Makes a list of text values to be written into the Descr table.index j:= ['id','Restext'];
var a:= Results;
index i:= subset(a[.j='Restext']);
a:= a[.j=j, .i=i];
a:= array(j, [textify(a+Cardinals[table1='Res']), a])464,232,148,162,674,46,476,2592,670,328,416,303,0,MIDM65535,45873,39321[Sys_localindex('J'),Sys_localindex('I')]2,I,4,2,0,0,4,0,$,0,"ABBREV",0[]W ObjinfoMakes a list of objects that contains some additional information to be written into the Inf table.index j:= ['Obj_id','Acttype_id','Who','Comments'];
var a:= Objects;
var b:= if a[.j='Ident'] = 0 then -1 else a[.j='Ident'];
b:= findid(b, Obj, 'Ident');
b:= if b='0' then cardinals[table1='Obj']+a[.j='id'] else b;
a:= if j='Obj_id' then b else a[.j=j];
a:= if a = null or a='' then 0 else a;
index i:= subset(if sum(a, j) = 0 then 0 else 1);
a:= a[.i=i];
a:= if a=null or a=0 then '' else a&''464,72,148,162,773,40,476,3402,34,427,690,274,0,MIDM65535,45873,39321[Sys_localindex('J'),Sys_localindex('I')]2,I,4,2,0,0,4,0,$,0,"ABBREV",0[][Sys_localindex('J'),1,Sys_localindex('I'),1,Sys_localindex('J'),1]W Resindex j:= ['id','Cell_id','Obs','Result'];
var a:= Results;
index i:= subset(if a[.j='Result']=null and a[.j='Description']=0 then 0 else 1);
a:= a[.j=j, .i=i];
a:= array(j, [textify(a+Cardinals[table1='Res']), textify(a+ Cardinals[table1='Cell']), textify(a),a]);
if a=null then '' else a
464,264,148,132,539,477,582,2972,62,129,609,303,0,MIDM65535,45873,39321[Sys_localindex('J'),Sys_localindex('I')]2,D,4,2,0,0,4,0,$,0,"ABBREV",0[][]Number of variablesAdditional information for each index and decision node. Description node is the name of a node containing information about the locations of the index. It must be indexed by the index.1192,328,148,222,140,217,476,2242,605,351,664,303,0,MIDM2,506,220,684,303,0,MIDM[Formnode Number_of_variables1]52425,39321,65535[Indices][Indices][1,1,0,1]Variablevar a:= if data_table_or_model_ = 'Data table' then 1 else N_variables;
'Var'&1..a192,264,148,131,1,1,1,1,1,0,,0,2,107,331,416,303,0,MIDM['Var1']VariablesAdditional information for each index and decision node. Description node is the name of a node containing information about the locations of the index. It must be indexed by the index.Table(Variable1,Varinfo)(
'Va6','Op_en2995',1
)192,232,148,222,140,217,476,2242,532,422,664,221,0,MIDM2,506,220,684,303,0,MIDM[Formnode Variables1]52425,39321,65535[Varinfo,Variable1][Varinfo,Variable1][1,1,1,0]Fieldvar a:= 'index'&1..N_indices;
{a:= if Is_the_data_probabil='Yes' then concat(a,['Iteration']) else a;}
var b:= 'parameter'&1..N_parameters;
concat(a, b)64,184,148,122,683,44,416,303,0,MIDM[1,1,1,0]['index1','index2','index3','index4','parameter1']Observationconcat(['Identifier'],'Obs'&1..N_observations)64,208,148,122,557,134,416,303,0,MIDM['Identifier','Obs1','Obs2','Obs3']Number of indices464,64,148,24[Formnode Number_of_indices1]52425,39321,65535[1,1,0,1]Number of observations364,112,152,24[Formnode Number_of_observati1]52425,39321,65535ObservationsTable(Field1,Observation)(
'Age','All','All','All',
'Country','Austria','Austria','Austria',
'Year',1970,1970,1970,
'Sex','Male','Female','All',
'Morbidity',142.77,72.41,98.95999999999999
)64,160,152,162,746,25,497,657,0,MIDM2,644,62,513,572,0,MIDM[Formnode Observations1]52425,39321,65535[Field1,Observation][Field1,Observation]Object infoTable(Object_info,Object1)(
'Total amount of car kilometres driven in the Helsinki metropolitan area','Different times of day',
'km/d','h'
)192,72,148,132,102,90,476,3492,498,89,531,290,0,MIDM2,184,194,660,316,0,MIDM[Formnode Study_or_variable_i1]52425,39321,65535[Object_info,Object1][Object_info,Object1]Object info['Name','Unit']192,96,148,12['Name','Unit']LoccellsMakes a list of all locations in all results in all variables. The list is as long as is needed for the Loccell table. A subset is taken then for the Cell table.
1) Initialises local variables, and slices variables from Object1.
2)-4) Does the process for each variable one at a time. This happens in function Loccell.
5) Makes i the row index.
, @observation=@cell_id+1var output:= 0;
output:= if data_table_or_model_ = 'Data table' then
Doloccell(Data_table)
else (
var e:= 0;
var f:= 0;
var x:= 1;
while x<= size(variable1) do (
var a:= mean(evaluate(variables[@variable1=x, varinfo='Analytica identifier']));
index j:= concat(indexnames(a),['Result']);
index i:= 1..size(a);
a:= mdarraytotable(a, i, j);
a:= Doloccell(a, x, e, f);
e:= e+size(a.i);
f:= f+size(i);
output:= if x=1 then a else for y:= output.j do ( concat(output[.j=y], a[.j=y]) );
x:= x+1) ;
output);
index i:= 1..size(output)/size(output.j);
for y:= output.j do (slice(output[.j=y],i))344,160,148,162,728,99,526,5582,15,39,656,488,0,MIDM[Sys_localindex('J'),Sys_localindex('I')]2,I,4,2,0,0,4,0,$,0,"ABBREV",0[][Undefined][Sys_localindex('CONV2'),1,Parameter,1,Sys_localindex('L'),1]ResultsThe usage of local variables: a: the temporary variable that is being edited. e: cardinal of the Cell table. f: cardinal of the Res table. j: output column headings. i: output row numbers. NOTE! ONLY THE DETERMINISTIC VERSION WORKS AT THE MOMENT.
1) Only one piece of information (Observations) is included.
2)-5) The process is done for each variable one at a time (this is indexed by x).
3) Several within-loop local variables are initiated.
4) The variable is given index runn which is equal to run if probabilistic and [0] if not. The array is flattened first to 2-D, the value only is kept.
5) Variables are concatenated to each other.
6) Index i is made the index of the implicit index.
NOTE! This node MUST be formatted to Integer, otherwise Res_id will be stored in a wrong format.var output:= 0;
output:= if data_table_or_model_ = 'Data table' then
Doresult(Data_table, 0)
else (
var e:= 0;
var f:= 0;
var x:= 1;
while x<= size(variable1) do (
var a:= sample(evaluate(variables[@variable1=x, varinfo='Analytica identifier']));
index j:= concat(indexnames(max(a,run)),['Result']);
index i:= 1..size(max(a,run));
a:= mdarraytotable(a, i, j);
a:= Doresult(a, variables[@variable1=x, varinfo='Probabilistic?'], e, f);
e:= max(a[.j='Cell_id'],a.i);
f:= max(a[.j='id'],a.i);
output:= if x=1 then a else for y:= output.j do ( concat(output[.j=y], a[.j=y]) );
x:= x+1) ;
output);
index i:= 1..size(output)/size(output.j);
output:= for y:= output.j do (slice(output[.j=y],i))344,232,148,162,50,25,585,6152,583,137,469,411,0,MIDM[Sys_localindex('J'),Sys_localindex('I')]2,I,4,2,0,0,4,0,$,0,"ABBREV",0[Run,2,Sys_localindex('J'),1,Sys_localindex('I'),1]LocationsThe format of this node MUST be integer, so that the id and Roww values are stored correctly.var output:= 0;
output:= if data_table_or_model_ = 'Data table' then
Dolocation(Data_table)
else (
var x:= 1;
while x<= size(variable1) do (
var a:= mean(evaluate(variables[@variable1=x, varinfo='Analytica identifier']));
index j:= concat(indexnames(a),['Result']);
index i:= 1..size(a);
a:= mdarraytotable(a, i, j);
a:= Dolocation(a);
output:= if x=1 then a else for y:= output.j do ( concat(output[.j=y], a[.j=y]) );
x:= x+1) ;
output);
index i:= 1..size(output)/size(output.j);
output:= for y:= output.j do (slice(output[.j=y],i));
if output.j='id' then i else output344,296,148,162,650,38,476,5812,745,15,483,348,0,MIDM[Sys_localindex('J'),Sys_localindex('I')]2,I,4,2,0,0,4,0,$,0,"ABBREV",0[0][Sys_localindex('D'),1,Object_all3,1,Age,1]# of observed parameters164,264,148,31[Formnode A__of_observed_para1]52425,39321,65535ParameterObservations[@Observation=1, @Field1=N_indices+(1..N_parameters)]192,184,148,122,746,363,416,303,0,MIDM['Fish','Samplesize','Minsize','Maxsize','137Cs_Bq/kgtpVammala','137Cs_Bq/kgtpSTUK']Data tableindex h:= observations[@observation=1, @field1=1..N_indices];
index j:= concat(h,['Parameter','Result']);
index i:= 1..(size(observation)-1)*size(parameter);
index loccell_id:= 1..(size(i)*size(h));
var conv:= if j='Result' then @parameter+N_indices else @j;
index conv2:= 1..N_observations;
var a:= observations[@field1=conv, @observation=conv2+1];
a:= if j='Parameter' then parameter else a;
a:= concatrows(a, parameter, conv2, i);192,160,148,132,102,90,482,3262,654,192,611,318,0,MIDM[Sys_localindex('I'),Sys_localindex('J')][Parameter,6,Sys_localindex('CONV2'),1,Sys_localindex('L'),1]Varinfo['Analytica identifier','Ident','Probabilistic?']192,288,148,122,90,166,416,303,0,MIDM['Analytica identifier','Ident','Probabilistic?']ObjectsIndex j:= ['id','Ident','Name','Unit','Objtype_id','Acttype_id','Page','Wiki_id', 'Who','Comments','Probabilistic?','Description node'];
index i:= concat(Object1,['Run']);
var a:= if j='Ident' and i = 'Var1' then study_ident else null;
a:= if data_table_or_model_ = 'Data table' then a else Variables[Variable1=i, Varinfo=j];
a:= if a=null then Object_info1[Object1=i, Object_info=j] else a;
var b:= findintext(wikis,a[j='Ident']);
var c:= sum(if b=0 then 0 else @wikis,wikis);
b:= sum(if b=0 then 0 else b+textlength(Wikis),wikis);
b:= if b = 0 then 2664 else selecttext(a[j='Ident'],b);
a:= array(j,[
@i,
if @i>size(variable1) and @i<size(i) then i else a,
if i ='Run' then 'Analytica '&Analyticaedition&', ('&Analyticaplatform&'), Version: '&Analyticaversion&', Samplesize: '&samplesize else a,
a,
if @i<=size(variable1) then 1 else if @i=size(i) then 9 else 6,
if @i<=size(variable1) then 11 else 1,
if i='Run' then '2817' else b&'',
if c=0 then 1&'' else c&'',
opasnet_username, '', a, '']);
a:= if a = null then '' else a
344,72,148,162,669,75,479,4732,106,165,1087,333,0,MIDM[Sys_localindex('I'),Sys_localindex('J')]Objectvar b:= if data_table_or_model_ = 'Data table' then "['Var1']" else "variable1";
var a:= indexnames(evaluate(Variables[Varinfo='Analytica identifier']));
a:= if a='Variable1' then 0 else 1;
a:= subset(a);
a:= if data_table_or_model_ = 'Data table' then Observations[@Observation=1, @Field1=1..n_indices] else a;
concat(evaluate(b),a)192,120,148,132,718,27,416,303,0,MIDM['Var1','Period1']Study Ident'Op_en2693'192,32,148,16[Formnode Study_ident1]52425,39321,65535[1,1,0,1]Data table or model?Choice(Self,2,False)64,384,148,24[Formnode Data_table_or_model1]['Data table','Analytica model'][1,1,0,0]Data table or model?0176,24,1160,131,0,0,1,0,0,0,142,0,1Data_table_or_model_Number of variables0164,281,1140,121,0,0,1,0,0,0,72,0,152425,39321,65535N_variablesVariables0164,305,1140,121,0,0,1,0,0,0,72,0,152425,39321,65535Variables1..264,520,148,24[Sys_localindex('I'),Sys_localindex('J')]Readerktluser3. Augta 2008 18:31jtue9. lokta 2008 14:01 48,24296,456,148,241,1,1,1,1,1,0,0,0,01,785,211,477,360,17Arial, 15(vident:text, runident:optional)Read meanReads the mean data about the vident variable from the Opasnet Base. Uses the runident run if specified; otherwise uses the newest run of that variable.
PARAMETERS:
* Vident: the Ident of the variable in the Opasnet Base.
* Runident: the Ident of the run from which the results will be brought. If omitted, the newest result will be brought.if isnotspecified(runident) then runident:= identfind(newestrun(vident));
var a:= '
SELECT Var.Ident as Vident, Var.Name as Vname, Var.Unit as Vunit, Cell.id, Ind.Ident as Iident, Location, Mean, N, Run.Name as Rname, Run.Ident AS Runident
FROM Obj as Var, Cell, Loccell, Loc, Obj as Ind, Obj as Run
WHERE Cell.Obj_id_r = Run.id
AND Cell.Obj_id_v = Var.id
AND Loccell.Cell_id = Cell.id
AND Loccell.Loc_id = Loc.id
AND Loc.Obj_id_i = Ind.id
AND Var.Ident = '&chr(39)&vident&chr(39)&'
AND Run.ident = '&chr(39)&runident&chr(39)
;
index i:= DBquery(Odbc,a);
index j:= dblabels(i);
dbtable(i,j)56,88,148,122,585,25,516,58939325,65535,39321vident,runident(vident:text)NewestrunThis function checks for the newest result (according to run_id) of the variable. The function is used if the user does not define the run_id as an optional parameter in functions Read_mean and Read_sample.
PARAMETERS:
* Vident: the Ident of the variable in the Opasnet Base.index i:= DBquery(Odbc,'
SELECT Obj_id_r
FROM Cell, Obj as Var
WHERE Var.id = Cell.Obj_id_v
AND Var.Ident = "'&vident&'"
GROUP BY Var.id, Obj_id_r
');
index j:= dblabels(i);
max(max(dbtable(i,j),i),j)56,16,148,122,678,59,476,56639325,65535,39321vident(vident:text, runident:optional)Read sampleReads the sample data about the vident variable from the Opasnet Base. Uses the runident run if specified; otherwise uses the newest run of that variable.
PARAMETERS:
* Vident: the name of the variable in the Opasnet Base.
* Runident: the Ident of the run from which the results will be brought. If omitted, the newest result will be brought.if isnotspecified(runident) then runident:= identfind(newestrun(vident));
var a:= '
SELECT Temp.id, Obs, Result, Restext
FROM
(SELECT Cell.id, Res.id AS Res_id, Obs, Result, Obj_id_r
FROM Cell, Res, Obj AS Run, Obj AS Var
WHERE Var.Ident = '&chr(39)&vident&chr(39)&'
AND Cell.Obj_id_v = Var.id
AND Cell.Obj_id_r = Run.id
AND Run.Ident = '&chr(39)&Runident&chr(39)&'
AND Res.Cell_id = Cell.id) AS Temp
LEFT JOIN Resinfo ON
Temp.Res_id = Resinfo.id
';
index i:= DBquery(Odbc,a);
index j:= dblabels(i);
dbtable(i,j)56,120,148,222,700,47,516,61239325,65535,39321vident,runidentEnter variable Ident'Op_en1912'168,83,148,27[Formnode Enter_variable1]52425,39321,65535Enter variable0288,24,1176,131,0,0,1,0,0,0,170,0,152425,39321,65535Enter_variableNewest runnewestrun(Enter_variable)288,60,148,12Var inforead_mean(Enter_variable)288,108,148,122,56,66,1205,308,0,MIDM[Sys_localindex('J'),Sys_localindex('I')](a,inde)MakeindThe input table a must have a structure that is also used as input for MDTable function. The function removes one column with location information and makes a dimension (index) with the locations in the column. Inde is the (local) index that will be added. Note that unlike MDTable function, this can use local indices in the output.if size(a.m)= 1 then a else (
a:= if inde = a[@.m=1] then a else 0;
index m:= slice(a.m,(2..size(a.m)));
a:= a[.m=m])56,176,148,122,283,62,476,224a,inde(a)Get res.idMakes a multi-dimensional array with the same structure as the original variable that was stored into the Opasnet Base. However, the indices do not have original names. They are named In1, In2,... The contents of the array are the res.ids of the variable. The input parameter must be a 2D table with the structure that comes from the Read_mean function.
1) Slices the necessary columns from the input table and converts that to a 2D table that has the same structure as is used for input to the function MDTable.
2) Defines the local indices (up to 10), and changes a location column to a dimension one at a time until all columns have been changed.index k:= ['Iident','Location','id'];
a:= a[.j=k];
index L:= a[@k=1]&'+'&textify(a[@k=3]);
index m:= concat(a[.i=unique(a[@k=1],a.i), @k=1],['Result']);
index n:= a[.i=unique(a[@k=3],a.i), @k=3];
a:= a[@.i=@L];
a:= a[L=(m)&'+'&textify(n), @k=2];
a:= if m='Result' then n else a;
index in1:= a[n=unique(a[@m=1],n),@m=1];
index in2:= a[n=unique(a[@m=2],n),@m=2];
index in3:= a[n=unique(a[@m=3],n),@m=3];
index In4:= a[n=unique(a[@m=4],n),@m=4];
index In5:= a[n=unique(a[@m=5],n),@m=5];
index in6:= a[n=unique(a[@m=6],n),@m=6];
index in7:= a[n=unique(a[@m=7],n),@m=7];
index in8:= a[n=unique(a[@m=8],n),@m=8];
index in9:= a[n=unique(a[@m=9],n),@m=9];
index in10:= a[n=unique(a[@m=10],n),@m=10];
a:= makeind(a, in1);
a:= makeind(a, in2);
a:= makeind(a, in3);
a:= makeind(a, in4);
a:= makeind(a, in5);
a:= makeind(a, in6);
a:= makeind(a, in7);
a:= makeind(a, in8);
a:= makeind(a, in9);
a:= makeind(a, in10);
sum(sum(a,a.m),a.n)56,152,148,122,669,44,476,545aVar meanget_mean(Enter_variable)288,132,148,122,547,35,416,622,0,MIDM[Sys_localindex('IN2'),Sys_localindex('IN3')][Sys_localindex('IN1'),1,Sys_localindex('IN4'),1,Sys_localindex('IN5'),1,Sys_localindex('IN3'),1,Sys_localindex('IN2'),1](vident:text, runident:optional)Get meanGives the mean result of a (multidimensional) variable stored in the Opasnet Base. The procedure is simple because it utilises the variable structure (with res_ids) derived by the get_res_id function.var a:= read_mean(vident, runident);
index o:= a[.j='id'];
var output:= a[@.i=@o, .j='Mean'];
a:= get_res_id(a);
output[o=a]56,200,148,122,665,82,476,428vident,runident(vident:text, runident:optional)Get sampleGives the sample result of a (multidimensional) variable stored in the Opasnet Base. The procedure is simple because it utilises the variable structure (with res_ids) derived by the get_res_id function.
Note that if the Analytica samplesize is smaller than the samplesize stored in the Opasnet Base, the extra samples will be discarded. If the samplesize is larger, the remaining rows will be null.
1) Brings the data into the right structure.
2) Chooses whether the actual result is numerical (in the Result column) or text (in the Description column).var a:= read_sample(vident, runident);
var b:= textify(get_res_id(read_mean(vident,runident)));
index k:= textify(a[.j='id'])&'+'&textify(a[.j='Obs']);
index runn:= textify(min(a[.j='Obs'])..max(a[.j='Obs']));
a:= a[@.i=@k];
a:= a[k=b&'+'&runn];
a:= if max(runn)=0 then a[@runn=1] else a[@runn=@run];
var c:= if a[.j='Restext']='' then 0 else 1;
c:= sum(sum(sum(sum(sum(sum(sum(sum(sum(sum(c))))))))));
if c=0 then a[.j='Result'] else a[.j='Restext']56,224,148,122,641,28,476,556vident,runidentVar sampleget_sample(Enter_variable)288,156,148,122,226,324,416,303,0,MEAN[Sys_localindex('IN5'),Sys_localindex('IN3')][Sys_localindex('IN1'),1,Sys_localindex('IN2'),1,Sys_localindex('IN4'),1,Sys_localindex('IN3'),1,Sys_localindex('J'),1,Sys_localindex('IN5'),1](runid)IdentfindFinds the Ident for the run (or another object) that has the id runid.index i:= DBquery(Odbc,'
SELECT Ident
FROM Obj
WHERE Obj.id = "'&runid&'"
');
index j:= dblabels(i);
var a:= dbtable(i,j);
a[@i=1, @j=1]56,64,148,122,732,65,516,58939325,65535,39321runidVar run infoDescribes the runs of the defined variable. This should be made a function.var_run_info(Enter_variable)288,84,148,122,136,146,1111,285,0,MIDM[Sys_localindex('J'),Sys_localindex('I')](vident:text)Var run infoThis function checks for the newest result (according to run_id) of the variable. The function is used if the user does not define the run_id as an optional parameter in functions Read_mean and Read_sample.
PARAMETERS:
* Vident: the Ident of the variable in the Opasnet Base.var a:= '
SELECT Var.Ident, Var.Name, Var.Unit, Run.Ident AS Runident, Act.When, Act.Who, Run.Name as Method
FROM Obj as Var, Obj as Run, Cell, Objinfo AS Act
WHERE Var.Ident = '&chr(39)&vident&chr(39)&'
AND Var.id = Cell.Obj_id_v
AND Run.id = Cell.Obj_id_r
AND Run.id = Act.id
GROUP BY Var.id, Run.id
';
index i:= DBquery(Odbc,a);
index j:= dblabels(i);
dbtable(i,j)56,40,148,132,678,59,476,56639325,65535,39321videntUse these functions to retireve data from the Opasnet base:
* Newest_run: finds the newest run of the object.
* Var_run_info: Finds the run information of the object.
* Read_mean: Reads the means of each cell.
* Get_mean
* Get_sample: Reads the whole sample.
Note! These should be updated when we get experience about what we actually want out.280,285,-1168,101(a)TextifyChanges an integer of any length to a text value. This bypasses the number formatting problem that tends to convert e.g. 93341 to '93.34K'. If the number is not integer, up to three digits after the decimal point will be taken as well.for y[]:= a do (var x:= 1;
var b:= '';
var c:= floor(logten(y))+1;
while x<= c do (
b:= (y-floor(y/10)*10)&b;
y:= floor(y/10);
x:= x+1);
b)56,248,148,122,102,90,476,371aget_sample('Op_en2995')392,120,148,242,136,146,416,303,0,MEAN[Run,Sys_localindex('IN1')]Detailsktluser8. Decta 2008 3:01 48,24184,456,148,241,30,288,495,456,17(a; x:optional = 1; e, f:optional=0)Doloccell2) Only the deterministic information about variables are considered (therefore mean). Makes a 2D table of the locres info.
3) Makes a table with fields required by the Loccell and Cell tables.
4) Reduces one dimension by expanding the length from the length of Cell to that of Loccell.index j:= ['id', 'Location', 'Cell_id', 'Loc_id', 'Obj_id_v', 'Obj_id_r', 'Mean', 'N'];
index h:= a.j[@.j=1..size(a.j)-1];
{index L:= copyindex(a.j);
var b:= if data_table_or_model_ = 'Data table' then size(parameter) else 1;
index cell_id:= 1..size(a.i)*b;}
index i:= 1..size(a.i)*size(h);
{a:= a[.j=L, .i=cell_id];}
var c:= Objects[@.i=x];
a:= array(j,[
i[@i=@a.i+size(a.i)*(@h-1)]+e,
a[.j=h]&'',
a.i+f,
h,
c[.j='Ident'],
'',
a[.j='Result'],
if c[.j='Probabilistic?']=0 then 0 else samplesize]);
concatrows(a,h,a.i,i)400,288,148,132,711,184,556,625a,x,e,f(a: prob; probabilistic; e, f: optional=0)Doresultindex runn:= if Probabilistic=1 then copyindex(run) else [0];
index i:= (1..size(max(a.i,run))*size(runn))+f;
a:= if Probabilistic=1 then a[run=runn] else (if runn=0 then mean(a) else mean(a));
a:= a[.j='Result'];
index j:= ['id','Cell_id','Obs','Result','Restext'];
a:= array(j,[0, a.i+e, runn, (if istext(a) then 0 else a) , (if istext(a) then a else 0)]);
a:= concatrows(a,a.i,runn, i);
a:= if j='id' then i else a400,264,148,132,242,24,476,526a,probabilistic,e,f(a)Dolocationvar b:= [0];
var c:= [0];
var e:= [0];
var f:= [0];
var x:= 1;
while x<= size(a.j)-1 do (
var h:= a[@.j=x];
var d:= h[.i=unique(h,h.i)];
b:= concat(b,d);
c:= concat(c,(if d=0 then slice(a.j,x) else slice(a.j,x)));
e:= concat(e,1..size(d));
x:= x+1);
index i:= 1..size(b)-1;
index j:= ['id','Obj_id_i', 'Location', 'Roww', 'Description'];
array(j,[i, slice(c,i+1), slice(b,i+1)&'', slice(e,i+1), '']);400,240,148,122,671,164,503,486aConcatenation UDFsThis library contains functions to make various instances of concatenation more convenient. Concat3 thru Concat10 are generalizations of the built-in Concat function which concatenate from 3 to 10 arrays in a single call (while the built-in Concat concatenates two arrays). ConcatRows concatenates all the rows of a single array.David Kendall & Lonnie ChrismanMon, Jan 26, 2004 8:49 AMLonnieWed, Sep 05, 2007 3:23 PM48,24184,328,168,201,0,0,1,1,1,0,0,0,01,50,200,488,454,23(A1, A2, A3: ArrayType; I1, I2, I3, J: IndexType )Concat3Concatenates three arrays, A1, A2, and A3. I1, I2, and I3 are the indexes that are joined; J is the index of the new array; J usually is the concatenation of I1, I2, and I3Index I12 := Concat(I1,I2);
Concat( Concat( A1,A2,I1,I2,I12 ), A3, I12, I3, J )88,64,148,262,56,56,986,596A1,A2,A3,I1,I2,I3,J(A1, A2, A3, A4: ArrayType; I1, I2, I3, I4, J: IndexType )Concat4Concatenates four arrays, A1, A2, A3, and A4. I1, I2, I3, and I4 are the indexes that are joined; J is the index of the new array; J usually is the concatenation of I1, I2, I3, and I4.Index I12 := Concat(I1,I2);
Index I123:= Concat(I12, I3);
Concat(
Concat(
Concat( A1,A2,I1,I2,I12 ),
A3, I12, I3, I123),
A4, I123, I4, J);
192,64,148,242,30,30,986,596A1,A2,A3,A4,I1,I2,I3,I4,J0(A1, A2, A3, A4, A5, A6, A7, A8, A9: ArrayType; I1, I2, I3, I4, I5, I6, I7, I8, I9, J: IndexType)Concat9Concatenates nine arrays, A1, ..., A9. I1, ..., I9 are the indexes joined; J is the index of the new array; J usually is the concatenation of I1, ..., I9.Index I12 := Concat(I1,I2);
Index I123 := Concat(I12, I3);
Index I1234 := Concat(I123, I4);
Index I12345 := Concat(I1234, I5);
Index I123456 := Concat(I12345, I6);
Index I1234567 := Concat(I123456, I7);
Index I12345678 := Concat(I1234567, I8);
Concat(
Concat(
Concat(
Concat(
Concat(
Concat(
Concat(
Concat( A1,A2,I1,I2,I12 ),
A3, I12, I3, I123),
A4, I123, I4, I1234),
A5, I1234, I5, I12345),
A6, I12345, I6, I123456),
A7, I123456, I7, I1234567),
A8, I1234567, I8, I12345678),
A9, I12345678, I9, J);88,232,148,242,27,120,469,638A1,A2,A3,A4,A5,A6,A7,A8,A9,I1,I2,I3,I4,I5,I6,I7,I8,I9,J0(A1, A2, A3, A4, A5: ArrayType; I1, I2, I3, I4, I5, J: IndexType )Concat5Concatenates five arrays, A1, ..., A5. I1, ..., I5 are the indexes joined; J is the index of the new array; J usually is the concatenation of I1, ..., I5.Index I12 := Concat(I1,I2);
Index I123:= Concat(I12, I3);
Index I1234 := Concat(I123, I4);
Concat(
Concat(
Concat(
Concat( A1,A2,I1,I2,I12 ),
A3, I12, I3, I123),
A4, I123, I4, I1234),
A5, I1234, I5, J);88,120,148,242,160,160,986,596A1,A2,A3,A4,A5,I1,I2,I3,I4,I5,J(A1, A2, A3, A4, A5, A6: ArrayType; I1, I2, I3, I4, I5, I6, J: IndexType )Concat6Concatenates six arrays, A1, ..., A6. I1, ..., I6 are the indexes joined; J is the index of the new array; J usually is the concatenation of I1, ..., I6.Index I12 := Concat(I1,I2);
Index I123:= Concat(I12, I3);
Index I1234 := Concat(I123, I4);
Index I12345 := Concat(I1234, I5);
Concat(
Concat(
Concat(
Concat(
Concat( A1,A2,I1,I2,I12 ),
A3, I12, I3, I123),
A4, I123, I4, I1234),
A5, I1234, I5, I12345),
A6, I12345, I6, J);192,120,148,242,644,94,602,712A1,A2,A3,A4,A5,A6,I1,I2,I3,I4,I5,I6,J0(A1, A2, A3, A4, A5, A6, A7: ArrayType; I1, I2, I3, I4, I5, I6, I7, J: IndexType )Concat7Concatenates seven arrays, A1, ..., A7. I1, ..., I7 are the indexes joined; J is the index of the new array; J usually is the concatenation of I1, ..., I7.Index I12 := Concat(I1,I2);
Index I123:= Concat(I12, I3);
Index I1234 := Concat(I123, I4);
Index I12345 := Concat(I1234, I5);
Index I123456 := Concat(I12345, I6);
Concat(
Concat(
Concat(
Concat(
Concat(
Concat( A1,A2,I1,I2,I12 ),
A3, I12, I3, I123),
A4, I123, I4, I1234),
A5, I1234, I5, I12345),
A6, I12345, I6, I123456),
A7, I123456, I7, J);88,176,148,242,580,98,551,565A1,A2,A3,A4,A5,A6,A7,I1,I2,I3,I4,I5,I6,I7,J(A1, A2, A3, A4, A5, A6, A7, A8: ArrayType; I1, I2, I3, I4, I5, I6, I7, I8, J: IndexType )Concat8Concatenates eight arrays, A1, ..., A8. I1, ..., I8 are the indexes joined; J is the index of the new array; J usually is the concatenation of I1, ..., I8.Index I12 := Concat(I1,I2);
Index I123:= Concat(I12, I3);
Index I1234 := Concat(I123, I4);
Index I12345 := Concat(I1234, I5);
Index I123456 := Concat(I12345, I6);
Index I1234567 := Concat(I123456, I7);
Concat(
Concat(
Concat(
Concat(
Concat(
Concat(
Concat( A1,A2,I1,I2,I12 ),
A3, I12, I3, I123),
A4, I123, I4, I1234),
A5, I1234, I5, I12345),
A6, I12345, I6, I123456),
A7, I123456, I7, I1234567),
A8, I1234567, I8, J);192,176,148,242,12,98,561,737A1,A2,A3,A4,A5,A6,A7,A8,I1,I2,I3,I4,I5,I6,I7,I8,J0(A1, A2, A3, A4, A5, A6, A7, A8, A9, A10: ArrayType; I1, I2, I3, I4, I5, I6, I7, I8, I9, I10, J: IndexType)Concat10Concatenates ten arrays, A1, ..., A10. I1, ..., I10 are the indexes joined; J is the index of the new array; J usually is the concatenation of I1, ..., I10.Index I12 := Concat(I1,I2);
Index I123 := Concat(I12, I3);
Index I1234 := Concat(I123, I4);
Index I12345 := Concat(I1234, I5);
Index I123456 := Concat(I12345, I6);
Index I1234567 := Concat(I123456, I7);
Index I12345678 := Concat(I1234567, I8);
Index I123456789 := Concat(I12345678, I9);
Concat(
Concat(
Concat(
Concat(
Concat(
Concat(
Concat(
Concat(
Concat( A1,A2,I1,I2,I12 ),
A3, I12, I3, I123),
A4, I123, I4, I1234),
A5, I1234, I5, I12345),
A6, I12345, I6, I123456),
A7, I123456, I7, I1234567),
A8, I1234567, I8, I12345678),
A9, I12345678, I9, I123456789),
A10, I123456789, I10, J);192,232,148,242,542,93,632,744A1,A2,A3,A4,A5,A6,A7,A8,A9,A10,I1,I2,I3,I4,I5,I6,I7,I8,I9,I10,J0(A : ArrayType ; RowIndex,ColIndex,ResultIndex : IndexType)ConcatRows (A,I,J,K)Takes an array, A indexed by RowIndex & ColIndex, and concatenates each row, henceforth flattening the array by one dimension. The result is indexed by ResultIndex, which must be an index with size(RowIndex) * size(ColIndex) elements.index L := [ identifier of RowIndex, identifier of ColIndex, "val"];
slice(Mdarraytotable(A,ResultIndex,L),L,3)320,64,164,242,499,85,478,348A,RowIndex,ColIndex,ResultIndexODBC LibraryLonnieThu, Sep 11, 1997 2:15 PMLonnieTue, Feb 05, 2008 10:03 AM48,2456,328,152,201,1,1,1,1,1,0,0,0,01,20,272,499,462,17Arial, 13(A:ArrayType;I:IndexType;L:IndexType;row:IndexType;dbTableName)InsertRecSqlGenerates the SQL "INSERT INTO" statement for one line of table A. A is a 2-D table indexed by rows I and columns L. L's domain serves as the column names in the database table. dbTableName is the name of the table in the database. The result begins with two semi-colons, since it will be used with an SQL statement preceeding it.
29.8.2008 Jouni Tuomisto
I added the parameter IGNORE because it ignores rows that would cause duplicate-key violations. This way, there is no need to check for e.g. existing locations of new indices.
6.1.2009 Jouni Tuomisto
I changed the A[I=row] to A[@I=@row] because the original function does not work correctly, if there are non-unique rows in the index.(';;INSERT IGNORE INTO ' & dbTableName & '(' & JoinText(L,L,',') & ') VALUES (' & Vallist(A[@I=@row],L)) & ') '184,32,152,242,591,203,487,469A,I,L,row,dbTableName(V:ArrayType;I:IndexType)ValListTakes a list of values, and returns a string which the concatenation of each value, separated by commas, and with each value quoted.JoinText( '''' & V & '''', I, ',')72,32,052,242,642,360,476,224V,I1,F,4,14,0,0(Tabl:ArrayType;RowIndex:IndexType;LabelIndex:IndexType;dbTableName)WriteTableSql(Table,Rows,Labels,dbTableName)Returns the SQL that will write the table to the database table.
This can be used as the second argument to DBWrite.
This SQL statement replaces the entire contents of an existing table with the new data.'DELETE FROM '& Dbtablename & JoinText(Insertrecsql(Tabl, Rowindex, Labelindex, Rowindex, Dbtablename),RowIndex)328,32,188,242,728,341,510,476Tabl,RowIndex,LabelIndex,dbTableName(Tabl:ArrayType;RowIndex:IndexType;LabelIndex:IndexType;dbTableName)AppendTableSql(Table,Rows,Labels,dbTableName)Returns the SQL that will write the table to the database table.
This can be used as the second argument to DBWrite.
This SQL statement replaces the entire contents of an existing table with the new data.JoinText(Insertrecsql(Tabl, Rowindex, Labelindex, Rowindex, Dbtablename),RowIndex)328,88,188,242,559,127,510,476Tabl,RowIndex,LabelIndex,dbTableName(table:texttype)CardBrings the largest id number from the table defined in the parameter.index i:= DBquery(odbc,'
SELECT MAX(id) AS id
FROM '&table&'
');
index j:= dblabels(i);
max(max(DBTable(i, j ),i),j)56,272,148,122,102,90,476,33139325,65535,39321tableTablesList of such tables in Opasnet Base that are being written to by this module.['Obj','Cell','Loc','Loccell','Sett','Item','Res']280,256,148,132,15,594,158,227,0,MIDM[Variable W_sett]['Obj','Cell','Loc','Loccell','Sett','Item','Res']CardinalsThe largest id values for the selected Opasnet Base tables. The table is updated by pressing the R_cardinals button.Table(Table1)(
565,93.66K,1774,517.571K,47,146,1.005417M
)280,232,148,122,634,394,476,3322,193,270,416,303,0,MIDM2,87,329,416,303,0,MIDM39325,65535,393212,I,4,2,0,0,4,0,$,0,"ABBREV",0[Variable W_sett](in, table; cond:texttype)FindidThis function gets an id from a table.
in: the property for which the id is needed. In MUST be unique in cond.
table: the table from where the id is brought. The table MUST have .j as the column index, .i as the row index, and a column named 'id'.
cond: the name of the field that is compared with in. Cond must be text.index L:= in[.i=unique(in, in.i)];
var a:= if (L&' ') = (table[.j=cond]&' ') then table[.j='id'] else 0;
a:= sum(a, table.i)&'';
a[.L=in]56,248,148,122,636,101,494,398in,table,cond[Variable W_sett](type)TypesFinds the objects that are of the object type "type" (the only parameter of this function). Based on the information in Objects1.var a:= if Objects1[.j='Typ_id']=type then 1 else 0;
Objects1[Object_all=subset(a),.j='id']56,224,148,122,551,191,476,344type(var, table)WriteFor Lumina AWP use the following should be used:
'Driver={MySQL ODBC 3.51 Driver};Server=193.167.179.97;Database=opasnet_base;User=resultwriter; Password=;Option=3'
For internal THL use the following should be used:
'Driver={MySQL ODBC 5.1 Driver};Server=10.66.10.102;Database=opasnet_base;User=resultwriter; Password='&writerpsswd&';Option=3'if size(var)>0 then dbwrite((if platform = 'Lumina AWP' then 'Driver={MySQL ODBC 3.51 Driver};Server=193.167.179.97' else 'Driver={MySQL ODBC 5.1 Driver};Server=10.66.10.102')&';Database=opasnet_base;User=resultwriter; Password='&writerpsswd&';Option=3'
, appendtablesql(var,var.i, var.j, table&' '))56,296,148,122,776,65,476,457var,tableODBC writeFor Lumina AWP use the following should be used:
'Driver={MySQL ODBC 3.51 Driver};Server=193.167.179.97;Database=opasnet_base;User=resultwriter; Password=;Option=3'
For internal THL use the following should be used:
'Driver={MySQL ODBC 5.1 Driver};Server=10.66.10.102;Database=opasnet_base;User=resultwriter; Password='&writerpsswd&';Option=3'var a:= if platform='Lumina AWP' then 'Driver={MySQL ODBC 3.51 Driver};Server=193.167.179.97' else 'Driver={MySQL ODBC 5.1 Driver};Server=10.66.10.102';
a&';Database=opasnet_base;User=resultwriter; Password='&writerpsswd&';Option=3'168,232,148,121,1,0,1,1,1,0,,0,2,102,90,495,3462,168,178,833,303,0,MIDM[]Opasnet usernameThe username for Opasnet wiki'Add username'168,161,148,221,1,1,1,1,1,0,0,0,0[Formnode Username1]52425,39321,65535Opasnet passwordThe user's password for Opasnet wiki.'Add password'168,200,148,221,1,1,1,1,1,0,0,0,0[Formnode Password1]52425,39321,65535ODBCContains the parameters for the open database connectivity (ODBC).
For Lumina AWP use the following should be used:
'Driver={MySQL ODBC 3.51 Driver};Server=193.167.179.97;Database=opasnet_base;User=result_reader; Password=ora4ever;Option=3'
For THL internal use the following should be used:
'Driver={MySQL ODBC 5.1 Driver};Server=10.66.10.102;Database=opasnet_base;User=result_reader; Password=ora4ever;Option=3'var a:= if platform='Lumina AWP' then 'Driver={MySQL ODBC 3.51 Driver};Server=193.167.179.97' else 'Driver={MySQL ODBC 5.1 Driver};Server=10.66.10.102';
a&';Database=opasnet_base;User=result_reader; Password=ora4ever;Option=3'168,128,148,121,1,0,1,1,1,0,,0,2,102,90,508,4202,56,66,918,303,0,MIDMDimindex i:= copyindex(D_i);
index j:= copyindex(D_j);
Dim1[d_i=i, d_j=j]400,160,148,131,1,0,1,1,1,0,0,0,02,89,98,476,2242,635,328,556,489,0,MIDM19661,54073,65535[D_i,D_j][Sys_localindex('J'),Sys_localindex('I')]Indindex i:= copyindex(I_i);
index j:= copyindex(I_j);
Ind1[I_i=i, I_j=j]400,184,148,131,1,0,1,1,1,0,0,0,02,380,47,476,2962,490,110,649,655,0,MIDM19661,54073,65535[Sys_localindex('J'),Sys_localindex('I')]Locindex i:= copyindex(L_i);
index j:= copyindex(L_j);
Loc1[L_i=i, L_j=j]400,96,148,131,1,0,1,1,1,0,0,0,02,370,45,476,4452,43,42,1147,516,0,MIDM19661,54073,65535[Sys_localindex('J'),Sys_localindex('I')]ObjThis node checks the variables listed in Var_for_rdb and makes an index of those that are NOT found in the result database. This is then used as an index in Inp_var for adding variable information.index i:= copyindex(O_i);
index j:= copyindex(O_j);
Obj2[O_i=i, O_j=j]400,48,148,131,1,0,1,1,1,0,0,0,02,378,21,493,5012,21,103,977,421,0,MIDM19661,54073,65535[Sys_localindex('J'),Sys_localindex('I')][Variable W_sett]['H1991'][Self,1,Sys_localindex('I'),1,Sys_localindex('J'),1]Standard versions400,112,-172,1001,0,0,1,0,1,0,,0,D_i[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22]168,24,148,12[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22]D_j['id','Ident','Name']168,48,148,12['id','Ident','Name']I_i[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34]168,72,148,12[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34]I_j['id','Iident','Iname','Did','Dident','Dname']168,96,148,12['id','Iident','Iname','Did','Dident','Dname']L_i[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,775,776,777,778,779,780,781,782,783,784,785,786,787,788,789,790,791,792,793,794,795,796,797,798,799,800,801,802,803,804,805,806,807,808,809,810,811,812,813,814,815,816,817,818,819,820,821,822,823,824,825,826,827,828,829,830,831,832,833,834,835,836,837,838,839,840,841,842,843,844,845,846,847,848,849,850,851,852,853,854,855,856,857,858,859,860,861,862,863,864,865,866,867,868,869,870,871,872,873,874,875,876,877,878,879,880,881,882,883,884,885,886,887,888,889,890,891,892,893,894,895,896,897,898,899,900,901,902,903,904,905,906,907,908,909,910,911,912,913,914,915,916,917,918,919,920,921,922,923,924,925,926,927,928,929,930,931,932,933,934,935,936,937,938,939,940,941,942,943,944,945,946,947,948,949,950,951,952,953,954,955,956,957,958,959,960,961,962,963,964,965,966,967,968,969,970,971,972,973,974,975,976,977,978,979,980,981,982,983,984,985,986,987,988,989,990,991,992,993,994,995,996,997,998,999,1000,1001,1002,1003,1004,1005,1006,1007,1008,1009,1010,1011,1012,1013,1014,1015,1016,1017,1018,1019,1020,1021,1022,1023,1024,1025,1026,1027,1028,1029,1030,1031,1032,1033,1034,1035,1036,1037,1038,1039,1040,1041,1042,1043,1044,1045,1046,1047,1048,1049,1050,1051,1052,1053,1054,1055,1056,1057,1058,1059,1060,1061,1062,1063,1064,1065,1066,1067,1068,1069,1070,1071,1072,1073,1074,1075,1076,1077,1078,1079,1080,1081,1082,1083,1084,1085,1086,1087,1088,1089,1090,1091,1092,1093,1094,1095,1096,1097,1098,1099,1100,1101,1102,1103,1104,1105,1106,1107,1108,1109,1110,1111,1112,1113,1114,1115,1116,1117,1118,1119,1120,1121,1122,1123,1124,1125,1126,1127,1128,1129,1130,1131,1132,1133,1134,1135,1136,1137,1138,1139,1140,1141,1142,1143,1144,1145,1146,1147,1148,1149,1150,1151,1152,1153,1154,1155,1156,1157,1158,1159,1160,1161,1162,1163,1164,1165,1166,1167,1168,1169,1170,1171,1172,1173,1174,1175,1176,1177,1178,1179,1180,1181,1182,1183,1184,1185,1186,1187,1188,1189,1190,1191,1192,1193,1194,1195,1196,1197,1198,1199,1200,1201,1202,1203,1204,1205,1206,1207,1208,1209,1210,1211,1212,1213,1214,1215,1216,1217,1218,1219,1220,1221,1222,1223,1224,1225,1226,1227,1228,1229,1230,1231,1232,1233,1234,1235,1236,1237,1238,1239,1240,1241,1242,1243,1244,1245,1246,1247,1248,1249,1250,1251,1252,1253,1254,1255,1256,1257,1258,1259,1260,1261,1262,1263,1264,1265,1266,1267,1268,1269,1270,1271,1272,1273,1274,1275,1276,1277,1278,1279,1280,1281,1282,1283,1284,1285,1286,1287,1288,1289,1290,1291,1292,1293,1294,1295,1296,1297,1298,1299,1300,1301,1302,1303,1304,1305,1306,1307,1308,1309,1310,1311,1312,1313,1314,1315,1316,1317,1318,1319,1320,1321,1322,1323,1324,1325,1326,1327,1328,1329,1330,1331,1332,1333,1334,1335,1336,1337,1338,1339,1340,1341,1342,1343,1344,1345,1346,1347,1348,1349,1350,1351,1352,1353,1354,1355,1356,1357,1358,1359,1360,1361,1362,1363,1364,1365,1366,1367,1368,1369,1370,1371,1372]56,120,148,12[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,775,776,777,778,779,780,781,782,783,784,785,786,787,788,789,790,791,792,793,794,795,796,797,798,799,800,801,802,803,804,805,806,807,808,809,810,811,812,813,814,815,816,817,818,819,820,821,822,823,824,825,826,827,828,829,830,831,832,833,834,835,836,837,838,839,840,841,842,843,844,845,846,847,848,849,850,851,852,853,854,855,856,857,858,859,860,861,862,863,864,865,866,867,868,869,870,871,872,873,874,875,876,877,878,879,880,881,882,883,884,885,886,887,888,889,890,891,892,893,894,895,896,897,898,899,900,901,902,903,904,905,906,907,908,909,910,911,912,913,914,915,916,917,918,919,920,921,922,923,924,925,926,927,928,929,930,931,932,933,934,935,936,937,938,939,940,941,942,943,944,945,946,947,948,94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node checks the variables listed in Var_for_rdb and makes an index of those that are NOT found in the result database. This is then used as an index in Inp_var for adding variable information.index i:= copyindex(S_i);
index j:= copyindex(S_j);
Sett1[S_i=i, S_j=j]400,72,148,131,1,0,1,1,1,0,0,0,02,378,21,493,5012,227,134,319,515,0,MIDM19661,54073,65535[Sys_localindex('J'),Sys_localindex('I')]['H1991'][Self,1,Sys_localindex('I'),1,Sys_localindex('J'),1]ItemThis node checks the variables listed in Var_for_rdb and makes an index of those that are NOT found in the result database. This is then used as an index in Inp_var for adding variable information.index i:= copyindex(It_i);
index j:= copyindex(It_j);
Item1[it_i=i, it_j=j]400,120,148,131,1,0,1,1,1,0,0,0,02,378,21,493,5012,298,216,382,519,0,MIDM19661,54073,65535[Sys_localindex('J'),Sys_localindex('I')]['H1991'][Self,1,Sys_localindex('I'),1,Sys_localindex('J'),1]It_i[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35]56,168,148,13[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35]It_j['id','Sett_id','Obj_id','Fail']56,192,148,13['id','Sett_id','Obj_id','Fail']S_i[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28]56,72,148,13[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28]S_j['id','Obj_id','Settype_id']56,96,148,13['id','Obj_id','Settype_id']DimTable(D_i,D_j)(
43,'Vehicle_type','Vehicle type',
45,'Transport_mode','Transport mode',
46,'Cost_type','Cost type',
47,'Composite_fraction','Composite fraction',
51,'Food_source','The method for food production',
52,'Feed_pollutant','Decision about fish feed',
53,'Salmon_recomm','Decision about samon consumption recommendation',
32,'0','No dimension has been identified',
54,'Parameter','Statistical and other parameters of a variable',
42,'Environ_compartment','Environmental compartment',
41,'Emission_source','Emission source',
36,'Pollutant','Pollutant',
34,'Health_impact','Health impact',
33,'Decision','Possible range of decisions for a single decision-maker',
35,'Time','Time',
40,'Period','Period',
48,'Age','Age',
37,'Spatial_location','Spatial location',
38,'Length','Length',
49,'Municipality_fin','Municipalities in Finland',
44,'Person_or_group','Person or group',
39,'Non_health_impact','Non-health impact'
)280,160,148,131,1,1,1,1,1,0,0,0,02,89,98,476,2242,604,56,556,489,0,MIDM39325,65535,39321[D_i,D_j][D_j,D_i]IndTable(I_i,I_j)(
55,'Salmon_decision','',33,'Decision','Possible range of decisions for a single decision-maker',
80,'Reg_poll','',33,'Decision','Possible range of decisions for a single decision-maker',
81,'Recommendation1','',33,'Decision','Possible range of decisions for a single decision-maker',
83,'H1899','',33,'Decision','Possible range of decisions for a single decision-maker',
84,'H1898','',33,'Decision','Possible range of decisions for a single decision-maker',
56,'Hma_area','',37,'Spatial_location','Spatial location',
57,'Hma_region','',37,'Spatial_location','Spatial location',
58,'Hma_zone','',37,'Spatial_location','Spatial location',
88,'Condb_location1','',37,'Spatial_location','Spatial location',
93,'Op_en2672','',37,'Spatial_location','Spatial location',
59,'Year_1','',35,'Time','Time',
61,'Year_2','',35,'Time','Time',
82,'Year3','',35,'Time','Time',
60,'Op_en2665','Cause of death 1',34,'Health_impact','Health impact',
62,'Cause_of_death_2','',34,'Health_impact','Health impact',
85,'Cause_of_death3','',34,'Health_impact','Health impact',
63,'Length_1','',38,'Length','Length',
70,'Output_1','',39,'Non_health_impact','Non-health impact',
65,'Period_1','',40,'Period','Period',
86,'Run','',32,'0','No dimension has been identified',
71,'Vehicle_noch','',43,'Vehicle_type','Vehicle type',
92,'Vehicle_1','',43,'Vehicle_type','Vehicle type',
72,'Stakeholder_1','',44,'Person_or_group','Person or group',
73,'Mode1','',45,'Transport_mode','Transport mode',
74,'Cost_structure_1','',46,'Cost_type','Cost type',
75,'Comp_fr_1','',47,'Composite_fraction','Composite fraction',
76,'Age1','',48,'Age','Age',
77,'Municipality_fin1','',49,'Municipality_fin','Municipalities in Finland',
79,'Salmon1','',51,'Food_source','The method for food production',
78,'Pollutant1','',36,'Pollutant','Pollutant',
89,'Condb_agent1','',36,'Pollutant','Pollutant',
91,'Condb_agent2','',36,'Pollutant','Pollutant',
87,'Condb_compartment1','',42,'Environ_compartment','Environmental compartment',
90,'Condb_param1','',54,'Parameter','Statistical and other parameters of a variable'
)280,184,148,131,1,1,1,1,1,0,0,0,02,380,47,476,2962,232,242,874,303,0,MIDM2,12,22,876,493,0,MIDM39325,65535,39321[I_j,I_i][I_j,I_i]LocTable(L_i,L_j)(
1,1,'Business as usual',0,'',1,'Op_en1901','Net health effects due to the consumption of salmon','avoided cases/a',1,1901,1,
2,1,'Recommend restrictions to salmon consumption',0,'',2,'Op_en1901','Net health effects due to the consumption of salmon','avoided cases/a',1,1901,1,
3,1,'Stricter limits for fish feed pollutants',0,'',3,'Op_en1901','Net health effects due to the consumption of salmon','avoided cases/a',1,1901,1,
4,1,'Restrictions to salmon consumption AND stricter fish feed limits',0,'',4,'Op_en1901','Net health effects due to the consumption of salmon','avoided cases/a',1,1901,1,
26,2,'All causes',0,'',26,'Op_en2693','Testvariable','kg',1,2693,1,
197,6,'>= 5 km',0,'',197,'Ppmconc_bustraffic','PM2.5 concentration from bus traffic in Helsinki in 2020','ug/m3',1,0,0,
196,6,'< 5 km',0,'',196,'Ppmconc_bustraffic','PM2.5 concentration from bus traffic in Helsinki in 2020','ug/m3',1,0,0,
8,3,'2020',0,'',8,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1,
7,3,'1997',0,'',7,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1,
10,2,'Cardiopulmonary',0,'',10,'Op_en2693','Testvariable','kg',1,2693,1,
11,2,'Lung cancer',0,'',11,'Op_en2693','Testvariable','kg',1,2693,1,
12,2,'All others',0,'',12,'Op_en2693','Testvariable','kg',1,2693,1,
27,5,'Downtown',0,'',27,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
28,5,'Centre',0,'',28,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
29,5,'Suburb',0,'',29,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
30,5,'Länsi-Espoo',0,'',30,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
31,5,'Pohjois-Espoo',0,'',31,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
32,5,'Etelä-Espoo',0,'',32,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
33,5,'Keski-Espoo',0,'',33,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
34,5,'Länsi-Vantaa',0,'',34,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
35,5,'Keski-Vantaa',0,'',35,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
36,5,'Pohjois-Vantaa',0,'',36,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
37,5,'Itä-Vantaa',0,'',37,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
38,5,'Kanta-Helsinki',0,'',38,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
39,5,'Länsi-Helsinki',0,'',39,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
40,5,'Vanha-Helsinki',0,'',40,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
41,5,'Konalanseutu',0,'',41,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
42,5,'Pakilanseutu',0,'',42,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
43,5,'Malminseutu',0,'',43,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
44,5,'Itä-Helsinki',0,'',44,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
45,5,'1001',0,'',45,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
46,5,'1002',0,'',46,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
47,5,'1003',0,'',47,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
48,5,'1004',0,'',48,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
49,5,'1005',0,'',49,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
50,5,'1006',0,'',50,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
51,5,'1007',0,'',51,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
52,5,'1008',0,'',52,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
53,5,'1009',0,'',53,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
54,5,'1010',0,'',54,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
55,5,'1011',0,'',55,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
56,5,'1012',0,'',56,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
57,5,'1013',0,'',57,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
58,5,'1014',0,'',58,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
59,5,'1015',0,'',59,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
60,5,'1016',0,'',60,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
61,5,'1017',0,'',61,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
62,5,'1018',0,'',62,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
63,5,'1019',0,'',63,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
64,5,'1020',0,'',64,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
65,5,'1021',0,'',65,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
66,5,'1022',0,'',66,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
67,5,'1023',0,'',67,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
68,5,'1024',0,'',68,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
69,5,'1025',0,'',69,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
70,5,'1026',0,'',70,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
71,5,'1027',0,'',71,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
72,5,'1028',0,'',72,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
73,5,'1029',0,'',73,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
74,5,'1030',0,'',74,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
75,5,'1031',0,'',75,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
76,5,'1032',0,'',76,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
77,5,'1033',0,'',77,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
78,5,'1034',0,'',78,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
79,5,'1035',0,'',79,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
80,5,'1036',0,'',80,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
81,5,'1037',0,'',81,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
82,5,'1038',0,'',82,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
83,5,'1039',0,'',83,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
84,5,'1040',0,'',84,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
85,5,'1041',0,'',85,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
86,5,'1042',0,'',86,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
87,5,'1043',0,'',87,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
88,5,'1044',0,'',88,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
89,5,'1045',0,'',89,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
90,5,'1046',0,'',90,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
91,5,'1047',0,'',91,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
92,5,'1048',0,'',92,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
93,5,'1049',0,'',93,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
94,5,'1050',0,'',94,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
95,5,'1051',0,'',95,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
96,5,'1052',0,'',96,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
97,5,'1053',0,'',97,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
98,5,'1054',0,'',98,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
99,5,'1055',0,'',99,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
100,5,'1056',0,'',100,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
101,5,'1057',0,'',101,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
102,5,'1058',0,'',102,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
103,5,'1059',0,'',103,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
104,5,'1060',0,'',104,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
105,5,'1061',0,'',105,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
106,5,'1062',0,'',106,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
107,5,'1063',0,'',107,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
108,5,'1064',0,'',108,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
109,5,'1065',0,'',109,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
110,5,'1066',0,'',110,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
111,5,'1067',0,'',111,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
112,5,'1068',0,'',112,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
113,5,'1069',0,'',113,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
114,5,'1070',0,'',114,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
115,5,'1071',0,'',115,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
116,5,'1072',0,'',116,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
117,5,'1073',0,'',117,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
118,5,'1074',0,'',118,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
119,5,'1075',0,'',119,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
120,5,'1076',0,'',120,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
121,5,'1077',0,'',121,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
122,5,'1078',0,'',122,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
123,5,'1079',0,'',123,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
124,5,'1080',0,'',124,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
125,5,'1081',0,'',125,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
126,5,'1082',0,'',126,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
127,5,'1083',0,'',127,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
128,5,'1084',0,'',128,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
129,5,'1085',0,'',129,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
130,5,'1086',0,'',130,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
131,5,'1087',0,'',131,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
132,5,'1088',0,'',132,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
133,5,'1089',0,'',133,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
134,5,'1090',0,'',134,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
135,5,'1091',0,'',135,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
136,5,'1092',0,'',136,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
137,5,'1093',0,'',137,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
138,5,'1094',0,'',138,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
139,5,'1095',0,'',139,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
140,5,'1096',0,'',140,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
141,5,'1097',0,'',141,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
142,5,'1098',0,'',142,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
143,5,'1099',0,'',143,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
144,5,'1100',0,'',144,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
145,5,'1101',0,'',145,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
146,5,'1102',0,'',146,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
147,5,'1103',0,'',147,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
148,5,'1104',0,'',148,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
149,5,'1105',0,'',149,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
150,5,'1106',0,'',150,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
151,5,'1107',0,'',151,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
152,5,'1108',0,'',152,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
153,5,'1109',0,'',153,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
154,5,'1110',0,'',154,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
155,5,'1111',0,'',155,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
156,5,'1112',0,'',156,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
157,5,'1113',0,'',157,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
158,5,'1114',0,'',158,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
159,5,'1115',0,'',159,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
160,5,'1116',0,'',160,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
161,5,'1117',0,'',161,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
162,5,'1118',0,'',162,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
163,5,'1119',0,'',163,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
164,5,'1120',0,'',164,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
165,5,'1121',0,'',165,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
166,5,'1122',0,'',166,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
167,5,'1123',0,'',167,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
168,5,'1124',0,'',168,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
169,5,'1125',0,'',169,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
170,5,'1126',0,'',170,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
171,5,'1127',0,'',171,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
172,5,'1128',0,'',172,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
173,5,'1129',0,'',173,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
174,5,'1130',0,'',174,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
175,35,'2000',0,'',175,'Time','Time','s or date',2,2497,1,
176,3,'2001',0,'',176,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1,
177,3,'2002',0,'',177,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1,
178,3,'2003',0,'',178,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1,
179,3,'2004',0,'',179,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1,
180,3,'2005',0,'',180,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1,
181,3,'2006',0,'',181,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1,
182,3,'2007',0,'',182,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1,
183,3,'2008',0,'',183,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1,
184,3,'2009',0,'',184,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1,
185,3,'2010',0,'',185,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1,
186,3,'2011',0,'',186,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1,
187,3,'2012',0,'',187,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1,
188,3,'2013',0,'',188,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1,
189,3,'2014',0,'',189,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1,
190,3,'2015',0,'',190,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1,
191,3,'2016',0,'',191,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1,
192,3,'2017',0,'',192,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1,
193,3,'2018',0,'',193,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1,
194,3,'2019',0,'',194,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1,
418,1,'BAU3',0,'',418,'Op_en1901','Net health effects due to the consumption of salmon','avoided cases/a',1,1901,1,
198,8,' 6.00-20.00',0,'',198,'Comptraf_scenoutput','Composite traffic v.1 scenario outputs','various',1,0,0,
199,8,'20.00-24.00',0,'',199,'Comptraf_scenoutput','Composite traffic v.1 scenario outputs','various',1,0,0,
200,8,' 0.00- 6.00',0,'',200,'Comptraf_scenoutput','Composite traffic v.1 scenario outputs','various',1,0,0,
364,7,'Trips',0,'',364,'Op_en2202','Concentration-response to PM2.5','m3/ug',1,2202,1,
365,7,'Trips by vehicle',0,'',365,'Op_en2202','Concentration-response to PM2.5','m3/ug',1,2202,1,
366,7,'Vehicle km',0,'',366,'Op_en2202','Concentration-response to PM2.5','m3/ug',1,2202,1,
367,7,'Parking lot',0,'',367,'Op_en2202','Concentration-response to PM2.5','m3/ug',1,2202,1,
368,7,'Link intensity',0,'',368,'Op_en2202','Concentration-response to PM2.5','m3/ug',1,2202,1,
369,7,'Vehicles',0,'',369,'Op_en2202','Concentration-response to PM2.5','m3/ug',1,2202,1,
370,7,'Waiting',0,'',370,'Op_en2202','Concentration-response to PM2.5','m3/ug',1,2202,1,
371,11,'Bus no change',0,'',371,'Fig_5b_subsidies','Subsidies needed to obtain the composite fraction objective','e/day',1,0,0,
372,11,'Bus one change',0,'',372,'Fig_5b_subsidies','Subsidies needed to obtain the composite fraction objective','e/day',1,0,0,
373,11,'Cab no change',0,'',373,'Fig_5b_subsidies','Subsidies needed to obtain the composite fraction objective','e/day',1,0,0,
374,11,'Cab one change',0,'',374,'Fig_5b_subsidies','Subsidies needed to obtain the composite fraction objective','e/day',1,0,0,
375,11,'Cab non-full',0,'',375,'Fig_5b_subsidies','Subsidies needed to obtain the composite fraction objective','e/day',1,0,0,
376,11,'Car',0,'',376,'Fig_5b_subsidies','Subsidies needed to obtain the composite fraction objective','e/day',1,0,0,
377,11,'No-change',0,'',377,'Fig_5b_subsidies','Subsidies needed to obtain the composite fraction objective','e/day',1,0,0,
378,12,'Passenger',0,'',378,'Fig_5c_expanding','Societal costs at different levels of guarantee','e/day',1,0,0,
379,12,'Society',0,'',379,'Fig_5c_expanding','Societal costs at different levels of guarantee','e/day',1,0,0,
380,13,'Car',0,'',380,'Bw1','Human body weight in Harjavalta','kg',1,2475,1,
381,13,'Composite',0,'',381,'Bw1','Human body weight in Harjavalta','kg',1,2475,1,
382,14,'Vehicle',0,'',382,'Testvariable2','Another variable for testing','kg',1,0,0,
383,14,'Driver',0,'',383,'Testvariable2','Another variable for testing','kg',1,0,0,
384,14,'Driving',0,'',384,'Testvariable2','Another variable for testing','kg',1,0,0,
385,14,'Parking',0,'',385,'Testvariable2','Another variable for testing','kg',1,0,0,
386,14,'Parking land',0,'',386,'Testvariable2','Another variable for testing','kg',1,0,0,
387,14,'Emissions',0,'',387,'Testvariable2','Another variable for testing','kg',1,0,0,
388,14,'Time',0,'',388,'Testvariable2','Another variable for testing','kg',1,0,0,
389,14,'Accidents',0,'',389,'Testvariable2','Another variable for testing','kg',1,0,0,
390,14,'Ticket',0,'',390,'Testvariable2','Another variable for testing','kg',1,0,0,
391,15,'0',0,'',391,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0,
392,15,'0.02',0,'',392,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0,
393,15,'0.05',0,'',393,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0,
394,15,'0.1',0,'',394,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0,
395,15,'0.25',0,'',395,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0,
396,15,'0.4',0,'',396,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0,
397,15,'0.45',0,'',397,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0,
398,15,'0.5',0,'',398,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0,
399,15,'0.55',0,'',399,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0,
400,15,'0.65',0,'',400,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0,
401,15,'0.75',0,'',401,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0,
402,15,'0.9',0,'',402,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0,
403,15,'1',0,'',403,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0,
404,16,'18-65',0,'',404,'Op_en1900','Pollutant health risk due to the consumption of salmon','avoided cases/a',1,1900,1,
405,16,'3',0,'',405,'Op_en1900','Pollutant health risk due to the consumption of salmon','avoided cases/a',1,1900,1,
406,17,'Harjavalta',0,'',406,'Op_en1903','Persistent pollutant concentrations in salmon','µg/kg',1,1903,1,
407,36,'Dieldrin',0,'',407,'Pollutant','Pollutant','-',2,2493,1,
408,36,'Toxaphene',0,'',408,'Pollutant','Pollutant','-',2,2493,1,
409,36,'Dioxin',0,'',409,'Pollutant','Pollutant','-',2,2493,1,
410,36,'PCB',0,'',410,'Pollutant','Pollutant','-',2,2493,1,
411,42,'Farmed salmon',0,'',411,'Environ_compartment','Environmental compartment','-',2,2490,1,
412,42,'Wild salmon',0,'',412,'Environ_compartment','Environmental compartment','-',2,2490,1,
413,42,'Market salmon',0,'',413,'Environ_compartment','Environmental compartment','-',2,2490,1,
414,33,'BAU',0,'',414,'Decision','Possible range of decisions for a single decision-maker','-',2,2496,1,
415,33,'More actions',0,'',415,'Decision','Possible range of decisions for a single decision-maker','-',2,2496,1,
416,33,'BAU2',0,'',416,'Decision','Possible range of decisions for a single decision-maker','-',2,2496,1,
417,33,'Restrict farmed salmon use',0,'',417,'Decision','Possible range of decisions for a single decision-maker','-',2,2496,1,
419,1,'More actions',0,'',419,'Op_en1901','Net health effects due to the consumption of salmon','avoided cases/a',1,1901,1,
421,1,'Restrict farmed salmon use2',0,'',421,'Op_en1901','Net health effects due to the consumption of salmon','avoided cases/a',1,1901,1,
422,34,'Cardiovascular',0,'',422,'Health_impact','Health impact','',2,2495,1,
423,10,'Home indoor',0,'Abbreviation in the Concentration database: I',423,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0,
424,10,'(Home) outdoor',0,'Abbreviation in the Concentration database: O',424,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0,
425,10,'(Personal) Work',0,'Abbreviation in the Concentration database: W',425,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0,
426,10,'Personal',0,'Abbreviation in the Concentration database: P',426,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0,
427,10,'Drinking water',0,'Abbreviation in the Concentration database: DW',427,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0,
428,10,'Indoor dust',0,'Abbreviation in the Concentration database: ID',428,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0,
429,10,'Human',0,'Abbreviation in the Concentration database: H',429,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0,
430,10,'Soil',0,'Abbreviation in the Concentration database: S',430,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0,
431,10,'Beverage',0,'Abbreviation in the Concentration database: B',431,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0,
432,10,'Food',0,'Abbreviation in the Concentration database: F',432,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0,
433,10,'In-Vehicle',0,'Abbreviation in the Concentration database: IV',433,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0,
434,10,'School',0,'Abbreviation in the Concentration database: SC',434,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0,
435,5,'Athens',0,'Country: Greece. Abbreviation in the Concentration Database: A',435,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
436,5,'Antwerp',0,'Country: Belgium. Abbreviation in the Concentration Database: ANT',436,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
437,5,'Antioch-Pittsburg',0,'Country: USA. Abbreviation in the Concentration Database: AP',437,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
438,5,'Antioch-Pittsburg A-P',0,'Country: USA. Abbreviation in the Concentration Database: A-P',438,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
439,5,'Arizona',0,'Country: USA. Abbreviation in the Concentration Database: AZ',439,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
440,5,'Basel',0,'Country: Germany. Abbreviation in the Concentration Database: B',440,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
441,5,'Baltimore',0,'Country: USA. Abbreviation in the Concentration Database: BAL',441,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
442,5,'Bayonne',0,'Country: USA. Abbreviation in the Concentration Database: BAY',442,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
443,5,'Bayonne-Ellizabeth',0,'Country: USA. Abbreviation in the Concentration Database: BE',443,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
444,5,'Copenhagen',0,'Country: Denmark. Abbreviation in the Concentration Database: C',444,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
445,5,'California',0,'Country: USA. Abbreviation in the Concentration Database: CA',445,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
446,5,'Columbus',0,'Country: USA. Abbreviation in the Concentration Database: CO',446,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
447,5,'Daegu',0,'Country: South Korea. Abbreviation in the Concentration Database: D',447,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
448,5,'Devils Lake',0,'Country: USA. Abbreviation in the Concentration Database: DLA',448,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
449,5,'Dublin',0,'Country: Ireland. Abbreviation in the Concentration Database: DU',449,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
450,5,'Elizabeth',0,'Country: USA. Abbreviation in the Concentration Database: ELI',450,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
451,5,'EPA Region 5.',0,'Country: USA. Abbreviation in the Concentration Database: EPA5',451,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
452,5,'Flanders',0,'Country: Belgium. Abbreviation in the Concentration Database: FLA',452,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
453,5,'Florence',0,'Country: Italy. Abbreviation in the Concentration Database: FL',453,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
454,5,'Grenoble',0,'Country: France. Abbreviation in the Concentration Database: G',454,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
455,5,'Germany',0,'Country: Germany. Abbreviation in the Concentration Database: GE',455,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
456,5,'Genoa',0,'Country: Italy. Abbreviation in the Concentration Database: GEN',456,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
457,5,'Greensboro GNC',0,'Country: USA. Abbreviation in the Concentration Database: GNC',457,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
458,5,'Greensboro',0,'Country: USA. Abbreviation in the Concentration Database: GRB',458,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
460,5,'Helsinki',0,'Country: Finland. Abbreviation in the Concentration Database: H',460,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
461,5,'Hannover',0,'Country: Germany. Abbreviation in the Concentration Database: HA',461,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
462,5,'Ile de France',0,'Country: France. Abbreviation in the Concentration Database: IDF',462,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
463,5,'Los Angeles',0,'Country: USA. Abbreviation in the Concentration Database: LA',463,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
464,5,'Milan',0,'Country: Italy. Abbreviation in the Concentration Database: M',464,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
465,5,'Minneapolis',0,'Country: USA. Abbreviation in the Concentration Database: MP',465,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
466,5,'Minnesota',0,'Country: USA. Abbreviation in the Concentration Database: MS',466,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
467,5,'Murcia',0,'Country: Spain. Abbreviation in the Concentration Database: MU',467,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
468,5,'Mexico City',0,'Country: Mexico. Abbreviation in the Concentration Database: MXC',468,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
469,5,'Oxford',0,'Country: England. Abbreviation in the Concentration Database: O',469,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
470,5,'Prague',0,'Country: Czech. Abbreviation in the Concentration Database: P',470,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
471,5,'Padua',0,'Country: Italy. Abbreviation in the Concentration Database: PA',471,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
472,5,'Puebla',0,'Country: Mexico. Abbreviation in the Concentration Database: PB',472,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
473,5,'Rouen',0,'Country: France. Abbreviation in the Concentration Database: R',473,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
475,5,'Strasbourg',0,'Country: France. Abbreviation in the Concentration Database: STR',475,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
476,5,'Umbria region',0,'Country: Italy. Abbreviation in the Concentration Database: UMB',476,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
477,5,'United States',0,'Country: USA. Abbreviation in the Concentration Database: USA',477,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
478,5,'Valdez',0,'Country: USA. Abbreviation in the Concentration Database: VAL',478,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
479,5,'Woodland',0,'Country: USA. Abbreviation in the Concentration Database: WDL',479,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
480,4,'66-25-1',0,'hexanal',480,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
481,4,'71-36-3',0,'1-butanol',481,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
482,4,'71-43-2',0,'benzene',482,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
483,4,'78-83-1',0,'2-methyl-1-propanol',483,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
484,4,'79-00-5',0,'1,1,2-trichloroethane',484,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
485,4,'79-01-6',0,'trichloroethene',485,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
486,4,'80-56-8',0,'alfa-pinene',486,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
487,4,'91-20-3',0,'naphtalene',487,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
488,4,'95-47-6',0,'o-xylene',488,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
489,4,'95-63-6',0,'trimethylbenzenes',489,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
490,4,'100-41-4',0,'ethylbenzene',490,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
491,4,'100-42-5',0,'styrene',491,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
492,4,'100-52-7',0,'benzaldehyde',492,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
493,4,'103-65-1',0,'propylbenzene',493,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
494,4,'104-76-7',0,'2-ethylhexanol',494,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
495,4,'108-38-3',0,'m(&p)-xylene',495,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
496,4,'108-88-3',0,'toluene',496,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
497,4,'108-95-2',0,'phenol',497,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
498,4,'110-54-3',0,'hexane',498,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
499,4,'110-82-7',0,'cyclohexane',499,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
500,4,'111-76-2',0,'ethanol, 2-butoxy-',500,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
501,4,'111-84-2',0,'nonane',501,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
502,4,'111-87-5',0,'1-octanol',502,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
503,4,'124-13-0',0,'octanal',503,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
504,4,'124-18-5',0,'decane',504,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
505,4,'127-18-4',0,'tetrachloroethene',505,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
506,4,'138-86-3',0,'d-limonene',506,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
507,4,'872-50-4',0,'2-pyrrolidinone, 1-methyl-',507,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
508,4,'1120-21-4',0,'undecane',508,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
509,4,'13466-78-9',0,'3-caren',509,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
510,4,'TVOC',0,'Toluene based total VOC',510,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
511,4,'67-66-3',0,'chloroform',511,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
512,4,'106-46-7',0,'1,4-dichlorobenzene',512,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
514,4,'56-23-5',0,'carbon tetrachloride',514,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
515,4,'75-09-2',0,'methylene chloride',515,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
517,4,'127-91-3',0,'b-pinene',517,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
520,4,'142-82-5',0,'n-heptane',520,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
521,4,'111-65-9',0,'n-octane',521,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
525,4,'112-40-3',0,'n-dodecane',525,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
526,4,'629-50-5',0,'n-tridecane',526,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
527,4,'629-59-4',0,'n-tetradecane',527,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
528,4,'629-62-9',0,'n-pentadecane',528,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
529,4,'107-83-5',0,'2-methylpentane',529,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
530,4,'96-14-0',0,'3-methylpentane',530,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
531,4,'565-59-3',0,'2,3-dimethylpentane',531,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
532,4,'591-76-4',0,'2-methylhexane',532,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
533,4,'589-34-4',0,'3-methylhexane',533,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
534,4,'592-27-8',0,'2-methylheptane',534,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
535,4,'589-81-1',0,'3-methylheptane',535,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
536,4,'96-37-7',0,'methylcyclopentane',536,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
537,4,'108-87-2',0,'methylcyclohexane',537,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
538,4,'526-73-8',0,'1,2,3-trimethylbenzene',538,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
540,4,'108-67-8',0,'1,3,5 trimethylbenzene',540,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
541,4,'4994-16-5',0,'4-phenylcyclohexene',541,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
542,4,'1,1,1-trichloroethane',0,'1,1,1-trichloroethane',542,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
545,4,'141-78-6',0,'ethylacetate',545,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
546,4,'123-86-4',0,'n-butylacetate',546,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
547,4,'78-93-3',0,'methyl ethyl ketone',547,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
548,4,'106-35-4',0,'3-heptatone',548,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
549,4,'93-58-3',0,'methyl benzoate',549,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
552,4,'123-51-3',0,'iso-amyl alcohol<sup>a</sup>',552,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
554,4,'67-63-0',0,'2-propanol<sup>a</sup>',554,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
555,4,'1634-04-4',0,'t-butyl methylether',555,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
556,4,'7439-92-1',0,'lead',556,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
557,4,'7440-38-2',0,'arsenic',557,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
558,4,'7440-43-9',0,'cadmium',558,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
559,4,'7440-39-3',0,'barium',559,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
560,4,'7440-47-3',0,'chrome',560,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
561,4,'7440-50-8',0,'copper',561,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
562,4,'7439-96-5',0,'manganese',562,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
563,4,'7440-02-0',0,'nickel',563,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
564,4,'7782-49-2',0,'selenium',564,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
565,4,'7440-62-2',0,'vanadium',565,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
566,4,'7440-66-6',0,'zinc',566,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
567,4,'71-55-6',0,'1,1,1-trichloroethane',567,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
568,4,'7439-97-6',0,'mercury',568,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
570,4,'60-27-5',0,'creatinine',570,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
571,4,'7429-90-5',0,'aluminium',571,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
572,4,'7440-70-2',0,'calcium',572,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
573,4,'7439-95-4',0,'magnesium',573,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
574,4,'7723-14-0',0,'phosphorus',574,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
575,4,'7440-24-6',0,'strontium',575,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
576,4,'7439-89-6',0,'iron',576,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
577,4,'7440-09-7',0,'potassium',577,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
578,4,'7440-23-5',0,'sodium',578,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
579,4,'58-89-9',0,'lindane',579,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
580,4,'52645-53-1',0,'permenthrine',580,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
581,4,'107-13-1',0,'acrylonitrile',581,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
582,4,'79-06-1',0,'acrylamide',582,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
589,4,'611-14-3',0,'1-ethyl 2methyl benzene',589,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
592,4,'109-66-0',0,'n-pentane',592,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
593,4,'7785-26-4',0,'alpha-pinene',593,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
594,4,'5989-27-5',0,'d-limonene',594,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
596,4,'106-99-0',0,'butadiene',596,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
597,4,'74-84-0',0,'ethane',597,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
598,4,'74-85-1',0,'ethylene',598,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
599,4,'74-86-2',0,'acetylene',599,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
600,4,'107-06-2',0,'1,2-dichloroethane',600,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
601,4,'106-42-3',0,'p-xylene',601,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
603,4,'98-82-8',0,'isopropylbenzene',603,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
604,4,'110-86-1',0,'pyridine',604,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
606,4,'109-06-8',0,'2-picoline',606,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
608,4,'108-99-6',0,'3-picoline',608,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
609,4,'108-89-4',0,'4-picoline',609,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
610,4,'104-51-8',0,'n-butylbenzene',610,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
611,4,'536-78-7',0,'3-ethylpyridine',611,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
613,4,'25551-13-7',0,'trimethylbenzene',613,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
618,4,'1336-36-3',0,'PCBs',618,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
619,4,'3547-04-4',0,'DDE',619,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
620,4,'118-74-1',0,'HCB',620,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
621,4,'5315-79-7',0,'1-hydroxypyrene',621,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
623,4,'1330-20-7',0,'xylenes',623,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
624,4,'37210-16-5',0,'CO2',624,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
625,4,'630-08-0',0,'CO',625,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
626,4,'54-11-5',0,'nicotine',626,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
628,4,'3588-17-8',0,'trans,trans-Muconic acid',628,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
629,4,'50-32-8',0,'benzo(a)pyrene',629,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
631,4,'590-86-3',0,'isovaleraldehyde',631,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
632,4,'123-38-6',0,'propionaldehyde',632,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
633,4,'123-72-8',0,'n-butyraldehyde',633,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
634,4,'75-07-0',0,'acetaldehyde',634,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
636,4,'50-00-0',0,'formaldehyde',636,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
637,4,'110-62-3',0,'valeraldehyde',637,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
638,4,'4170-30-3',0,'crotonaldehyde',638,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
639,22,'n',0,'Number of observations',639,'Op_en1910','Total mortality in the Western Europe','cases/a',1,1910,1,
640,22,'n_lt_LOQ',0,'Number of observations below level of quantitation',640,'Op_en1910','Total mortality in the Western Europe','cases/a',1,1910,1,
641,22,'F0.10',0,'Fractile 0.1',641,'Op_en1910','Total mortality in the Western Europe','cases/a',1,1910,1,
642,22,'F0.50',0,'Fractile 0.5',642,'Op_en1910','Total mortality in the Western Europe','cases/a',1,1910,1,
643,22,'F0.90',0,'Fractile 0.9',643,'Op_en1910','Total mortality in the Western Europe','cases/a',1,1910,1,
644,22,'F0.95',0,'Fractile 0.95',644,'Op_en1910','Total mortality in the Western Europe','cases/a',1,1910,1,
645,22,'Mean',0,'Arithmetic mean',645,'Op_en1910','Total mortality in the Western Europe','cases/a',1,1910,1,
646,22,'GeoMean',0,'Geometric mean',646,'Op_en1910','Total mortality in the Western Europe','cases/a',1,1910,1,
647,5,'ang',0,'Anglian Water ',647,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
648,5,'bou',0,'Bristol Water ',648,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
649,5,'brw',0,'Bournemouth & West hants ',649,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
650,5,'caw',0,'Cambridge Water ',650,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
651,5,'cho',0,'Cholderton Water ',651,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
652,5,'dcc',0,'Dee Valley Water ',652,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
653,5,'eas',0,'Welsh Water ',653,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
654,5,'ess',0,'Essex and Suffolk Water ',654,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
655,5,'fol',0,'Folkestone & Dover Water ',655,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
656,5,'har',0,'Hartlepool Water ',656,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
657,5,'mik',0,'Mid Kent Water ',657,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
658,5,'nor',0,'Northumbrian Water ',658,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
659,5,'nww',0,'Portsmouth Water ',659,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
660,5,'por',0,'Sutton & East Surrey Water ',660,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
661,5,'sea',0,'South East Water ',661,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
662,5,'sev',0,'Southern Water ',662,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
663,5,'sos',0,'South Staffordshire Water ',663,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
664,5,'sou',0,'Severn Trent Water ',664,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
665,5,'sww',0,'South West Water ',665,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
666,5,'teh',0,'Tendring Hundred Water ',666,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
667,5,'tha',0,'Thames Water ',667,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
668,5,'thr',0,'Three Valleys Water ',668,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
669,5,'wes',0,'United Utilties (North West Water) ',669,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
670,5,'wrx',0,'Wessex Water ',670,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
671,5,'yor',0,'Yorkshire Water',671,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
672,25,'BAU',0,'0.00000000000000',672,'Op_en1898','Recommendation for consumption of farmed salmon','-',1,1898,1,
673,25,'Restrict farmed salmon use',0,'0.00000000000000',673,'Op_en1898','Recommendation for consumption of farmed salmon','-',1,1898,1,
674,26,'BAU',0,'0.00000000000000',674,'Op_en1899','Pollutant concentration limits for fish feed','-',1,1899,1,
675,26,'More actions',0,'0.00000000000000',675,'Op_en1899','Pollutant concentration limits for fish feed','-',1,1899,1,
676,130,'Dieldrin',0,'0.00000000000000',676,'Op_en2705','Pollutant','-',6,2705,1,
677,130,'Toxaphene',0,'0.00000000000000',677,'Op_en2705','Pollutant','-',6,2705,1,
678,130,'Dioxin',0,'0.00000000000000',678,'Op_en2705','Pollutant','-',6,2705,1,
679,130,'PCB',0,'0.00000000000000',679,'Op_en2705','Pollutant','-',6,2705,1,
680,131,'Farmed salmon',0,'0.00000000000000',680,'Op_en2706','Salmon type','-',6,2706,1,
681,131,'Wild salmon',0,'0.00000000000000',681,'Op_en2706','Salmon type','-',6,2706,1,
682,131,'Market salmon',0,'0.00000000000000',682,'Op_en2706','Salmon type','-',6,2706,1,
685,133,'Cardiovascular',0,'0.00000000000000',685,'Op_en2707','Cause of death3','ICD-10',6,2707,1,
688,135,'2000',0,'0.00000000000000',688,'Op_en2708','Year3','year',6,2708,1,
689,185,'Male',0,'0.00000000000000',689,'Op_en2780','Sex','-',6,2780,1,
690,185,'Female',0,'0.00000000000000',690,'Op_en2780','Sex','-',6,2780,1,
691,186,'All causes',0,'AAA',691,'Op_en2779','Diagnosis1','-',6,2779,1,
692,186,'Infectious and parasitic diseases',0,'A00-B99',692,'Op_en2779','Diagnosis1','-',6,2779,1,
693,186,'Typhoid and paratyphoid fever',0,'A01',693,'Op_en2779','Diagnosis1','-',6,2779,1,
694,186,'Other intestinal infectious diseases',0,'A00, A02-A09',694,'Op_en2779','Diagnosis1','-',6,2779,1,
695,186,'Tuberculosis of respiratory system',0,'A15-A16',695,'Op_en2779','Diagnosis1','-',6,2779,1,
696,186,'Tuberculosis, other forms',0,'A17-A19',696,'Op_en2779','Diagnosis1','-',6,2779,1,
697,186,'Whooping cough',0,'A37',697,'Op_en2779','Diagnosis1','-',6,2779,1,
698,186,'Meningococcal infection',0,'A39',698,'Op_en2779','Diagnosis1','-',6,2779,1,
699,186,'Tetanus',0,'A35',699,'Op_en2779','Diagnosis1','-',6,2779,1,
700,186,'Septicaemia',0,'A40-A41',700,'Op_en2779','Diagnosis1','-',6,2779,1,
701,186,'Other bacterial diseases',0,'A20-A32, A36, A38, A42-49',701,'Op_en2779','Diagnosis1','-',6,2779,1,
702,186,'Measles',0,'B05',702,'Op_en2779','Diagnosis1','-',6,2779,1,
703,186,'HIV disease',0,'B20-B24',703,'Op_en2779','Diagnosis1','-',6,2779,1,
704,186,'Other viral diseases',0,'A70-A74, A80-B34, B05, B20-B24',704,'Op_en2779','Diagnosis1','-',6,2779,1,
705,186,'Malaria',0,'B50-B54',705,'Op_en2779','Diagnosis1','-',6,2779,1,
706,186,'Other arthropod-borne diseases',0,'A75-A79, B55-B57, B60, B64',706,'Op_en2779','Diagnosis1','-',6,2779,1,
707,186,'Sexually transmitted diseases',0,'A50-A64',707,'Op_en2779','Diagnosis1','-',6,2779,1,
708,186,'Other infectious and parasitic diseases',0,'A65-A69, B35-B49, B58, B59, B65-B99',708,'Op_en2779','Diagnosis1','-',6,2779,1,
709,186,'Malignant neoplasms',0,'C00-C97',709,'Op_en2779','Diagnosis1','-',6,2779,1,
710,186,'Malignant neoplasm of lip, oral cavity and pharynx',0,'C00-C14',710,'Op_en2779','Diagnosis1','-',6,2779,1,
711,186,'Malignant neoplasm of oesophagus',0,'C15',711,'Op_en2779','Diagnosis1','-',6,2779,1,
712,186,'Malignant neoplasm of stomach',0,'C16',712,'Op_en2779','Diagnosis1','-',6,2779,1,
713,186,'Malignant neoplasm of colon',0,'C18',713,'Op_en2779','Diagnosis1','-',6,2779,1,
714,186,'Malignant neoplasm of rectum, rectosigmoid junction and anus',0,'C19-C21',714,'Op_en2779','Diagnosis1','-',6,2779,1,
715,186,'Malignant neoplasm of liver',0,'C22',715,'Op_en2779','Diagnosis1','-',6,2779,1,
716,186,'Malignant neoplasm of larynx',0,'C32',716,'Op_en2779','Diagnosis1','-',6,2779,1,
717,186,'Malignant neoplasm of trachea, bronchus and lung',0,'C33-C34',717,'Op_en2779','Diagnosis1','-',6,2779,1,
718,186,'Malignant neoplasm of breast',0,'C50',718,'Op_en2779','Diagnosis1','-',6,2779,1,
719,186,'Malignant neoplasm of cervix uteri',0,'C53',719,'Op_en2779','Diagnosis1','-',6,2779,1,
720,186,'Malignant neoplasm of uterus, other and unspecified',0,'C54-C55',720,'Op_en2779','Diagnosis1','-',6,2779,1,
721,186,'Malignant neoplasm of prostate',0,'C61',721,'Op_en2779','Diagnosis1','-',6,2779,1,
722,186,'Malignant neoplasm of bladder',0,'C67',722,'Op_en2779','Diagnosis1','-',6,2779,1,
723,186,'Malignant neoplasm of other sites',0,'C17, C23-C31, C37-C49, C51, C52, C56-C60, C62-C66, C68-C80, C97',723,'Op_en2779','Diagnosis1','-',6,2779,1,
724,186,'Leukaemia',0,'C91-C95',724,'Op_en2779','Diagnosis1','-',6,2779,1,
725,186,'Other malignant neoplasms of lymphoid and haematopoietic and related tissue',0,'C81-C90, C96',725,'Op_en2779','Diagnosis1','-',6,2779,1,
726,186,'Benign neoplasm, other and unspecified neoplasm',0,'D00-D48',726,'Op_en2779','Diagnosis1','-',6,2779,1,
727,186,'Diabetes mellitus',0,'E10-E14',727,'Op_en2779','Diagnosis1','-',6,2779,1,
728,186,'Other endocrine and metabolic diseases',0,'E00-E07, E15-E34, E65-E68, E70-E88',728,'Op_en2779','Diagnosis1','-',6,2779,1,
729,186,'Malnutrition',0,'E41-E46',729,'Op_en2779','Diagnosis1','-',6,2779,1,
730,186,'Other nutritional deficiencies',0,'E40, E50-E64',730,'Op_en2779','Diagnosis1','-',6,2779,1,
731,186,'Anaemias',0,'D50-D64',731,'Op_en2779','Diagnosis1','-',6,2779,1,
732,186,'Other diseases of blood and blood-forming organs',0,'D65-D89',732,'Op_en2779','Diagnosis1','-',6,2779,1,
733,186,'Mental disorders',0,'F01-F99',733,'Op_en2779','Diagnosis1','-',6,2779,1,
734,186,'Meningitis',0,'G00, G03',734,'Op_en2779','Diagnosis1','-',6,2779,1,
735,186,'Multiple sclerosis',0,'G35',735,'Op_en2779','Diagnosis1','-',6,2779,1,
736,186,'Epilepsy',0,'G40-G41',736,'Op_en2779','Diagnosis1','-',6,2779,1,
737,186,'Other diseases of the nervous system and sense organs',0,'G04-G31, G36-G37, G43-H95',737,'Op_en2779','Diagnosis1','-',6,2779,1,
738,186,'Diseases of the circulatory system',0,'I00-I99',738,'Op_en2779','Diagnosis1','-',6,2779,1,
739,186,'Acute rheumatic fever',0,'I00-I02',739,'Op_en2779','Diagnosis1','-',6,2779,1,
740,186,'Chronic rheumatic heart disease',0,'I05-I09',740,'Op_en2779','Diagnosis1','-',6,2779,1,
741,186,'Hypertensive disease',0,'I10-I13',741,'Op_en2779','Diagnosis1','-',6,2779,1,
742,186,'Acute myocardial infarction',0,'I21, I22',742,'Op_en2779','Diagnosis1','-',6,2779,1,
743,186,'Other ischaemic heart diseases',0,'I20, I24, I25',743,'Op_en2779','Diagnosis1','-',6,2779,1,
744,186,'Diseases of pulmonary circulation and other forms of heart disease',0,'I26-I51',744,'Op_en2779','Diagnosis1','-',6,2779,1,
745,186,'Cerebrovascular disease',0,'I60-I69',745,'Op_en2779','Diagnosis1','-',6,2779,1,
746,186,'Atherosclerosis',0,'I70',746,'Op_en2779','Diagnosis1','-',6,2779,1,
747,186,'Embolism, thrombosis and other diseases of arteries, arterioles and capillaries',0,'I71-I78',747,'Op_en2779','Diagnosis1','-',6,2779,1,
748,186,'Phlebitis, thrombophlebitis, venous embolism and thrombosis',0,'I80-I82',748,'Op_en2779','Diagnosis1','-',6,2779,1,
749,186,'Other diseases of the circulatory system',0,'I83-I99',749,'Op_en2779','Diagnosis1','-',6,2779,1,
750,186,'Acute upper respiratory infection',0,'J00-J06',750,'Op_en2779','Diagnosis1','-',6,2779,1,
751,186,'Acute bronchitis and bronchiolitis',0,'J20-J21',751,'Op_en2779','Diagnosis1','-',6,2779,1,
752,186,'Pneumonia',0,'J12-J18',752,'Op_en2779','Diagnosis1','-',6,2779,1,
753,186,'Influenza',0,'J10-J11',753,'Op_en2779','Diagnosis1','-',6,2779,1,
754,186,'Bronchitis, chronic and unspecified, emphysema and asthma',0,'J40-J46',754,'Op_en2779','Diagnosis1','-',6,2779,1,
755,186,'Other diseases of the respiratory system',0,'J22, J30-J39, J47-J98',755,'Op_en2779','Diagnosis1','-',6,2779,1,
756,186,'Ulcer of stomach and duodenum',0,'K25-K27',756,'Op_en2779','Diagnosis1','-',6,2779,1,
757,186,'Appendicitis',0,'K35-K38',757,'Op_en2779','Diagnosis1','-',6,2779,1,
758,186,'Hernia of abdominal cavity and intestinal obstruction',0,'K40-K46,K56',758,'Op_en2779','Diagnosis1','-',6,2779,1,
759,186,'Chronic liver disease and cirrhosis',0,'K70,K73-K74,K76',759,'Op_en2779','Diagnosis1','-',6,2779,1,
760,186,'Other diseases of the digestive system',0,'K00-K22, K28-K31, K50-K55, K57-K66, K71, K72, K75, K80-K92',760,'Op_en2779','Diagnosis1','-',6,2779,1,
761,186,'Nephritis, nephrotic syndrome and nephrosis',0,'N00-N07, N13-N19',761,'Op_en2779','Diagnosis1','-',6,2779,1,
762,186,'Infections of kidney',0,'N10-N12',762,'Op_en2779','Diagnosis1','-',6,2779,1,
763,186,'Hyperplasia of prostate',0,'N40',763,'Op_en2779','Diagnosis1','-',6,2779,1,
764,186,'Other diseases of the genitourinary system',0,'N20-N39, N41-N98',764,'Op_en2779','Diagnosis1','-',6,2779,1,
765,186,'Abortion',0,'O00-O07',765,'Op_en2779','Diagnosis1','-',6,2779,1,
766,186,'Haemorrhage of pregnancy and childbirth',0,'O20, O46, O67, O72',766,'Op_en2779','Diagnosis1','-',6,2779,1,
767,186,'Toxaemia of pregnancy',0,'O13-O16, O21',767,'Op_en2779','Diagnosis1','-',6,2779,1,
768,186,'Complications of the puerperium',0,'O85-O92, A34',768,'Op_en2779','Diagnosis1','-',6,2779,1,
769,186,'Other direct obstetric causes',0,'O10-O12, O22-O75, O95-O97',769,'Op_en2779','Diagnosis1','-',6,2779,1,
770,186,'Indirect obstetric causes',0,'O98-O99',770,'Op_en2779','Diagnosis1','-',6,2779,1,
771,186,'Diseases of skin and subcutaneous tissue',0,'L00-L98',771,'Op_en2779','Diagnosis1','-',6,2779,1,
772,186,'Diseases of the musculoskeletal system and connective tissue',0,'M00-M99',772,'Op_en2779','Diagnosis1','-',6,2779,1,
773,186,'Spina bifida and hydrocephalus',0,'Q03,Q05',773,'Op_en2779','Diagnosis1','-',6,2779,1,
774,186,'Congenital anomalies of the circulatory system',0,'Q20-Q28',774,'Op_en2779','Diagnosis1','-',6,2779,1,
775,186,'Other congenital anomalies',0,'Q00-Q02, Q04, Q06-Q18, Q30-Q99',775,'Op_en2779','Diagnosis1','-',6,2779,1,
776,186,'Birth trauma',0,'P10-P15',776,'Op_en2779','Diagnosis1','-',6,2779,1,
777,186,'Other conditions originating in the perinatal period',0,'P00-P08, P20-P96, A33',777,'Op_en2779','Diagnosis1','-',6,2779,1,
778,186,'Senility',0,'R54',778,'Op_en2779','Diagnosis1','-',6,2779,1,
779,186,'Signs, symptoms and other ill-defined conditions',0,'R00-R53, R55-R99',779,'Op_en2779','Diagnosis1','-',6,2779,1,
780,186,'Accidents and adverse effects',0,'V01-X59, Y40-Y86, Y88',780,'Op_en2779','Diagnosis1','-',6,2779,1,
781,186,'Motor vehicle traffic accidents',0,'V02-V04, V09, V12-V14, V19-V79, V86-V89',781,'Op_en2779','Diagnosis1','-',6,2779,1,
782,186,'Other transport accidents',0,'V01, V05-V06, V10, V11, V15-V18, V80-V85, V90-V99',782,'Op_en2779','Diagnosis1','-',6,2779,1,
783,186,'Accidental poisoning',0,'X40-X49',783,'Op_en2779','Diagnosis1','-',6,2779,1,
784,186,'Accidental falls',0,'W00-W19',784,'Op_en2779','Diagnosis1','-',6,2779,1,
785,186,'Accidents caused by fire and flames',0,'X00-X09',785,'Op_en2779','Diagnosis1','-',6,2779,1,
786,186,'Accidental drowning and submersion',0,'W65-W74',786,'Op_en2779','Diagnosis1','-',6,2779,1,
787,186,'Accidents caused by machinery and by cutting and piercing instruments',0,'W24-W31',787,'Op_en2779','Diagnosis1','-',6,2779,1,
788,186,'Accidents caused by firearm missile',0,'W32-W34',788,'Op_en2779','Diagnosis1','-',6,2779,1,
789,186,'All other accidents, including late effects',0,'W20-W23, W35-W64, W75-W99, X10-X39, X50-X59, Y85, Y86',789,'Op_en2779','Diagnosis1','-',6,2779,1,
790,186,'Drugs, medicaments causing adverse effects in therapeutic use',0,'Y40-Y84, Y88',790,'Op_en2779','Diagnosis1','-',6,2779,1,
791,186,'Suicide and self- inflicted injury',0,'X60-X84',791,'Op_en2779','Diagnosis1','-',6,2779,1,
792,186,'Homicide and injury purposely inflicted by other persons',0,'X85-Y09',792,'Op_en2779','Diagnosis1','-',6,2779,1,
793,186,'Other external causes',0,'Y10-Y36, Y87, Y89',793,'Op_en2779','Diagnosis1','-',6,2779,1,
794,187,'Number',0,'0.00000000000000',794,'Op_en2784','Units1','-',6,2784,1,
795,187,'Number/100000 person-years',0,'0.00000000000000',795,'Op_en2784','Units1','-',6,2784,1,
796,188,'All ages',0,'0.00000000000000',796,'Op_en2781','Age group1','a',6,2781,1,
797,188,'< 1',0,'0.00000000000000',797,'Op_en2781','Age group1','a',6,2781,1,
798,188,'1-4',0,'0.00000000000000',798,'Op_en2781','Age group1','a',6,2781,1,
799,188,'5-14',0,'0.00000000000000',799,'Op_en2781','Age group1','a',6,2781,1,
800,188,'15-24',0,'0.00000000000000',800,'Op_en2781','Age group1','a',6,2781,1,
801,188,'25-34',0,'0.00000000000000',801,'Op_en2781','Age group1','a',6,2781,1,
802,188,'35-44',0,'0.00000000000000',802,'Op_en2781','Age group1','a',6,2781,1,
803,188,'45-54',0,'0.00000000000000',803,'Op_en2781','Age group1','a',6,2781,1,
804,188,'55-64',0,'0.00000000000000',804,'Op_en2781','Age group1','a',6,2781,1,
805,188,'65-74',0,'0.00000000000000',805,'Op_en2781','Age group1','a',6,2781,1,
806,188,'75+',0,'0.00000000000000',806,'Op_en2781','Age group1','a',6,2781,1,
807,188,'Age not specified',0,'0.00000000000000',807,'Op_en2781','Age group1','a',6,2781,1,
808,189,'Finland',0,'0.00000000000000',808,'Country1','Country1','-',6,2785,1,
809,193,'All',0,'0.00000000000000',809,'Age2','Age2','a',6,2812,1,
810,193,'0-64',0,'0.00000000000000',810,'Age2','Age2','a',6,2812,1,
811,193,'64+',0,'0.00000000000000',811,'Age2','Age2','a',6,2812,1,
812,194,'Austria',0,'0.00000000000000',812,'Country2','Country2','-',6,2813,1,
813,194,'Belgium',0,'0.00000000000000',813,'Country2','Country2','-',6,2813,1,
814,194,'Bulgaria',0,'0.00000000000000',814,'Country2','Country2','-',6,2813,1,
815,194,'Cyprus',0,'0.00000000000000',815,'Country2','Country2','-',6,2813,1,
816,194,'Czech Republic',0,'0.00000000000000',816,'Country2','Country2','-',6,2813,1,
817,194,'Denmark',0,'0.00000000000000',817,'Country2','Country2','-',6,2813,1,
818,194,'Estonia',0,'0.00000000000000',818,'Country2','Country2','-',6,2813,1,
819,194,'Finland',0,'0.00000000000000',819,'Country2','Country2','-',6,2813,1,
820,194,'France',0,'0.00000000000000',820,'Country2','Country2','-',6,2813,1,
821,194,'Germany',0,'0.00000000000000',821,'Country2','Country2','-',6,2813,1,
822,194,'Greece',0,'0.00000000000000',822,'Country2','Country2','-',6,2813,1,
823,194,'Hungary',0,'0.00000000000000',823,'Country2','Country2','-',6,2813,1,
824,194,'Ireland',0,'0.00000000000000',824,'Country2','Country2','-',6,2813,1,
825,194,'Italy',0,'0.00000000000000',825,'Country2','Country2','-',6,2813,1,
826,194,'Latvia',0,'0.00000000000000',826,'Country2','Country2','-',6,2813,1,
827,194,'Luxembourg',0,'0.00000000000000',827,'Country2','Country2','-',6,2813,1,
828,194,'Malta',0,'0.00000000000000',828,'Country2','Country2','-',6,2813,1,
829,194,'Netherlands',0,'0.00000000000000',829,'Country2','Country2','-',6,2813,1,
830,194,'Poland',0,'0.00000000000000',830,'Country2','Country2','-',6,2813,1,
831,194,'Portugal',0,'0.00000000000000',831,'Country2','Country2','-',6,2813,1,
832,194,'Romania',0,'0.00000000000000',832,'Country2','Country2','-',6,2813,1,
833,194,'Slovakia',0,'0.00000000000000',833,'Country2','Country2','-',6,2813,1,
834,194,'Slovenia',0,'0.00000000000000',834,'Country2','Country2','-',6,2813,1,
835,194,'Spain',0,'0.00000000000000',835,'Country2','Country2','-',6,2813,1,
836,194,'Sweden',0,'0.00000000000000',836,'Country2','Country2','-',6,2813,1,
837,194,'United Kingdom',0,'0.00000000000000',837,'Country2','Country2','-',6,2813,1,
838,194,'EU ',0,'0.00000000000000',838,'Country2','Country2','-',6,2813,1,
839,195,'1970',0,'0.00000000000000',839,'Year2','Year2','a',6,2814,1,
840,195,'1971',0,'0.00000000000000',840,'Year2','Year2','a',6,2814,1,
841,195,'1972',0,'0.00000000000000',841,'Year2','Year2','a',6,2814,1,
842,195,'1973',0,'0.00000000000000',842,'Year2','Year2','a',6,2814,1,
843,195,'1974',0,'0.00000000000000',843,'Year2','Year2','a',6,2814,1,
844,195,'1975',0,'0.00000000000000',844,'Year2','Year2','a',6,2814,1,
845,195,'1976',0,'0.00000000000000',845,'Year2','Year2','a',6,2814,1,
846,195,'1977',0,'0.00000000000000',846,'Year2','Year2','a',6,2814,1,
847,195,'1978',0,'0.00000000000000',847,'Year2','Year2','a',6,2814,1,
848,195,'1979',0,'0.00000000000000',848,'Year2','Year2','a',6,2814,1,
849,195,'1980',0,'0.00000000000000',849,'Year2','Year2','a',6,2814,1,
850,195,'1981',0,'0.00000000000000',850,'Year2','Year2','a',6,2814,1,
851,195,'1982',0,'0.00000000000000',851,'Year2','Year2','a',6,2814,1,
852,195,'1983',0,'0.00000000000000',852,'Year2','Year2','a',6,2814,1,
853,195,'1984',0,'0.00000000000000',853,'Year2','Year2','a',6,2814,1,
854,195,'1985',0,'0.00000000000000',854,'Year2','Year2','a',6,2814,1,
855,195,'1986',0,'0.00000000000000',855,'Year2','Year2','a',6,2814,1,
856,195,'1987',0,'0.00000000000000',856,'Year2','Year2','a',6,2814,1,
857,195,'1988',0,'0.00000000000000',857,'Year2','Year2','a',6,2814,1,
858,195,'1989',0,'0.00000000000000',858,'Year2','Year2','a',6,2814,1,
859,195,'1990',0,'0.00000000000000',859,'Year2','Year2','a',6,2814,1,
860,195,'1991',0,'0.00000000000000',860,'Year2','Year2','a',6,2814,1,
861,195,'1992',0,'0.00000000000000',861,'Year2','Year2','a',6,2814,1,
862,195,'1993',0,'0.00000000000000',862,'Year2','Year2','a',6,2814,1,
863,195,'1994',0,'0.00000000000000',863,'Year2','Year2','a',6,2814,1,
864,195,'1995',0,'0.00000000000000',864,'Year2','Year2','a',6,2814,1,
865,195,'1996',0,'0.00000000000000',865,'Year2','Year2','a',6,2814,1,
866,195,'1997',0,'0.00000000000000',866,'Year2','Year2','a',6,2814,1,
867,195,'1998',0,'0.00000000000000',867,'Year2','Year2','a',6,2814,1,
868,195,'1999',0,'0.00000000000000',868,'Year2','Year2','a',6,2814,1,
869,195,'2000',0,'0.00000000000000',869,'Year2','Year2','a',6,2814,1,
870,195,'2001',0,'0.00000000000000',870,'Year2','Year2','a',6,2814,1,
871,195,'2002',0,'0.00000000000000',871,'Year2','Year2','a',6,2814,1,
872,195,'2003',0,'0.00000000000000',872,'Year2','Year2','a',6,2814,1,
873,195,'2004',0,'0.00000000000000',873,'Year2','Year2','a',6,2814,1,
874,195,'2005',0,'0.00000000000000',874,'Year2','Year2','a',6,2814,1,
875,195,'2006',0,'0.00000000000000',875,'Year2','Year2','a',6,2814,1,
876,195,'2007',0,'0.00000000000000',876,'Year2','Year2','a',6,2814,1,
877,196,'Male',0,'0.00000000000000',877,'Sex2','Sex2','-',6,2815,1,
878,196,'Female',0,'0.00000000000000',878,'Sex2','Sex2','-',6,2815,1,
879,196,'All',0,'0.00000000000000',879,'Sex2','Sex2','-',6,2815,1,
883,207,'All causes',0,'-’',883,'Diagnosis2','Diagnosis2','ICD-10',6,2835,1,
884,207,'Infectious and parasitic diseases',0,'A00-B99’',884,'Diagnosis2','Diagnosis2','ICD-10',6,2835,1,
885,207,'Typhoid and paratyphoid fever',0,'A01’',885,'Diagnosis2','Diagnosis2','ICD-10',6,2835,1,
919,36,'ncd',1,'0.00000000000000',919,'Pollutant','Pollutant','-',2,2493,1,
920,36,'o31',2,'0.00000000000000',920,'Pollutant','Pollutant','-',2,2493,1,
921,36,'p10',3,'0.00000000000000',921,'Pollutant','Pollutant','-',2,2493,1,
922,36,'p25',4,'0.00000000000000',922,'Pollutant','Pollutant','-',2,2493,1,
923,36,'s10',5,'0.00000000000000',923,'Pollutant','Pollutant','-',2,2493,1,
924,36,'s25',6,'0.00000000000000',924,'Pollutant','Pollutant','-',2,2493,1,
925,36,'som',7,'0.00000000000000',925,'Pollutant','Pollutant','-',2,2493,1,
933,277,'AD',1,'0.00000000000000',933,'CountryID','Country identifier','-',6,2664,1,
934,278,'crops',1,'0.00000000000000',934,'Receptor','Receptor of the impact','-',6,2664,1,
935,278,'human',2,'0.00000000000000',935,'Receptor','Receptor of the impact','-',6,2664,1,
936,279,'total',1,'0.00000000000000',936,'ReceptorSubGroup','Receptor subgroup','-',6,2664,1,
937,279,'potato',2,'0.00000000000000',937,'ReceptorSubGroup','Receptor subgroup','-',6,2664,1,
938,279,'rice',3,'0.00000000000000',938,'ReceptorSubGroup','Receptor subgroup','-',6,2664,1,
939,279,'sugar beet',4,'0.00000000000000',939,'ReceptorSubGroup','Receptor subgroup','-',6,2664,1,
940,279,'sunflower seed',5,'0.00000000000000',940,'ReceptorSubGroup','Receptor subgroup','-',6,2664,1,
941,279,'tobacco',6,'0.00000000000000',941,'ReceptorSubGroup','Receptor subgroup','-',6,2664,1,
942,279,'wheat',7,'0.00000000000000',942,'ReceptorSubGroup','Receptor subgroup','-',6,2664,1,
943,279,'adults_20',8,'0.00000000000000',943,'ReceptorSubGroup','Receptor subgroup','-',6,2664,1,
944,279,'adults_27',9,'0.00000000000000',944,'ReceptorSubGroup','Receptor subgroup','-',6,2664,1,
945,279,'adults_ab15',10,'0.00000000000000',945,'ReceptorSubGroup','Receptor subgroup','-',6,2664,1,
946,279,'children_5_14',11,'0.00000000000000',946,'ReceptorSubGroup','Receptor subgroup','-',6,2664,1,
947,279,'adults_15_64',12,'0.00000000000000',947,'ReceptorSubGroup','Receptor subgroup','-',6,2664,1,
948,279,'adults_30',13,'0.00000000000000',948,'ReceptorSubGroup','Receptor subgroup','-',6,2664,1,
949,279,'infants',14,'0.00000000000000',949,'ReceptorSubGroup','Receptor subgroup','-',6,2664,1,
950,279,'adults_18_64',15,'0.00000000000000',950,'ReceptorSubGroup','Receptor subgroup','-',6,2664,1,
951,279,'adults_65',16,'0.00000000000000',951,'ReceptorSubGroup','Receptor subgroup','-',6,2664,1,
952,280,'add. fertil. needed [kg]',1,'0.00000000000000',952,'Impact','Impact in the receptor','-',6,2664,1,
953,280,'yield loss [dt]',2,'0.00000000000000',953,'Impact','Impact in the receptor','-',6,2664,1,
954,280,'Bronchodilator usage',3,'0.00000000000000',954,'Impact','Impact in the receptor','-',6,2664,1,
955,280,'chronic bronchitis',4,'0.00000000000000',955,'Impact','Impact in the receptor','-',6,2664,1,
956,280,'Lower resp. symptoms',5,'0.00000000000000',956,'Impact','Impact in the receptor','-',6,2664,1,
957,280,'Cardiac hosp.admissions',6,'0.00000000000000',957,'Impact','Impact in the receptor','-',6,2664,1,
958,280,'resp. hosp. admission',7,'0.00000000000000',958,'Impact','Impact in the receptor','-',6,2664,1,
959,280,'Restr. activity days',8,'0.00000000000000',959,'Impact','Impact in the receptor','-',6,2664,1,
960,280,'Work loss days',9,'0.00000000000000',960,'Impact','Impact in the receptor','-',6,2664,1,
961,280,'chronic’ YOLL',10,'0.00000000000000',961,'Impact','Impact in the receptor','-',6,2664,1,
962,280,'IncreasedInfantMort',11,'0.00000000000000',962,'Impact','Impact in the receptor','-',6,2664,1,
963,280,'Minor RAD',12,'0.00000000000000',963,'Impact','Impact in the receptor','-',6,2664,1,
964,280,'LRSwo cough',13,'0.00000000000000',964,'Impact','Impact in the receptor','-',6,2664,1,
965,280,'acute’ YOLL',14,'0.00000000000000',965,'Impact','Impact in the receptor','-',6,2664,1,
973,282,'Hornung, 1997',1,'0.00000000000000',973,'FunctionReference','Reference for the impact function','-',6,2664,1,
974,282,'Mills et al. 2003',2,'0.00000000000000',974,'FunctionReference','Reference for the impact function','-',6,2664,1,
975,282,'NEEDS_PPM10',3,'0.00000000000000',975,'FunctionReference','Reference for the impact function','-',6,2664,1,
976,282,'NEEDS_PPM25',4,'0.00000000000000',976,'FunctionReference','Reference for the impact function','-',6,2664,1,
977,282,'NEEDS_SIA10',5,'0.00000000000000',977,'FunctionReference','Reference for the impact function','-',6,2664,1,
978,282,'NEEDS_SIA25',6,'0.00000000000000',978,'FunctionReference','Reference for the impact function','-',6,2664,1,
979,282,'NEEDS_SOMO35',7,'0.00000000000000',979,'FunctionReference','Reference for the impact function','-',6,2664,1,
980,301,'Kiikoinen',1,'',980,'Municipality','Municipality in Finland','-',6,2664,1,
981,301,'Lavia',2,'',981,'Municipality','Municipality in Finland','-',6,2664,1,
982,301,'Mouhijärvi',3,'',982,'Municipality','Municipality in Finland','-',6,2664,1,
983,301,'Suodenniemi',4,'',983,'Municipality','Municipality in Finland','-',6,2664,1,
984,301,'Vammala',5,'',984,'Municipality','Municipality in Finland','-',6,2664,1,
985,301,'Äetsä',6,'',985,'Municipality','Municipality in Finland','-',6,2664,1,
986,302,'Kuorsumaanjärvi',1,'',986,'Lake','Lake in Finland','-',6,2664,1,
987,302,'Lavijärvi',2,'',987,'Lake','Lake in Finland','-',6,2664,1,
988,302,'Karhijärvi',3,'',988,'Lake','Lake in Finland','-',6,2664,1,
989,302,'Saarijärvi',4,'',989,'Lake','Lake in Finland','-',6,2664,1,
990,302,'Iso-Poikkelus',5,'',990,'Lake','Lake in Finland','-',6,2664,1,
991,302,'Pääjärvi',6,'',991,'Lake','Lake in Finland','-',6,2664,1,
992,302,'Kirkkojärvi',7,'',992,'Lake','Lake in Finland','-',6,2664,1,
993,302,'Kankaanjärvi',8,'',993,'Lake','Lake in Finland','-',6,2664,1,
994,302,'Valkeajärvi',9,'',994,'Lake','Lake in Finland','-',6,2664,1,
995,302,'Hurttionjärvi',10,'',995,'Lake','Lake in Finland','-',6,2664,1,
996,302,'Kulovesi',11,'',996,'Lake','Lake in Finland','-',6,2664,1,
997,302,'Rekujärvi',12,'',997,'Lake','Lake in Finland','-',6,2664,1,
998,302,'Pieni_Haukijärvi',13,'',998,'Lake','Lake in Finland','-',6,2664,1,
999,302,'Latvajärvi',14,'',999,'Lake','Lake in Finland','-',6,2664,1,
1000,302,'Murtojärvi',15,'',1000,'Lake','Lake in Finland','-',6,2664,1,
1001,302,'Miekkajärvi',16,'',1001,'Lake','Lake in Finland','-',6,2664,1,
1002,302,'Potkujärvi',17,'',1002,'Lake','Lake in Finland','-',6,2664,1,
1003,302,'Joutsijärvi',18,'',1003,'Lake','Lake in Finland','-',6,2664,1,
1004,302,'Rautavesi',19,'',1004,'Lake','Lake in Finland','-',6,2664,1,
1005,302,'Vähä-Poikelus',20,'',1005,'Lake','Lake in Finland','-',6,2664,1,
1006,302,'Iso-Lattio',21,'',1006,'Lake','Lake in Finland','-',6,2664,1,
1007,302,'Otajärvi',22,'',1007,'Lake','Lake in Finland','-',6,2664,1,
1008,302,'Ylinen/Ritajärvi',23,'',1008,'Lake','Lake in Finland','-',6,2664,1,
1009,302,'Houhajärvi',24,'',1009,'Lake','Lake in Finland','-',6,2664,1,
1010,302,'Alinen/Ritajärvi',25,'',1010,'Lake','Lake in Finland','-',6,2664,1,
1011,302,'Pitkäjärvi',26,'',1011,'Lake','Lake in Finland','-',6,2664,1,
1012,302,'Ylistenjärvi',27,'',1012,'Lake','Lake in Finland','-',6,2664,1,
1013,302,'Riippilänjärvi',28,'',1013,'Lake','Lake in Finland','-',6,2664,1,
1014,302,'Aurajärvi',29,'',1014,'Lake','Lake in Finland','-',6,2664,1,
1015,302,'Tyrisevä',30,'',1015,'Lake','Lake in Finland','-',6,2664,1,
1016,302,'Kivijärvi',31,'',1016,'Lake','Lake in Finland','-',6,2664,1,
1017,302,'Kiimajärvi',32,'',1017,'Lake','Lake in Finland','-',6,2664,1,
1018,303,'07.09.05',1,'',1018,'Date','Date of observation','date',6,2664,1,
1019,303,'13.09.05',2,'',1019,'Date','Date of observation','date',6,2664,1,
1020,303,'11.10.05',3,'',1020,'Date','Date of observation','date',6,2664,1,
1021,303,'18.07.05',4,'',1021,'Date','Date of observation','date',6,2664,1,
1022,303,'16.08.05',5,'',1022,'Date','Date of observation','date',6,2664,1,
1023,303,'06.09.05',6,'',1023,'Date','Date of observation','date',6,2664,1,
1024,303,'25.09.05',7,'',1024,'Date','Date of observation','date',6,2664,1,
1025,303,'23.08.05',8,'',1025,'Date','Date of observation','date',6,2664,1,
1026,303,'03.09.05',9,'',1026,'Date','Date of observation','date',6,2664,1,
1027,303,'04.07.05',10,'',1027,'Date','Date of observation','date',6,2664,1,
1028,303,'07.07.05',11,'',1028,'Date','Date of observation','date',6,2664,1,
1029,303,'28.07.05',12,'',1029,'Date','Date of observation','date',6,2664,1,
1030,303,'10.07.05',13,'',1030,'Date','Date of observation','date',6,2664,1,
1031,303,'11.07.05',14,'',1031,'Date','Date of observation','date',6,2664,1,
1032,303,'12.07.05',15,'',1032,'Date','Date of observation','date',6,2664,1,
1033,303,'19.07.05',16,'',1033,'Date','Date of observation','date',6,2664,1,
1034,303,'25.07.05',17,'',1034,'Date','Date of observation','date',6,2664,1,
1035,303,'27.07.05',18,'',1035,'Date','Date of observation','date',6,2664,1,
1036,303,'01.08.05',19,'',1036,'Date','Date of observation','date',6,2664,1,
1037,303,'02.08.05',20,'',1037,'Date','Date of observation','date',6,2664,1,
1038,303,'06.08.05',21,'',1038,'Date','Date of observation','date',6,2664,1,
1039,303,'09.08.05',22,'',1039,'Date','Date of observation','date',6,2664,1,
1040,303,'10.08.05',23,'',1040,'Date','Date of observation','date',6,2664,1,
1041,303,'17.08.05',24,'',1041,'Date','Date of observation','date',6,2664,1,
1042,303,'01.09.05',25,'',1042,'Date','Date of observation','date',6,2664,1,
1043,303,'27.08.05',26,'',1043,'Date','Date of observation','date',6,2664,1,
1044,303,'29.08.05',27,'',1044,'Date','Date of observation','date',6,2664,1,
1045,303,'12.09.05',28,'',1045,'Date','Date of observation','date',6,2664,1,
1046,303,'20.09.05',29,'',1046,'Date','Date of observation','date',6,2664,1,
1047,303,'28.09.05',30,'',1047,'Date','Date of observation','date',6,2664,1,
1048,303,'27.09.05',31,'',1048,'Date','Date of observation','date',6,2664,1,
1049,303,'02.10.05',32,'',1049,'Date','Date of observation','date',6,2664,1,
1050,303,'02.11.05',33,'',1050,'Date','Date of observation','date',6,2664,1,
1051,303,'03.10.05',34,'',1051,'Date','Date of observation','date',6,2664,1,
1052,303,'22.08.05',35,'',1052,'Date','Date of observation','date',6,2664,1,
1053,303,'30.08.05',36,'',1053,'Date','Date of observation','date',6,2664,1,
1054,303,'05.09.05',37,'',1054,'Date','Date of observation','date',6,2664,1,
1055,304,'Ahven',1,'',1055,'Fish','Fish species','-',6,0,5,
1056,304,'Hauki',2,'',1056,'Fish','Fish species','-',6,0,5,
1057,305,'12',1,'',1057,'Samplesize','Number of samples taken','#',6,2664,1,
1058,305,'3',2,'',1058,'Samplesize','Number of samples taken','#',6,2664,1,
1059,305,'4',3,'',1059,'Samplesize','Number of samples taken','#',6,2664,1,
1060,305,'6',4,'',1060,'Samplesize','Number of samples taken','#',6,2664,1,
1061,305,'1',5,'',1061,'Samplesize','Number of samples taken','#',6,2664,1,
1062,305,'25',6,'',1062,'Samplesize','Number of samples taken','#',6,2664,1,
1063,305,'26',7,'',1063,'Samplesize','Number of samples taken','#',6,2664,1,
1064,305,'19',8,'',1064,'Samplesize','Number of samples taken','#',6,2664,1,
1065,305,'20',9,'',1065,'Samplesize','Number of samples taken','#',6,2664,1,
1066,305,'24',10,'',1066,'Samplesize','Number of samples taken','#',6,2664,1,
1067,305,'32',11,'',1067,'Samplesize','Number of samples taken','#',6,2664,1,
1068,305,'34',12,'',1068,'Samplesize','Number of samples taken','#',6,2664,1,
1069,305,'2',13,'',1069,'Samplesize','Number of samples taken','#',6,2664,1,
1070,305,'15',14,'',1070,'Samplesize','Number of samples taken','#',6,2664,1,
1071,305,'11',15,'',1071,'Samplesize','Number of samples taken','#',6,2664,1,
1072,305,'18',16,'',1072,'Samplesize','Number of samples taken','#',6,2664,1,
1073,305,'13',17,'',1073,'Samplesize','Number of samples taken','#',6,2664,1,
1074,305,'5',18,'',1074,'Samplesize','Number of samples taken','#',6,2664,1,
1075,305,'31',19,'',1075,'Samplesize','Number of samples taken','#',6,2664,1,
1076,305,'40',20,'',1076,'Samplesize','Number of samples taken','#',6,2664,1,
1077,305,'39',21,'',1077,'Samplesize','Number of samples taken','#',6,2664,1,
1078,305,'28',22,'',1078,'Samplesize','Number of samples taken','#',6,2664,1,
1079,305,'22',23,'',1079,'Samplesize','Number of samples taken','#',6,2664,1,
1080,305,'21',24,'',1080,'Samplesize','Number of samples taken','#',6,2664,1,
1081,305,'23',25,'',1081,'Samplesize','Number of samples taken','#',6,2664,1,
1082,305,'8',26,'',1082,'Samplesize','Number of samples taken','#',6,2664,1,
1083,305,'45',27,'',1083,'Samplesize','Number of samples taken','#',6,2664,1,
1084,305,'17',28,'',1084,'Samplesize','Number of samples taken','#',6,2664,1,
1085,305,'37',29,'',1085,'Samplesize','Number of samples taken','#',6,2664,1,
1086,305,'16',30,'',1086,'Samplesize','Number of samples taken','#',6,2664,1,
1087,305,'27',31,'',1087,'Samplesize','Number of samples taken','#',6,2664,1,
1088,305,'35',32,'',1088,'Samplesize','Number of samples taken','#',6,2664,1,
1089,305,'33',33,'',1089,'Samplesize','Number of samples taken','#',6,2664,1,
1090,305,'48',34,'',1090,'Samplesize','Number of samples taken','#',6,2664,1,
1091,305,'7',35,'',1091,'Samplesize','Number of samples taken','#',6,2664,1,
1092,306,'21',1,'',1092,'Minsize','Minimum size','cm',6,2664,1,
1093,306,'31',2,'',1093,'Minsize','Minimum size','cm',6,2664,1,
1094,306,'50',3,'',1094,'Minsize','Minimum size','cm',6,2664,1,
1095,306,'25',4,'',1095,'Minsize','Minimum size','cm',6,2664,1,
1096,306,'56',5,'',1096,'Minsize','Minimum size','cm',6,2664,1,
1097,306,'10',6,'',1097,'Minsize','Minimum size','cm',6,2664,1,
1098,306,'24',7,'',1098,'Minsize','Minimum size','cm',6,2664,1,
1099,306,'14',8,'',1099,'Minsize','Minimum size','cm',6,2664,1,
1100,306,'32',9,'',1100,'Minsize','Minimum size','cm',6,2664,1,
1101,306,'11',10,'',1101,'Minsize','Minimum size','cm',6,2664,1,
1102,306,'17',11,'',1102,'Minsize','Minimum size','cm',6,2664,1,
1103,306,'12',12,'',1103,'Minsize','Minimum size','cm',6,2664,1,
1104,306,'15',13,'',1104,'Minsize','Minimum size','cm',6,2664,1,
1105,306,'9',14,'',1105,'Minsize','Minimum size','cm',6,2664,1,
1106,306,'8',15,'',1106,'Minsize','Minimum size','cm',6,2664,1,
1107,306,'165',16,'',1107,'Minsize','Minimum size','cm',6,2664,1,
1108,306,'16',17,'',1108,'Minsize','Minimum size','cm',6,2664,1,
1109,306,'37',18,'',1109,'Minsize','Minimum size','cm',6,2664,1,
1110,306,'72',19,'',1110,'Minsize','Minimum size','cm',6,2664,1,
1111,306,'19',20,'',1111,'Minsize','Minimum size','cm',6,2664,1,
1112,306,'27',21,'',1112,'Minsize','Minimum size','cm',6,2664,1,
1113,306,'13',22,'',1113,'Minsize','Minimum size','cm',6,2664,1,
1114,306,'42',23,'',1114,'Minsize','Minimum size','cm',6,2664,1,
1115,306,'7',24,'',1115,'Minsize','Minimum size','cm',6,2664,1,
1116,306,'49',25,'',1116,'Minsize','Minimum size','cm',6,2664,1,
1117,306,'44',26,'',1117,'Minsize','Minimum size','cm',6,2664,1,
1118,306,'20',27,'',1118,'Minsize','Minimum size','cm',6,2664,1,
1119,306,'46',28,'',1119,'Minsize','Minimum size','cm',6,2664,1,
1120,306,'36',29,'',1120,'Minsize','Minimum size','cm',6,2664,1,
1121,306,'39',30,'',1121,'Minsize','Minimum size','cm',6,2664,1,
1122,306,'29',31,'',1122,'Minsize','Minimum size','cm',6,2664,1,
1123,307,'30',1,'',1123,'Maxsize','Maximum size','cm',6,2664,1,
1124,307,'41',2,'',1124,'Maxsize','Maximum size','cm',6,2664,1,
1125,307,'55',3,'',1125,'Maxsize','Maximum size','cm',6,2664,1,
1126,307,'56',4,'',1126,'Maxsize','Maximum size','cm',6,2664,1,
1127,307,'15',5,'',1127,'Maxsize','Maximum size','cm',6,2664,1,
1128,307,'31',6,'',1128,'Maxsize','Maximum size','cm',6,2664,1,
1129,307,'12',7,'',1129,'Maxsize','Maximum size','cm',6,2664,1,
1130,307,'20',8,'',1130,'Maxsize','Maximum size','cm',6,2664,1,
1131,307,'54',9,'',1131,'Maxsize','Maximum size','cm',6,2664,1,
1132,307,'16',10,'',1132,'Maxsize','Maximum size','cm',6,2664,1,
1133,307,'21',11,'',1133,'Maxsize','Maximum size','cm',6,2664,1,
1134,307,'25',12,'',1134,'Maxsize','Maximum size','cm',6,2664,1,
1135,307,'19',13,'',1135,'Maxsize','Maximum size','cm',6,2664,1,
1136,307,'70',14,'',1136,'Maxsize','Maximum size','cm',6,2664,1,
1137,307,'23',15,'',1137,'Maxsize','Maximum size','cm',6,2664,1,
1138,307,'135',16,'',1138,'Maxsize','Maximum size','cm',6,2664,1,
1139,307,'22',17,'',1139,'Maxsize','Maximum size','cm',6,2664,1,
1140,307,'49',18,'',1140,'Maxsize','Maximum size','cm',6,2664,1,
1141,307,'85',19,'',1141,'Maxsize','Maximum size','cm',6,2664,1,
1142,307,'17',20,'',1142,'Maxsize','Maximum size','cm',6,2664,1,
1143,307,'48',21,'',1143,'Maxsize','Maximum size','cm',6,2664,1,
1144,307,'45',22,'',1144,'Maxsize','Maximum size','cm',6,2664,1,
1145,307,'13',23,'',1145,'Maxsize','Maximum size','cm',6,2664,1,
1146,307,'14',24,'',1146,'Maxsize','Maximum size','cm',6,2664,1,
1147,307,'44',25,'',1147,'Maxsize','Maximum size','cm',6,2664,1,
1148,307,'57',26,'',1148,'Maxsize','Maximum size','cm',6,2664,1,
1149,307,'24',27,'',1149,'Maxsize','Maximum size','cm',6,2664,1,
1150,307,'29',28,'',1150,'Maxsize','Maximum size','cm',6,2664,1,
1151,307,'46',29,'',1151,'Maxsize','Maximum size','cm',6,2664,1,
1152,307,'39',30,'',1152,'Maxsize','Maximum size','cm',6,2664,1,
1153,307,'50',31,'',1153,'Maxsize','Maximum size','cm',6,2664,1,
1154,307,'38',32,'',1154,'Maxsize','Maximum size','cm',6,2664,1,
1155,307,'58',33,'',1155,'Maxsize','Maximum size','cm',6,2664,1,
1156,307,'64',34,'',1156,'Maxsize','Maximum size','cm',6,2664,1,
1157,307,'11',35,'',1157,'Maxsize','Maximum size','cm',6,2664,1,
1158,307,'32',36,'',1158,'Maxsize','Maximum size','cm',6,2664,1,
1159,307,'67',37,'',1159,'Maxsize','Maximum size','cm',6,2664,1,
1160,308,'28',1,'',1160,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1161,308,'27',2,'',1161,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1162,308,'25',3,'',1162,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1163,308,'32',4,'',1163,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1164,308,'41',5,'',1164,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1165,308,'18',6,'',1165,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1166,308,'34',7,'',1166,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1167,308,'358',8,'',1167,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1168,308,'391',9,'',1168,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1169,308,'996',10,'',1169,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1170,308,'419',11,'',1170,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1171,308,'373',12,'',1171,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1172,308,'560',13,'',1172,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1173,308,'696',14,'',1173,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1174,308,'9',15,'',1174,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1175,308,'4',16,'',1175,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1176,308,'<15',17,'',1176,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1177,308,'290',18,'',1177,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1178,308,'451',19,'',1178,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1179,308,'496',20,'',1179,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1180,308,'1310',21,'',1180,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1181,308,'1800',22,'',1181,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1182,308,'1420',23,'',1182,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1183,308,'949',24,'',1183,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1184,308,'1940',25,'',1184,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1185,308,'124',26,'',1185,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1186,308,'1070',27,'',1186,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1187,308,'1410',28,'',1187,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1188,308,'1560',29,'',1188,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1189,308,'250',30,'',1189,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1190,308,'304',31,'',1190,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1191,308,'723',32,'',1191,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1192,308,'717',33,'',1192,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1193,308,'788',34,'',1193,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1194,308,'273',35,'',1194,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1195,308,'163',36,'',1195,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1196,308,'136',37,'',1196,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1197,308,'172',38,'',1197,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1198,308,'500',39,'',1198,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1199,308,'472',40,'',1199,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1200,308,'425',41,'',1200,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1201,308,'572',42,'',1201,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1202,308,'66',43,'',1202,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1203,308,'90',44,'',1203,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1204,308,'412',45,'',1204,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1205,308,'240',46,'',1205,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1206,308,'261',47,'',1206,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1207,308,'287',48,'',1207,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1208,308,'302',49,'',1208,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1209,308,'345',50,'',1209,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1210,308,'237',51,'',1210,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1211,308,'80',52,'',1211,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1212,308,'53',53,'',1212,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1213,308,'252',54,'',1213,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1214,308,'532',55,'',1214,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1215,308,'1150',56,'',1215,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1216,308,'158',57,'',1216,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1217,308,'211',58,'',1217,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1218,308,'355',59,'',1218,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1219,308,'889',60,'',1219,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1220,308,'37',61,'',1220,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1221,308,'56',62,'',1221,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1222,308,'101',63,'',1222,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1223,308,'146',64,'',1223,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1224,308,'149',65,'',1224,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1225,308,'193',66,'',1225,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1226,308,'15',67,'',1226,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1227,308,'39',68,'',1227,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1228,308,'1350',69,'',1228,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1229,308,'1030',70,'',1229,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1230,308,'48',71,'',1230,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1231,308,'116',72,'',1231,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1232,308,'130',73,'',1232,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1233,308,'31',74,'',1233,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1234,308,'63',75,'',1234,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1235,308,'40',76,'',1235,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1236,309,'33',1,'',1236,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1237,309,'32',2,'',1237,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1238,309,'36',3,'',1238,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1239,309,'35',4,'',1239,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1240,309,'14',5,'',1240,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1241,309,'31',6,'',1241,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1242,309,'357',7,'',1242,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1243,309,'390',8,'',1243,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1244,309,'1075',9,'',1244,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1245,309,'422',10,'',1245,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1246,309,'367',11,'',1246,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1247,309,'554',12,'',1247,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1248,309,'724',13,'',1248,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1249,309,'3',14,'',1249,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1250,309,'5',15,'',1250,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1251,309,'6',16,'',1251,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1252,309,'286',17,'',1252,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1253,309,'482',18,'',1253,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1254,309,'512',19,'',1254,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1255,309,'1266',20,'',1255,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1256,309,'1709',21,'',1256,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1257,309,'1416',22,'',1257,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1258,309,'945',23,'',1258,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1259,309,'1852',24,'',1259,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1260,309,'123',25,'',1260,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1261,309,'1066',26,'',1261,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1262,309,'1437',27,'',1262,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1263,309,'1587',28,'',1263,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1264,309,'262',29,'',1264,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1265,309,'303',30,'',1265,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1266,309,'721',31,'',1266,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1267,309,'737',32,'',1267,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1268,309,'811',33,'',1268,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1269,309,'282',34,'',1269,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1270,309,'172',35,'',1270,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1271,309,'146',36,'',1271,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1272,309,'178',37,'',1272,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1273,309,'500',38,'',1273,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1274,309,'444',39,'',1274,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1275,309,'575',40,'',1275,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1276,309,'71',41,'',1276,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1277,309,'92',42,'',1277,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1278,309,'435',43,'',1278,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1279,309,'257',44,'',1279,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1280,309,'275',45,'',1280,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1281,309,'271',46,'',1281,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1282,309,'307',47,'',1282,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1283,309,'354',48,'',1283,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1361,54,'Samplesize',1,' ',1361,'Parameter','Statistical and other parameters of a variable','-',2,0,0,
1362,54,'Minsize',2,' ',1362,'Parameter','Statistical and other parameters of a variable','-',2,0,0,
1363,54,'Maxsize',3,' ',1363,'Parameter','Statistical and other parameters of a variable','-',2,0,0,
1364,54,'137Cs_Bq/kgtpVammala',4,' ',1364,'Parameter','Statistical and other parameters of a variable','-',2,0,0,
1365,54,'137Cs_Bq/kgtpSTUK',5,' ',1365,'Parameter','Statistical and other parameters of a variable','-',2,0,0,
1366,54,'Hg_mg/kgtp',6,' ',1366,'Parameter','Statistical and other parameters of a variable','-',2,0,0,
1367,48,'All',1,' ',1367,'Age','Age','a',2,2497,1,
1368,48,'-64',2,' ',1368,'Age','Age','a',2,2497,1,
1369,48,'65+',3,' ',1369,'Age','Age','a',2,2497,1,
1370,422,'Austria',1,' ',1370,'Country','Country of observation','-',6,2664,1,
1371,422,'Belgium',2,' ',1371,'Country','Country of observation','-',6,2664,1,
1372,422,'Bulgaria',3,' ',1372,'Country','Country of observation','-',6,2664,1,
1373,422,'Cyprus',4,' ',1373,'Country','Country of observation','-',6,2664,1,
1374,422,'Czech Republic',5,' ',1374,'Country','Country of observation','-',6,2664,1,
1375,422,'Denmark',6,' ',1375,'Country','Country of observation','-',6,2664,1,
1376,422,'Estonia',7,' ',1376,'Country','Country of observation','-',6,2664,1,
1377,422,'Finland',8,' ',1377,'Country','Country of observation','-',6,2664,1,
1378,422,'France',9,' ',1378,'Country','Country of observation','-',6,2664,1,
1379,422,'Germany',10,' ',1379,'Country','Country of observation','-',6,2664,1,
1380,422,'Greece',11,' ',1380,'Country','Country of observation','-',6,2664,1,
1381,422,'Hungary',12,' ',1381,'Country','Country of observation','-',6,2664,1,
1382,422,'Ireland',13,' ',1382,'Country','Country of observation','-',6,2664,1,
1383,422,'Italy',14,' ',1383,'Country','Country of observation','-',6,2664,1,
1384,422,'Latvia',15,' ',1384,'Country','Country of observation','-',6,2664,1,
1385,422,'Luxembourg',16,' ',1385,'Country','Country of observation','-',6,2664,1,
1386,422,'Malta',17,' ',1386,'Country','Country of observation','-',6,2664,1,
1387,422,'Netherlands',18,' ',1387,'Country','Country of observation','-',6,2664,1,
1388,422,'Poland',19,' ',1388,'Country','Country of observation','-',6,2664,1,
1389,422,'Portugal',20,' ',1389,'Country','Country of observation','-',6,2664,1,
1390,422,'Romania',21,' ',1390,'Country','Country of observation','-',6,2664,1,
1391,422,'Slovakia',22,' ',1391,'Country','Country of observation','-',6,2664,1,
1392,422,'Slovenia',23,' ',1392,'Country','Country of observation','-',6,2664,1,
1393,422,'Spain',24,' ',1393,'Country','Country of observation','-',6,2664,1,
1394,422,'Sweden',25,' ',1394,'Country','Country of observation','-',6,2664,1,
1395,422,'United Kingdom',26,' ',1395,'Country','Country of observation','-',6,2664,1,
1396,422,'EU ',27,' ',1396,'Country','Country of observation','-',6,2664,1,
1397,423,'1970',1,' ',1397,'Year','Year of observation','a',6,2664,1,
1398,423,'1971',2,' ',1398,'Year','Year of observation','a',6,2664,1,
1399,423,'1972',3,' ',1399,'Year','Year of observation','a',6,2664,1,
1400,423,'1973',4,' ',1400,'Year','Year of observation','a',6,2664,1,
1401,423,'1974',5,' ',1401,'Year','Year of observation','a',6,2664,1,
1402,423,'1975',6,' ',1402,'Year','Year of observation','a',6,2664,1,
1403,423,'1976',7,' ',1403,'Year','Year of observation','a',6,2664,1,
1404,423,'1977',8,' ',1404,'Year','Year of observation','a',6,2664,1,
1405,423,'1978',9,' ',1405,'Year','Year of observation','a',6,2664,1,
1406,423,'1979',10,' ',1406,'Year','Year of observation','a',6,2664,1,
1407,423,'1980',11,' ',1407,'Year','Year of observation','a',6,2664,1,
1408,423,'1981',12,' ',1408,'Year','Year of observation','a',6,2664,1,
1409,423,'1982',13,' ',1409,'Year','Year of observation','a',6,2664,1,
1410,423,'1983',14,' ',1410,'Year','Year of observation','a',6,2664,1,
1411,423,'1984',15,' ',1411,'Year','Year of observation','a',6,2664,1,
1412,423,'1985',16,' ',1412,'Year','Year of observation','a',6,2664,1,
1413,423,'1986',17,' ',1413,'Year','Year of observation','a',6,2664,1,
1414,423,'1987',18,' ',1414,'Year','Year of observation','a',6,2664,1,
1415,423,'1988',19,' ',1415,'Year','Year of observation','a',6,2664,1,
1416,423,'1989',20,' ',1416,'Year','Year of observation','a',6,2664,1,
1417,423,'1990',21,' ',1417,'Year','Year of observation','a',6,2664,1,
1418,423,'1991',22,' ',1418,'Year','Year of observation','a',6,2664,1,
1419,423,'1992',23,' ',1419,'Year','Year of observation','a',6,2664,1,
1420,423,'1993',24,' ',1420,'Year','Year of observation','a',6,2664,1,
1421,423,'1994',25,' ',1421,'Year','Year of observation','a',6,2664,1,
1422,423,'1995',26,' ',1422,'Year','Year of observation','a',6,2664,1,
1423,423,'1996',27,' ',1423,'Year','Year of observation','a',6,2664,1,
1424,423,'1997',28,' ',1424,'Year','Year of observation','a',6,2664,1,
1425,423,'1998',29,' ',1425,'Year','Year of observation','a',6,2664,1,
1426,423,'1999',30,' ',1426,'Year','Year of observation','a',6,2664,1,
1427,423,'2000',31,' ',1427,'Year','Year of observation','a',6,2664,1,
1428,423,'2001',32,' ',1428,'Year','Year of observation','a',6,2664,1,
1429,423,'2002',33,' ',1429,'Year','Year of observation','a',6,2664,1,
1430,423,'2003',34,' ',1430,'Year','Year of observation','a',6,2664,1,
1431,423,'2004',35,' ',1431,'Year','Year of observation','a',6,2664,1,
1432,423,'2005',36,' ',1432,'Year','Year of observation','a',6,2664,1,
1433,423,'2006',37,' ',1433,'Year','Year of observation','a',6,2664,1,
1434,423,'2007',38,' ',1434,'Year','Year of observation','a',6,2664,1,
1435,424,'Male',1,' ',1435,'Sex','Sex of a person','-',6,2664,1,
1436,424,'Female',2,' ',1436,'Sex','Sex of a person','-',6,2664,1,
1437,424,'All',3,' ',1437,'Sex','Sex of a person','-',6,2664,1,
1438,54,'Morbidity',1,' ',1438,'Parameter','Statistical and other parameters of a variable','-',2,0,0,
1455,422,'Liechtenstein',17,'',1455,'Country','Country of observation','-',6,2664,1,
1456,422,'Lithuania',18,'',1456,'Country','Country of observation','-',6,2664,1,
1467,480,'1. Combustion installations',1,'',1467,'CITL sector','CITL sector','-',6,2664,1,
1468,480,'2. Mineral oil refineries',2,'',1468,'CITL sector','CITL sector','-',6,2664,1,
1469,480,'3. Coke ovens',3,'',1469,'CITL sector','CITL sector','-',6,2664,1,
1470,480,'4. Metal ore roasting or sintering',4,'',1470,'CITL sector','CITL sector','-',6,2664,1,
1471,480,'5. Pig iron or steel',5,'',1471,'CITL sector','CITL sector','-',6,2664,1,
1472,480,'6. Cement clinker or lime',6,'',1472,'CITL sector','CITL sector','-',6,2664,1,
1473,480,'7. Glass including glass fibre',7,'',1473,'CITL sector','CITL sector','-',6,2664,1,
1474,480,'8. Ceramic products by firing',8,'',1474,'CITL sector','CITL sector','-',6,2664,1,
1475,480,'9. Pulp, paper and board',9,'',1475,'CITL sector','CITL sector','-',6,2664,1,
1476,480,'99. Other activity opted-in',10,'',1476,'CITL sector','CITL sector','-',6,2664,1,
1477,481,'Allocated allowances',1,'',1477,'CITL_information','CITL information','-',6,2664,1,
1478,481,'Surrendered allowances',2,'',1478,'CITL_information','CITL information','-',6,2664,1,
1479,481,'Verified emissions',3,'',1479,'CITL_information','CITL information','-',6,2664,1,
1483,423,'2008',4,'',1483,'Year','Year of observation','a',6,2664,1,
1484,54,'ETS data',1,'',1484,'Parameter','Statistical and other parameters of a variable','-',2,0,0,
1488,493,'All causes',1,'',1488,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1489,493,'Infectious and parasitic diseases',2,'',1489,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1490,493,'Typhoid and paratyphoid fever',3,'',1490,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1491,493,'Other intestinal infectious diseases',4,'',1491,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1492,493,'Tuberculosis of respiratory system',5,'',1492,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1493,493,'Tuberculosis other forms',6,'',1493,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1494,493,'Whooping cough',7,'',1494,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1495,493,'Meningococcal infection',8,'',1495,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1496,493,'Tetanus',9,'',1496,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1497,493,'Septicaemia',10,'',1497,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1498,493,'Other bacterial diseases',11,'',1498,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1499,493,'Measles',12,'',1499,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1500,493,'HIV disease',13,'',1500,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1501,493,'Other viral diseases',14,'',1501,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1502,493,'Malaria',15,'',1502,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1503,493,'Other arthropod-borne diseases',16,'',1503,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1504,493,'Sexually transmitted diseases',17,'',1504,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1505,493,'Other infectious and parasitic diseases',18,'',1505,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1506,493,'Malignant neoplasms',19,'',1506,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1507,493,'Malignant neoplasm of lip oral cavity and pharynx',20,'',1507,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1508,493,'Malignant neoplasm of oesophagus',21,'',1508,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1509,493,'Malignant neoplasm of stomach',22,'',1509,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1510,493,'Malignant neoplasm of colon',23,'',1510,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1511,493,'Malignant neoplasm of rectum rectosigmoid junction and anus',24,'',1511,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1512,493,'Malignant neoplasm of liver',25,'',1512,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1513,493,'Malignant neoplasm of larynx',26,'',1513,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1514,493,'Malignant neoplasm of trachea bronchus and lung',27,'',1514,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1515,493,'Malignant neoplasm of breast',28,'',1515,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1516,493,'Malignant neoplasm of cervix uteri',29,'',1516,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1517,493,'Malignant neoplasm of uterus other and unspecified',30,'',1517,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1518,493,'Malignant neoplasm of prostate',31,'',1518,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1519,493,'Malignant neoplasm of bladder',32,'',1519,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1520,493,'Malignant neoplasm of other sites',33,'',1520,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1521,493,'Leukaemia',34,'',1521,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1522,493,'Other malignant neoplasms of lymphoid and haematopoietic and related tissue',35,'',1522,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1523,493,'Benign neoplasm other and unspecified neoplasm',36,'',1523,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1524,493,'Diabetes mellitus',37,'',1524,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1525,493,'Other endocrine and metabolic diseases',38,'',1525,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1526,493,'Malnutrition',39,'',1526,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1527,493,'Other nutritional deficiencies',40,'',1527,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1528,493,'Anaemias',41,'',1528,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1529,493,'Other diseases of blood and blood-forming organs',42,'',1529,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1530,493,'Mental disorders',43,'',1530,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1531,493,'Meningitis',44,'',1531,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1532,493,'Multiple sclerosis',45,'',1532,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1533,493,'Epilepsy',46,'',1533,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1534,493,'Other diseases of the nervous system and sense organs',47,'',1534,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1535,493,'Diseases of the circulatory system',48,'',1535,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1536,493,'Acute rheumatic fever',49,'',1536,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1537,493,'Chronic rheumatic heart disease',50,'',1537,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1538,493,'Hypertensive disease',51,'',1538,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1539,493,'Acute myocardial infarction',52,'',1539,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1540,493,'Other ischaemic heart diseases',53,'',1540,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1541,493,'Diseases of pulmonary circulation and other forms of heart disease',54,'',1541,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1542,493,'Cerebrovascular disease',55,'',1542,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1543,493,'Atherosclerosis',56,'',1543,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1544,493,'Embolism thrombosis and other diseases of arteries arterioles and capillaries',57,'',1544,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1545,493,'Phlebitis thrombophlebitis venous embolism and thrombosis',58,'',1545,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1546,493,'Other diseases of the circulatory system',59,'',1546,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1547,493,'Acute upper respiratory infection',60,'',1547,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1548,493,'Acute bronchitis and bronchiolitis',61,'',1548,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1549,493,'Pneumonia',62,'',1549,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1550,493,'Influenza',63,'',1550,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1551,493,'Bronchitis chronic and unspecified emphysema and asthma',64,'',1551,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1552,493,'Other diseases of the respiratory system',65,'',1552,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1553,493,'Ulcer of stomach and duodenum',66,'',1553,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1554,493,'Appendicitis',67,'',1554,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1555,493,'Hernia of abdominal cavity and intestinal obstruction',68,'',1555,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1556,493,'Chronic liver disease and cirrhosis',69,'',1556,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1557,493,'Other diseases of the digestive system',70,'',1557,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1558,493,'Nephritis nephrotic syndrome and nephrosis',71,'',1558,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1559,493,'Infections of kidney',72,'',1559,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1560,493,'Hyperplasia of prostate',73,'',1560,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1561,493,'Other diseases of the genitourinary system',74,'',1561,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1562,493,'Abortion',75,'',1562,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1563,493,'Haemorrhage of pregnancy and childbirth',76,'',1563,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1564,493,'Toxaemia of pregnancy',77,'',1564,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1565,493,'Complications of the puerperium',78,'',1565,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1566,493,'Other direct obstetric causes',79,'',1566,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1567,493,'Indirect obstetric causes',80,'',1567,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1568,493,'Diseases of skin and subcutaneous tissue',81,'',1568,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1569,493,'Diseases of the musculoskeletal system and connective tissue',82,'',1569,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1570,493,'Spina bifida and hydrocephalus',83,'',1570,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1571,493,'Congenital anomalies of the circulatory system',84,'',1571,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1572,493,'Other congenital anomalies',85,'',1572,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1573,493,'Birth trauma',86,'',1573,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1574,493,'Other conditions originating in the perinatal period',87,'',1574,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1575,493,'Senility',88,'',1575,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1576,493,'Signs symptoms and other ill-defined conditions',89,'',1576,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1577,493,'Accidents and adverse effects',90,'',1577,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1578,493,'Motor vehicle traffic accidents',91,'',1578,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1579,493,'Other transport accidents',92,'',1579,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1580,493,'Accidental poisoning',93,'',1580,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1581,493,'Accidental falls',94,'',1581,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1582,493,'Accidents caused by fire and flames',95,'',1582,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1583,493,'Accidental drowning and submersion',96,'',1583,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1584,493,'Accidents caused by machinery and by cutting and piercing instruments',97,'',1584,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1585,493,'Accidents caused by firearm missile',98,'',1585,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1586,493,'All other accidents including late effects',99,'',1586,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1587,493,'Drugs medicaments causing adverse effects in therapeutic use',100,'',1587,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1588,493,'Suicide and self- inflicted injury',101,'',1588,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1589,493,'Homicide and injury purposely inflicted by other persons',102,'',1589,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1590,493,'Other external causes',103,'',1590,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1591,48,'All Ages',1,'',1591,'Age','Age','a',2,2497,1,
1592,48,'Under 1',2,'',1592,'Age','Age','a',2,2497,1,
1593,48,'1 to 4',3,'',1593,'Age','Age','a',2,2497,1,
1594,48,'5 to 14',4,'',1594,'Age','Age','a',2,2497,1,
1595,48,'15 to 24',5,'',1595,'Age','Age','a',2,2497,1,
1596,48,'25 to 34',6,'',1596,'Age','Age','a',2,2497,1,
1597,48,'35 to 44',7,'',1597,'Age','Age','a',2,2497,1,
1598,48,'45 to 54',8,'',1598,'Age','Age','a',2,2497,1,
1599,48,'55 to 64',9,'',1599,'Age','Age','a',2,2497,1,
1600,48,'65 to 74',10,'',1600,'Age','Age','a',2,2497,1,
1601,48,'Over 75',11,'',1601,'Age','Age','a',2,2497,1,
1603,496,'# deaths',1,'',1603,'Parameter1','’Parameter','# or 1/100000 py',6,2664,1,
1604,496,'Mortality',2,'',1604,'Parameter1','’Parameter','# or 1/100000 py',6,2664,1,
1605,422,'Seychelles',1,'',1605,'Country','Country of observation','-',6,2664,1,
1606,422,'Brunei Darussalam',2,'',1606,'Country','Country of observation','-',6,2664,1,
1621,493,'1000',1,'',1621,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1622,493,'1001',2,'',1622,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1623,493,'1002',3,'',1623,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1624,493,'1003',4,'',1624,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1625,493,'1004',5,'',1625,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1626,493,'1005',6,'',1626,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1627,493,'1006',7,'',1627,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1628,493,'1007',8,'',1628,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1629,493,'1008',9,'',1629,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1630,493,'1009',10,'',1630,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1631,493,'1010',11,'',1631,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1632,493,'1011',12,'',1632,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1633,493,'1012',13,'',1633,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1634,493,'1013',14,'',1634,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1635,493,'1014',15,'',1635,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1636,493,'1015',16,'',1636,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1637,493,'1016',17,'',1637,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1638,493,'1017',18,'',1638,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1639,493,'1018',19,'',1639,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1640,493,'1019',20,'',1640,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1641,493,'1020',21,'',1641,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1642,493,'1021',22,'',1642,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1643,493,'1022',23,'',1643,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1644,493,'1023',24,'',1644,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1645,493,'1024',25,'',1645,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1646,493,'1025',26,'',1646,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1647,493,'1026',27,'',1647,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1648,493,'1027',28,'',1648,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1649,493,'1028',29,'',1649,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1650,493,'1029',30,'',1650,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1651,493,'1030',31,'',1651,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1652,493,'1031',32,'',1652,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1653,493,'1032',33,'',1653,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1654,493,'1033',34,'',1654,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1655,493,'1034',35,'',1655,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1656,493,'1035',36,'',1656,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1657,493,'1036',37,'',1657,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1658,493,'1037',38,'',1658,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1659,493,'1038',39,'',1659,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1660,493,'1039',40,'',1660,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1661,493,'1040',41,'',1661,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1662,493,'1041',42,'',1662,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1663,493,'1042',43,'',1663,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1664,493,'1043',44,'',1664,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1665,493,'1044',45,'',1665,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1666,493,'1045',46,'',1666,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1667,493,'1046',47,'',1667,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1668,493,'1047',48,'',1668,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1669,493,'1048',49,'',1669,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1670,493,'1049',50,'',1670,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1671,493,'1050',51,'',1671,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1672,493,'1051',52,'',1672,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1673,493,'1052',53,'',1673,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1674,493,'1053',54,'',1674,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1675,493,'1054',55,'',1675,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1676,493,'1055',56,'',1676,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1677,493,'1056',57,'',1677,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1678,493,'1057',58,'',1678,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1679,493,'1058',59,'',1679,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1680,493,'1059',60,'',1680,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1681,493,'1060',61,'',1681,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1682,493,'1061',62,'',1682,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1683,493,'1062',63,'',1683,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1684,493,'1063',64,'',1684,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1685,493,'1064',65,'',1685,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1686,493,'1065',66,'',1686,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1687,493,'1066',67,'',1687,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1688,493,'1067',68,'',1688,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1689,493,'1068',69,'',1689,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1690,493,'1069',70,'',1690,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1691,493,'1070',71,'',1691,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1692,493,'1071',72,'',1692,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1693,493,'1072',73,'',1693,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1694,493,'1073',74,'',1694,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1695,493,'1074',75,'',1695,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1696,493,'1075',76,'',1696,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1697,493,'1076',77,'',1697,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1698,493,'1077',78,'',1698,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1699,493,'1078',79,'',1699,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1700,493,'1079',80,'',1700,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1701,493,'1080',81,'',1701,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1702,493,'1081',82,'',1702,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1703,493,'1082',83,'',1703,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1704,493,'1083',84,'',1704,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1705,493,'1084',85,'',1705,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1706,493,'1085',86,'',1706,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1707,493,'1086',87,'',1707,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1708,493,'1087',88,'',1708,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1709,493,'1088',89,'',1709,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1710,493,'1089',90,'',1710,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1711,493,'1090',91,'',1711,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1712,493,'1091',92,'',1712,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1713,493,'1092',93,'',1713,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1714,493,'1093',94,'',1714,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1715,493,'1094',95,'',1715,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1716,493,'1095',96,'',1716,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1717,493,'1096',97,'',1717,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1718,493,'1097',98,'',1718,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1719,493,'1098',99,'',1719,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1720,493,'1099',100,'',1720,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1721,493,'1100',101,'',1721,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1722,493,'1101',102,'',1722,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1723,493,'1102',103,'',1723,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1724,493,'1103',104,'',1724,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1729,48,'1-4',1,'',1729,'Age','Age','a',2,2497,1,
1730,48,'5-14',2,'',1730,'Age','Age','a',2,2497,1,
1731,48,'15-24',3,'',1731,'Age','Age','a',2,2497,1,
1732,48,'25-34',4,'',1732,'Age','Age','a',2,2497,1,
1733,48,'35-44',5,'',1733,'Age','Age','a',2,2497,1,
1734,48,'45-54',6,'',1734,'Age','Age','a',2,2497,1,
1735,48,'55-64',7,'',1735,'Age','Age','a',2,2497,1,
1736,48,'65-74',8,'',1736,'Age','Age','a',2,2497,1,
1737,48,'75 &+',9,'',1737,'Age','Age','a',2,2497,1,
1738,48,'Unknown',10,'',1738,'Age','Age','a',2,2497,1,
1740,48,'0',12,'',1740,'Age','Age','a',2,2497,1,
1741,48,'1',13,'',1741,'Age','Age','a',2,2497,1,
1742,48,'2',14,'',1742,'Age','Age','a',2,2497,1,
1743,48,'3',15,'',1743,'Age','Age','a',2,2497,1,
1744,48,'4',16,'',1744,'Age','Age','a',2,2497,1,
1745,48,'5-9',17,'',1745,'Age','Age','a',2,2497,1,
1746,48,'10-14',18,'',1746,'Age','Age','a',2,2497,1,
1747,48,'15-19',19,'',1747,'Age','Age','a',2,2497,1,
1748,48,'20-24',20,'',1748,'Age','Age','a',2,2497,1,
1749,48,'25-29',21,'',1749,'Age','Age','a',2,2497,1,
1750,48,'30-34',22,'',1750,'Age','Age','a',2,2497,1,
1751,48,'35-39',23,'',1751,'Age','Age','a',2,2497,1,
1752,48,'40-44',24,'',1752,'Age','Age','a',2,2497,1,
1753,48,'45-49',25,'',1753,'Age','Age','a',2,2497,1,
1754,48,'50-54',26,'',1754,'Age','Age','a',2,2497,1,
1755,48,'55-59',27,'',1755,'Age','Age','a',2,2497,1,
1756,48,'60-64',28,'',1756,'Age','Age','a',2,2497,1,
1757,48,'65-69',29,'',1757,'Age','Age','a',2,2497,1,
1758,48,'70-74',30,'',1758,'Age','Age','a',2,2497,1,
1759,48,'75-79',31,'',1759,'Age','Age','a',2,2497,1,
1760,48,'80-84',32,'',1760,'Age','Age','a',2,2497,1,
1761,48,'85 &+',33,'',1761,'Age','Age','a',2,2497,1,
1763,54,'Deaths',1,'',1763,'Parameter','Statistical and other parameters of a variable','-',2,0,0,
1764,36,'PM',1,'',1764,'Pollutant','Pollutant','-',2,2493,1,
1765,36,'CO2',2,'',1765,'Pollutant','Pollutant','-',2,2493,1,
1766,43,'Bus',1,'',1766,'Vehicle_type','Vehicle type','-',2,0,0,
1767,43,'Minibus',2,'',1767,'Vehicle_type','Vehicle type','-',2,0,0,
1768,43,'Car (d)',3,'',1768,'Vehicle_type','Vehicle type','-',2,0,0,
1769,43,'Car (g)',4,'',1769,'Vehicle_type','Vehicle type','-',2,0,0,
1770,539,'Injuries',1,'',1770,'Accidents','Accident type','-',6,2664,1,
1771,539,'Deaths',2,'',1771,'Accidents','Accident type','-',6,2664,1,
1775,567,' 6.00-20.00',1,'',1775,'Period1','Different times of day','h',6,2664,1,
1776,567,'20.00-24.00',2,'',1776,'Period1','Different times of day','h',6,2664,1,
1777,567,' 0.00- 6.00',3,'',1777,'Period1','Different times of day','h',6,2664,1
)280,96,148,131,1,1,1,1,1,0,0,0,02,370,45,476,4452,518,523,725,303,0,MIDM2,404,34,750,516,0,MIDM39325,65535,39321[L_j,L_i][L_j,L_i]ObjThis node checks the variables listed in Var_for_rdb and makes an index of those that are NOT found in the result database. This is then used as an index in Inp_var for adding variable information.Table(O_i,O_j)(
1,'Op_en1901','Net health effects due to the consumption of salmon','avoided cases/a',1,1901,1,
2,'Op_en2693','Testvariable','kg',1,2693,1,
3,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1,
4,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
5,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
6,'Ppmconc_bustraffic','PM2.5 concentration from bus traffic in Helsinki in 2020','ug/m3',1,0,0,
7,'Op_en2202','Concentration-response to PM2.5','m3/ug',1,2202,1,
8,'Comptraf_scenoutput','Composite traffic v.1 scenario outputs','various',1,0,0,
9,'Fig_3_cost_by_source','Cost by source','e/trip',1,0,0,
10,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0,
11,'Fig_5b_subsidies','Subsidies needed to obtain the composite fraction objective','e/day',1,0,0,
12,'Fig_5c_expanding','Societal costs at different levels of guarantee','e/day',1,0,0,
13,'Bw1','Human body weight in Harjavalta','kg',1,2475,1,
14,'Testvariable2','Another variable for testing','kg',1,0,0,
15,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0,
16,'Op_en1900','Pollutant health risk due to the consumption of salmon','avoided cases/a',1,1900,1,
17,'Op_en1903','Persistent pollutant concentrations in salmon','µg/kg',1,1903,1,
18,'Op_en1905','Exposure to persistent pollutants due to salmon in the population of the Western Europe','µg/kg/d',1,1905,1,
19,'Op_en1906','Dose-response function of persistent pollutants','(mg/kg/d)-1',1,1906,1,
20,'Op_en1907','Omega-3 content in salmon','g/g',1,1907,1,
21,'Op_en1908','Omega-3 intake due to salmon in the population of the Western Europe','g/d',1,1908,1,
22,'Op_en1910','Total mortality in the Western Europe','cases/a',1,1910,1,
23,'Op_en1911','Cardiovascular mortality in the Western Europe','cases/a',1,1911,1,
24,'Op_en1912','Cardiovascular effects of omega-3 in salmon in teh Western Europe','avoided cases/a',1,1912,1,
25,'Op_en1898','Recommendation for consumption of farmed salmon','-',1,1898,1,
26,'Op_en1899','Pollutant concentration limits for fish feed','-',1,1899,1,
27,'Op_en1902','Persistent pollutant concentrations in fish feed','fraction',1,1902,1,
28,'Op_en1904','Salmon intake in the population of the Western Europe','g/d',1,1904,1,
29,'Op_en1909','ERF of omega-3 fatty acids on cardiovascular effects','1/(g/d)',1,1909,1,
30,'Op_en2556','Personal exposures to volatile organic compounds in Germany','ug/m^3',1,2556,1,
31,'Op_en2406','Excess cases of iMetHb in England and Wales','number',1,2406,1,
33,'Decision','Possible range of decisions for a single decision-maker','-',2,2496,1,
34,'Health_impact','Health impact','',2,2495,1,
35,'Time','Time','s or date',2,2497,1,
36,'Pollutant','Pollutant','-',2,2493,1,
37,'Spatial_location','Spatial location',' km or °',2,2498,1,
38,'Length','Length','km',2,2498,1,
39,'Non_health_impact','Non-health impact','-',2,2500,1,
40,'Period','Period','s',2,2497,1,
41,'Emission_source','Emission source','-',2,2492,1,
42,'Environ_compartment','Environmental compartment','-',2,2490,1,
43,'Vehicle_type','Vehicle type','-',2,0,0,
44,'Person_or_group','Person or group','-',2,2499,1,
45,'Transport_mode','Transport mode','-',2,0,0,
46,'Cost_type','Cost type','-',2,0,0,
47,'Composite_fraction','Composite fraction','fraction',2,0,0,
48,'Age','Age','a',2,2497,1,
49,'Municipality_fin','Municipalities in Finland','-',2,2498,1,
51,'Food_source','The method for food production','-',2,0,0,
52,'Feed_pollutant','Decision about fish feed','-',2,0,0,
53,'Salmon_recomm','Decision about samon consumption recommendation','-',2,0,0,
32,'0','No dimension has been identified','-',2,0,0,
54,'Parameter','Statistical and other parameters of a variable','-',2,0,0,
55,'Salmon_decision','','',6,0,0,
56,'Hma_area','','',6,0,0,
57,'Hma_region','','',6,0,0,
58,'Hma_zone','','',6,0,0,
59,'Year_1','','',6,0,0,
60,'Op_en2665','Cause of death 1','ICD-10',6,2665,1,
61,'Year_2','','',6,0,0,
62,'Cause_of_death_2','','',6,0,0,
63,'Length_1','','',6,0,0,
70,'Output_1','','',6,0,0,
65,'Period_1','','',6,0,0,
86,'Run','','',6,0,0,
71,'Vehicle_noch','','',6,0,0,
72,'Stakeholder_1','','',6,0,0,
73,'Mode1','','',6,0,0,
74,'Cost_structure_1','','',6,0,0,
75,'Comp_fr_1','','',6,0,0,
76,'Age1','','',6,0,0,
77,'Municipality_fin1','','',6,0,0,
82,'Year3','','',6,0,0,
81,'Recommendation1','','',6,0,0,
80,'Reg_poll','','',6,0,0,
79,'Salmon1','','',6,0,0,
78,'Pollutant1','','',6,0,0,
83,'H1899','','',6,0,0,
84,'H1898','','',6,0,0,
85,'Cause_of_death3','','',6,0,0,
87,'Condb_compartment1','','',6,0,0,
88,'Condb_location1','','',6,0,0,
89,'Condb_agent1','','',6,0,0,
90,'Condb_param1','','',6,0,0,
91,'Condb_agent2','','',6,0,0,
92,'Vehicle_1','','',6,0,0,
93,'Op_en2672','','',6,0,0,
94,'94','Analytica','',9,0,0,
95,'95','Analytica 4.1.0.9','',9,0,0,
97,'97','Analytica 4.1.0.9, CompositeTraffic_1_0_6.ana v. 11:47, 1000 iterations','',9,0,0,
99,'99','Analytica 4.1.0.9, RDB connection.ANA, 100 iterations','',9,0,0,
100,'100','RDB connection.ANA v. 1.9.2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 100','',9,0,0,
101,'101','RDB connection.ANA v. 2.9.2008. Test data only., Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 100','',9,0,0,
102,'102','RDB connection.ANA v. 3.9.2008 b. Test data only., Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 100','',9,0,0,
103,'103','Farmed salmon.ANA 10:36, 31 December 2007, RDB connection.ANA 13:58, 3 September 2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 1000','',9,0,0,
104,'104','Farmed salmon.ANA 10:36, 31 December 2007, RDB connection.ANA 13:58, 3 September 2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 1000','',9,0,0,
105,'105','Farmed salmon.ANA 10:36, 31 December 2007, RDB connection.ANA 13:58, 3 September 2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 1000','',9,0,0,
106,'106','Farmed salmon.ANA 10:36, 31 December 2007, RDB connection.ANA 13:58, 3 September 2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 10','',9,0,0,
107,'107','Farmed salmon.ANA 8.9.2008, RDB connection.ANA 8.9.2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 10','',9,0,0,
108,'108','Farmed salmon.ANA 8.9.2008, RDB connection.ANA 8.9.2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 1000','',9,0,0,
98,'98','Test','',9,0,0,
109,'109',' CompositeTraffic_1_0_6.ANA 16.9.2008, RDB connection.ANA 16.9.2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 10','',9,0,0,
110,'110',' CompositeTraffic_1_0_6.ANA 16.9.2008, RDB connection.ANA 16.9.2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 1000','',9,0,0,
111,'111','RDB connection.ANA 16.9.2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 100','',9,0,0,
112,'112','RDB connection.ANA 9.10.2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 100','',9,0,0,
113,'113','RDB connection.ANA 9.10.2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 10','',9,0,0,
114,'Op_en1896','Benefit-risk assessment on farmed salmon','',4,1896,1,
130,'Op_en2705','Pollutant','-',6,2705,1,
131,'Op_en2706','Salmon type','-',6,2706,1,
133,'Op_en2707','Cause of death3','ICD-10',6,2707,1,
135,'Op_en2708','Year3','year',6,2708,1,
137,'Op_en2694','Testrun 1: Analytica Enterprise, (Windows), Version: 40100, Samplesize: 10','',9,2694,1,
159,'Op_eni1896','Benefit-risk assessment of farmed salmon','',4,0,1,
160,'Op_eni2694','Testrun 1: Analytica Enterprise, (Windows), Version: 40100, Samplesize: 10','',9,0,1,
183,'Op_eni2695','Testrun 2: Analytica Enterprise, (Windows), Version: 40100, Samplesize: 1000','',9,0,1,
184,'Op_en2778','Mortality in Finland','# or 1/100000 py',1,2778,1,
185,'Op_en2780','Sex','-',6,2780,1,
186,'Op_en2779','Diagnosis1','-',6,2779,1,
187,'Op_en2784','Units1','-',6,2784,1,
188,'Op_en2781','Age group1','a',6,2781,1,
189,'Country1','Country1','-',6,2785,1,
191,'Op_en2695','Testrun 2: Analytica Enterprise, (Windows), Version: 40100, Samplesize: 100','',9,2695,1,
192,'Op_en2811','Morbidity data for Europe','1/100000 a',1,2811,1,
193,'Age2','Age2','a',6,2812,1,
194,'Country2','Country2','-',6,2813,1,
195,'Year2','Year2','a',6,2814,1,
196,'Sex2','Sex2','-',6,2815,1,
207,'Diagnosis2','Diagnosis2','ICD-10',6,2835,1,
203,'203','Testrun 2: Analytica Enterprise, (Windows), Version: 40100, Samplesize: 100','',9,2817,1,
210,'Op_en2495','Health outcome1','-',6,2495,1,
211,'Op_en2922','Ecosense test study','Euro',7,2922,1,
212,'212.00000000000000','Country identifier','-',6,2664,1,
213,'213.00000000000000','Receptor of the impact','-',6,2664,1,
214,'214.00000000000000','Receptor subgroup','-',6,2664,1,
215,'215.00000000000000','Impact in the receptor','-',6,2664,1,
216,'216.00000000000000','Pollutant causing the impact','-',6,2664,1,
217,'217.00000000000000','Reference for the impact function','-',6,2664,1,
218,'Op_en2817','Analytica Enterprise, (Windows), Version: 40100, Samplesize: 100','0.00000000000000',9,2817,1,
220,'220.00000000000000','Country identifier','-',6,2664,1,
221,'221.00000000000000','Receptor of the impact','-',6,2664,1,
222,'222.00000000000000','Receptor subgroup','-',6,2664,1,
223,'223.00000000000000','Impact in the receptor','-',6,2664,1,
224,'224.00000000000000','Pollutant causing the impact','-',6,2664,1,
225,'225.00000000000000','Reference for the impact function','-',6,2664,1,
227,'227.00000000000000','Country identifier','-',6,2664,1,
228,'228.00000000000000','Receptor of the impact','-',6,2664,1,
229,'229.00000000000000','Receptor subgroup','-',6,2664,1,
230,'230.00000000000000','Impact in the receptor','-',6,2664,1,
231,'231.00000000000000','Pollutant causing the impact','-',6,2664,1,
232,'232.00000000000000','Reference for the impact function','-',6,2664,1,
234,'234.00000000000000','Country identifier','-',6,2664,1,
235,'235.00000000000000','Receptor of the impact','-',6,2664,1,
236,'236.00000000000000','Receptor subgroup','-',6,2664,1,
237,'237.00000000000000','Impact in the receptor','-',6,2664,1,
238,'238.00000000000000','Pollutant causing the impact','-',6,2664,1,
239,'239.00000000000000','Reference for the impact function','-',6,2664,1,
241,'241.00000000000000','Country identifier','-',6,2664,1,
242,'242.00000000000000','Receptor of the impact','-',6,2664,1,
243,'243.00000000000000','Receptor subgroup','-',6,2664,1,
244,'244.00000000000000','Impact in the receptor','-',6,2664,1,
245,'245.00000000000000','Pollutant causing the impact','-',6,2664,1,
246,'246.00000000000000','Reference for the impact function','-',6,2664,1,
248,'248.00000000000000','Country identifier','-',6,2664,1,
249,'249.00000000000000','Receptor of the impact','-',6,2664,1,
250,'250.00000000000000','Receptor subgroup','-',6,2664,1,
251,'251.00000000000000','Impact in the receptor','-',6,2664,1,
252,'252.00000000000000','Pollutant causing the impact','-',6,2664,1,
253,'253.00000000000000','Reference for the impact function','-',6,2664,1,
255,'255.00000000000000','Country identifier','-',6,2664,1,
256,'256.00000000000000','Receptor of the impact','-',6,2664,1,
257,'257.00000000000000','Receptor subgroup','-',6,2664,1,
258,'258.00000000000000','Impact in the receptor','-',6,2664,1,
259,'259.00000000000000','Pollutant causing the impact','-',6,2664,1,
260,'260.00000000000000','Reference for the impact function','-',6,2664,1,
262,'262.00000000000000','Country identifier','-',6,2664,1,
263,'263.00000000000000','Receptor of the impact','-',6,2664,1,
264,'264.00000000000000','Receptor subgroup','-',6,2664,1,
265,'265.00000000000000','Impact in the receptor','-',6,2664,1,
266,'266.00000000000000','Pollutant causing the impact','-',6,2664,1,
267,'267.00000000000000','Reference for the impact function','-',6,2664,1,
269,'269.00000000000000','Country identifier','-',6,2664,1,
270,'270.00000000000000','Receptor of the impact','-',6,2664,1,
271,'271.00000000000000','Receptor subgroup','-',6,2664,1,
272,'272.00000000000000','Impact in the receptor','-',6,2664,1,
273,'273.00000000000000','Pollutant causing the impact','-',6,2664,1,
274,'274.00000000000000','Reference for the impact function','-',6,2664,1,
275,'275.00000000000000','Analytica Enterprise, (Windows), Version: 40100, Samplesize: 100','0.00000000000000',9,2817,1,
277,'CountryID','Country identifier','-',6,2664,1,
278,'Receptor','Receptor of the impact','-',6,2664,1,
279,'ReceptorSubGroup','Receptor subgroup','-',6,2664,1,
280,'Impact','Impact in the receptor','-',6,2664,1,
282,'FunctionReference','Reference for the impact function','-',6,2664,1,
283,'283.00000000000000','Analytica Enterprise, (Windows), Version: 40100, Samplesize: 100','0.00000000000000',9,2817,1,
291,'291','Analytica Enterprise, (Windows), Version: 40100, Samplesize: 100','0',9,2817,1,
299,'299','Analytica Enterprise, (Windows), Version: 40100, Samplesize: 100','0',9,2817,1,
300,'Op_fi1669','Cesium-137 elintarvikkeissa','Bq or mg/kg f.w.',7,1669,2,
301,'Municipality','Municipality in Finland','-',6,2664,1,
302,'Lake','Lake in Finland','-',6,2664,1,
303,'Date','Date of observation','date',6,2664,1,
304,'Fish','Fish species','-',6,0,5,
305,'Samplesize','Number of samples taken','#',6,2664,1,
306,'Minsize','Minimum size','cm',6,2664,1,
307,'Maxsize','Maximum size','cm',6,2664,1,
308,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
309,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
310,'Hg_mg/kgtp','Concentration of methyl mercury','mg/kg f.w.',6,2664,1,
311,'311','Analytica Enterprise, (Windows), Version: 40100, Samplesize: 100','0',9,2817,1,
323,'323','Analytica Enterprise, (Windows), Version: 40100, Samplesize: 100','0',9,2817,1,
335,'335','Analytica Enterprise, (Windows), Version: 40100, Samplesize: 100','0',9,2817,1,
347,'347','Analytica Enterprise, (Windows), Version: 40100, Samplesize: 100','0',9,2817,1,
359,'359','Analytica Enterprise, (Windows), Version: 40100, Samplesize: 100','0',9,2817,1,
371,'371','Analytica Enterprise, (Windows), Version: 40100, Samplesize: 100','0',9,2817,1,
383,'383','Analytica Enterprise, (Windows), Version: 40100, Samplesize: 100','0',9,2817,1,
395,'395','Analytica Enterprise, (Windows), Version: 40100, Samplesize: 100','0',9,2817,1,
407,'407','Analytica Enterprise, (Windows), Version: 40100, Samplesize: 100','0',9,2817,1,
419,'419','Analytica Enterprise, (Windows), Version: 40100, Samplesize: 100','0',9,2817,1,
422,'Country','Country of observation','-',6,2664,1,
423,'Year','Year of observation','a',6,2664,1,
424,'Sex','Sex of a person','-',6,2664,1,
425,'Morbidity','Morbidity of a person','1/100000 py',6,2664,1,
426,'426','Analytica Enterprise, (Windows), Version: 40100, Samplesize: 100','0',9,2817,1,
433,'433','Analytica Enterprise, (Windows), Version: 40100, Samplesize: 100','0',9,2817,1,
434,'Op_fi2811','Morbidity in Europe','Bq or mg/kg f.w.',7,2811,1,
440,'440','Analytica Enterprise, (Windows), Version: 40100, Samplesize: 100','0',9,2817,1,
447,'447','Analytica Enterprise, (Windows), Version: 40100, Samplesize: 100','0',9,2817,1,
448,'Op_fi2818','Morbidity in Europe','a',1,2818,2,
453,'453','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 100','',9,2817,1,
459,'459','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 100','',9,2817,1,
465,'465','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 100','',9,2817,1,
471,'471','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 100','',9,2817,1,
477,'477','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 100','',9,2817,1,
478,'Op_en2943','EU ETS data from CITL','tonne of CO2-equ',1,2943,1,
480,'CITL sector','CITL sector','-',6,2664,1,
481,'CITL_information','CITL information','-',6,2664,1,
483,'483','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 100','',9,2817,1,
489,'489','Analytica ADE Optimizer, (Windows), Version: 40.2K, Samplesize: 100','',9,2817,1,
493,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
496,'Parameter1','’Parameter','# or 1/100000 py',6,2664,1,
497,'497','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 100','',9,2817,1,
504,'504','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 100','',9,2817,1,
511,'511','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 100','',9,2817,1,
518,'518','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 100','',9,2817,1,
519,'Op_en2987','Costs of unit emissions of air pollutants','e/kg',1,2987,1,
521,'521','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 1000','',9,2817,1,
527,'527','Analytica ADE Optimizer, (Windows), Version: 4.02e+004, Samplesize: 100','',9,2817,1,
529,'Op_en2988','Unit cost of driving','e/km',1,2988,1,
530,'Op_en2989','Emission factors of cars on air pollutants','g/km',1,2989,1,
533,'533','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 1000','',9,2817,1,
537,'Op_en2990','Traffic accidents in the Helsinki metropolitan area’','#/a',1,2990,1,
539,'Accidents','Accident type','-',6,2664,1,
541,'541','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 1000','',9,2817,1,
549,'549','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 1000','',9,2817,1,
557,'557','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 1000','',9,2817,1,
558,'Op_en2995','Total amount of car kilometres driven in the Helsinki metropolitan area','km/d',1,2995,1,
559,'559','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 1000','',9,2817,1,
561,'561','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 1000','',9,2817,1,
563,'563','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 1000','',9,2817,1,
565,'565','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 1000','',9,2817,1,
567,'Period1','Different times of day','h',6,2664,1,
568,'568','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 1000','',9,2817,1
)280,48,148,131,1,1,1,1,1,0,0,0,02,378,21,493,5012,152,162,1057,343,0,MIDM2,573,21,700,421,0,MIDM39325,65535,39321[O_j,O_i][O_j,O_i]['H1991'][Self,1,Sys_localindex('I'),1,Sys_localindex('J'),1]SettThis node checks the variables listed in Var_for_rdb and makes an index of those that are NOT found in the result database. This is then used as an index in Inp_var for adding variable information.Table(S_i,S_j)(
1,33,1,
2,37,1,
5,35,1,
6,34,1,
9,38,1,
10,39,1,
11,40,1,
12,32,1,
13,43,1,
14,44,1,
15,45,1,
16,46,1,
17,47,1,
18,48,1,
19,49,1,
23,51,1,
24,36,1,
28,42,1,
31,54,1,
35,114,3,
38,137,9,
37,114,4,
39,159,3,
41,160,9,
44,183,9,
47,191,9,
46,184,4,
45,184,3
)280,72,148,131,1,1,1,1,1,0,0,0,02,378,21,493,5012,529,143,700,421,0,MIDM39325,65535,39321[S_j,S_i][S_i,S_j]['H1991'][Self,1,Sys_localindex('I'),1,Sys_localindex('J'),1]ItemThis node checks the variables listed in Var_for_rdb and makes an index of those that are NOT found in the result database. This is then used as an index in Inp_var for adding variable information.Table(It_i,It_j)(
1,1,55,0,
2,2,56,0,
3,2,57,0,
4,2,58,0,
5,5,59,0,
6,6,60,0,
7,5,61,0,
8,6,62,0,
9,9,63,0,
10,11,65,0,
11,10,70,0,
12,13,71,0,
13,14,72,0,
14,15,73,0,
15,16,74,0,
16,17,75,0,
17,18,76,0,
18,19,77,0,
19,24,78,0,
20,23,79,0,
21,1,80,0,
22,1,81,0,
23,5,82,0,
24,1,83,0,
25,1,84,0,
26,6,85,0,
27,12,86,0,
28,28,87,0,
29,2,88,0,
30,24,89,0,
31,31,90,0,
32,24,91,0,
33,13,92,0,
34,2,93,0,
35,35,28,0
)280,120,148,131,1,1,1,1,1,0,0,0,02,378,21,493,5012,529,143,700,421,0,MIDM39325,65535,39321[It_j,It_i][It_i,It_j]['H1991'][Self,1,Sys_localindex('I'),1,Sys_localindex('J'),1]AssessmentDO NOT REMOVE THIS NODE. It is needed for computing the Objects node.ktluser29. Decta 2008 21:51 48,24168,256,152,121,11,11,550,300,17(var, table)Write1if size(var)>0 then appendtablesql(var,var.i, var.j, table&' ')56,368,148,132,687,61,476,224var,table''168,280,052,121,1,1,1,1,1,0,0,0,02,163,375,476,224[Formnode Writerpsswd1]52425,39321,65535PlatformChoice(Self,2,False,1)320,328,148,12[Formnode Platform1]52425,39321,65535['Lumina AWP','THL computer']Platform0308,364,1140,121,0,0,1,0,0,0,158,0,152425,39321,65535PlatformWriterpsswd0308,388,1140,121,0,0,1,0,0,0,158,0,152425,39321,65535WriterpsswdUpload an Analytica model. This functionality requires that you have an editable version of Opasnet Base Connection. The nodes must be found from within this same model.456,356,-1144,1161,0,0,1,0,1,0,,0,2,693,146,476,224