Log
Trip rate on a workday in HMA
ktluser
10. Novta 2008 7:18
ktluser
10. Novta 2008 19:10
48,24
0,Linkmodule Trip_rate_on_a_workd,2,2,-32766,1,Trip rate on a workday in HMA.ANA
Unadjusted trip rate
trips/time unit
Calculates 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:= Trips_by_hour[Reg=From,Reg1=To1];
c:= if c=null then 0 else c;
c:= cubicinterp(hour,c,time,hour)
208,104,1
48,24
2,454,141,476,358
2,151,124,782,471,0,MIDM
[Time,From]
[To1,From]
[Time,4,From,1,To1,1]
Trip rate on a workday in HMA
trips/time unit
Calculates 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.
8.11.2008 Jouni Tuomisto
Note that the adjustment is turned off in this node. I don't remember when this has been done and why.
<a href="http://en.opasnet.org/w/Trip rate on a workday in the Helsinki metropolitan area">Opasnet variable</a>
<a href="http://www.pyrkilo.fi/resultdb/index.php?page_id=2625&wiki_id=1&sample=1">Result from the Result database</a>
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
208,168,1
48,29
1,1,1,1,1,1,0,,1,
2,26,34,476,616
2,578,39,507,476,0,MIDM
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]
[Time,From]
[To1,From]
[Time,5,From,1,To1,1]
Total trips
trips
Total 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 taxi
2.9M*0.44
96,168,1
48,24
2,102,90,476,478
65535,52427,65534
YTV: 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>
Modelled trip rate
jtue
13. Febta 2003 16:03
ktluser
25. touta 2005 12:30
48,24
208,40,1
48,24
1,1,1,1,1,1,0,0,0,0
1,85,42,656,530,17
Arial, 13
Hour
Hour of day.
Sequence( 0, 23 )
400,272,1
48,12
1,1,1,1,1,1,0,,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]
(param1, param2;suurind,pienind:indextype;indtieto)
Normitus
A function used to divide aggragate data into its disaggregate units based on weighting factors.
using a:=sum((if indtieto=Suurind then param1 else 0), pienind)
do using b:= sum((if indtieto=suurind then a else 0), suurind)
do param2/b
168,368,1
48,24
2,591,58,476,514
param1,param2,suurind,pienind,indtieto
(param1, param2; suurind, pienind:indextype;indtieto)
Si_pi
A function used to divide aggragate data into its disaggregate units based on weighting factors.
using a:= Normitus(param2,param2,suurind,pienind,indtieto)
do using b:= (if indtieto=suurind then param1*a else 0)
do using c:= sum(b, suurind)
do c
168,424,1
48,24
2,36,83,476,312
param1,param2,suurind,pienind,indtieto
Trips municipality
1000 tips/d
One-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
)
56,64,1
48,24
2,422,91,476,513
1,77,139,758,383,0,MIDM
2,52,332,708,188,0,MIDM
65535,52427,65534
[Self,Municipality1]
[Municipality,Municipality1]
[Index Suuralue]
YTV: Liikkumisen nykytila. PŠŠkaupunkiseudun 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 place
1000 trips/d
One-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
)
56,176,1
48,24
2,402,104,476,603
2,44,37,504,196,0,MIDM
65535,52427,65534
[Place,Place1]
[Place,Place1]
[Index Kohde]
YTV: Liikkumisen nykytila. PŠŠkaupunkiseudun 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&mode
fraction
The 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
)
64,288,1
48,24
2,377,111,476,441
1,494,125,416,303,0,MIDM
2,27,185,456,199,0,MIDM
65535,52427,65534
[Mode2,Place1]
[Place,Place1]
[Index Kohde]
YTV: Liikkumisen nykytila. PŠŠkaupunkiseudun 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&mode
trips/d/inh
Number 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
)
64,368,1
48,24
2,491,162,476,551
1,136,146,595,314,0,MIDM
2,30,208,649,187,0,MIDM
65535,52427,65534
[Mode2,Self]
[Municipality,Mode2]
[Index Suuralue]
YTV: Liikkumisen nykytila. PŠŠkaupunkiseudun 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 munic
fraction
The 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
)
64,424,1
48,24
2,102,90,476,471
1,200,210,666,291,0,MIDM
2,200,210,752,301,0,MIDM
65535,52427,65534
[Self,Municipality1]
[Municipality,Municipality1]
YTV: Liikkumisen nykytila. PŠŠkaupunkiseudun 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 hour
A 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)
504,96,1
48,24
2,667,115,476,570
2,400,26,509,574,0,MIDM
52425,39321,65535
[Place1,Hour]
[Index Tunti]
Municipality
Municipalities in the Helsinki metropolitan area. Helsinki is divided into two parts; Kauniainen is together with Espoo.
['Helsinki, downtown','Helsinki, suburbs','Espoo, Kauniainen','Vantaa']
56,96,1
48,12
2,243,104,476,437
2,17,221,416,303,0,MIDM
['Helsinki, downtown','Helsinki, suburbs','Espoo, Kauniainen','Vantaa']
Municipality1
The same as Municipality; this index is used as the destination.
copyindex(Municipality)
56,120,1
48,12
2,451,144,476,421
2,72,82,416,303,0,MIDM
['Helsinki, downtown','Helsinki, suburbs','Espoo, Kauniainen','Vantaa']
Place
The 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']
56,208,1
48,12
2,704,209,476,464
['Home','Workplace','Business','School','Other']
Place1
The place where the trip ends.
copyindex(Place)
56,232,1
48,12
2,120,130,416,303,0,MIDM
['Home','Workplace','Business','School','Other']
Mode
The modes of transportation.
['Kevyt liikenne','Joukkoliikenne','Henkilšauto','Muu']
64,320,1
48,12
2,102,90,476,446
['Kevyt liikenne','Joukkoliikenne','Henkilšauto','Muu']
Time of day
Time of day
['Morning','Day','Afternoon','Evening','Night']
504,128,1
48,12
2,102,90,476,423
Time in traffic
min/h
Time 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)
400,240,1
48,24
2,161,264,476,428
2,136,28,416,569,0,MIDM
2,288,21,316,544,0,MIDM
65535,52427,65534
[Index Tunti]
Anu Kousa, Expolis database 12.11.2002.
Car trips
trips/d
Car trips per day.
var a:= Trips_place*Trips_place_mode*1000;
a[Mode2='Henkilšauto']
176,176,1
48,24
2,108,133,476,462
2,32,12,489,204,0,MIDM
[Place,Place1]
Time of day by hour
Time of day by hour
Table(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')
504,32,1
48,24
2,18,279,476,224
2,884,115,416,538,0,MIDM
2,56,66,416,303,0,MIDM
52425,39321,65535
Inhabitants
#
Number of inhabitants by district in Jan 1st, 2001.
Table(Area1)(
389,10.248K,8215,882,6768,4157,11.62K,761,2407,3401,13.137K,14.569K,8705,6832,4746,10,3542,2284,15.89K,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)
400,32,1
48,24
2,102,90,476,492
1,216,226,703,303,0,MIDM
2,489,294,416,303,0,MIDM
65535,52427,65534
SeutuCD 02, a CD ROM database about the Helsinki area.
Workplaces
#
The number of workplaces by district
Table(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)
288,32,1
48,24
2,102,90,476,548
1,248,258,713,303,0,MIDM
2,583,35,416,303,0,MIDM
65535,52427,65534
SeutuCD 02, a CD ROM database about the Helsinki area.
Municipality info
The 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)
184,32,1
48,24
2,102,90,476,356
2,696,93,416,532,0,MIDM
52425,39321,65535
Trips place munic
trips/d
Car 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)
288,176,1
48,24
2,16,104,498,591
1,339,342,644,303,0,MIDM
2,49,67,784,245,0,MIDM
[Place,Place1]
[Municipality1,Municipality]
[Index Suuralue]
Trips by hour
trips/h
Trips 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=reg1],Municipality1,reg1,Municipality_info[area1=reg1]);
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);
a[area1=reg]
400,176,1
48,24
2,38,32,562,688
2,571,93,540,493,0,MIDM
[Reg1,Reg]