2
0
1
1
4
2
0
-1
Time
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.
[0,1,2]
Congestion charge case study
ktluser
17. touta 2007 22:41
ktluser
21. touta 2007 11:10
48,24
1,40,0,740,544,17
Arial, 12
0,Model Congestion_charge_ca,2,2,0,1,E:\Congestion_charge_case_study.ANA
Current graph
ktluser
21. touta 2007 9:30
48,24
464,40,1
48,24
1,40,0,933,544,17
Actions
56,96,-1
48,24
Valuation
736,96,-1
48,24
Impacts
560,96,-1
48,24
Exposure
400,96,-1
48,24
Media
288,96,-1
48,24
Sources
176,96,-1
48,24
Congestion charging
0
56,280,1
48,24
Noise levels
Noise_emission;
Landuse;
Background_conc;
Geography;
Meteorology
288,152,1
48,24
Annoyance
Noise_levels1;
Vehicle_speed
400,192,1
48,24
Air pollution emissions
Vehicle_number;
Traffic_composition;
Traffic_speed;
Road_data;
Traffic_volume
176,248,1
48,24
Cardiovascular illness
Annoyance;
Noise_levels1;
Existing_dose_respon;
Population
624,152,1
52,24
Air quality related morbidity /mortality
Air_pollution_emissi;
Existing_dose_respon;
Population
624,248,1
48,36
Health gains related to physical activity: diabetes, obesity
Physical_activity;
Existing_dose_respon;
Population
624,328,1
56,36
Psycho-social well-being
Annoyance;
Air_pollution_emissi;
Amount_of_public_tra;
Accidents1
624,424,1
48,24
Injury-related morbidity /mortality
Amount_of_public_tra;
Accidents1;
Existing_dose_respon;
Population
624,488,1
48,28
Resident and public transport areas
Vehicle_number
232,320,1
48,38
Amount of public transport
Resident_and_public_
232,424,1
48,28
Driver behaviour
Vehicle_number;
Vehicle_speed
232,488,1
48,24
Accidents
Resident_and_public_;
Enviroment_for_pedes;
Vehicle_speed
512,488,1
48,24
Enviroment for pedestrians /cyclists
Driver_behaviour
400,488,1
48,28
Congestion, travel time
Vehicle_speed
56,488,1
48,24
Local and global environmental impacts
Congestion_charging
56,168,1
48,36
New variables merged into the graph.
480,32,-1
128,26
Context
Spatial aspects (infrastructure)
Social, demografic, economic processes
168,32,-1
156,28
Car type
['Private car','Goods transport vehicle']
176,184,1
48,12
Car type: used in emission variables
- private car
-goods transport vehicle
304,808,-1
92,34
Polluntant
['PM2.5','NOx']
176,208,1
48,12
Pollutant: used in emission variables
- PM2.5
- NOx
504,808,-1
92,34
Group
['Residents','Workers','Commuters']
624,664,1
48,12
Group: Used in population variables
- residents
- workers
- commuters
104,800,-1
96,42
Population
0
624,632,1
48,24
Non-accidental morbidity /mortality
Cardiovascular_illn1;
Air_quality_related1;
Health_gains_relate1
736,248,1
52,28
Existing dose-response relations
0
624,576,1
52,28
[Object Variable]
[, , , , ]
Cycling
Congestion_charging
344,392,1
48,24
19661,48336,65535
Vehicle number
Congestion_charging
56,360,1
48,24
Vehicle speed
Vehicle_number
56,424,1
48,24
[]
Traffic composition
0
176,560,1
48,24
[]
Traffic speed
0
176,616,1
48,24
[]
Road data
0
176,672,1
48,24
[]
Traffic volume
0
176,728,1
48,24
[]
Geography
0
288,672,1
48,24
Objective Pm10_conc, Objective Nox_conc, Objective Noise_levels, Objective Accident_rate
Landuse
0
288,560,1
48,24
Objective Pm10_conc, Objective Nox_conc, Objective Noise_levels
Meteorology
0
288,728,1
48,24
Objective Pm10_conc, Objective Nox_conc
Background conc
0
288,616,1
48,24
Objective Noise_levels
Travel surveys
0
400,560,1
48,24
Variable Working_population, Variable Commuters
Time-Activity data
0
400,616,1
48,24
Variable Working_population, Variable Commuters, Objective Resident_population
Origin - Destination matrices
0
400,672,1
48,28
Variable Working_population, Variable Commuters, Objective Resident_population
Regional air monitoring
0
400,728,1
48,24
Air pollution exposure
Air_pollution_concen;
Travel_surveys;
Time_activity_data;
Origin___destination;
Regional_air_monitor
400,248,1
48,24
Noise emission
Vehicle_number;
Traffic_composition;
Traffic_speed;
Road_data;
Traffic_volume
176,152,1
48,24
Air pollution concentration
Air_pollution_emissi;
Landuse;
Background_conc;
Geography;
Meteorology
288,248,1
48,24
Physical activity
Resident_and_public_;
Cycling
400,320,1
48,24
Disease
['Cardiovascular','Diabetes','Obesity','Other morbidity','Other mortality']
736,288,1
48,12
Disease: used with non-accidental morbidity and mortality:
- Cardiovascular
- Diabetes
- Obesity
- Other morbidity
- Other mortality
696,760,-1
92,68
Key variable
56,112,1
48,24
Indicator
56,176,1
48,24
Proxy
56,240,1
48,24
Variable
56,40,1
48,24
39321,65535,65535
NOTE! This notation is actually for an argument. It is often the case that an argument can be used as a proxy to defend the result of a variable. However, it is not always the same thing.
328,248,-1
208,28
Data variable
56,312,1
48,24
65535,52427,65534
This is used for variables that contain measured data. Sometimes proxies can be of this form: they are measurement of something that is a variable, but it is not THE variable that we are interested, and it is used as a proxy. However, if the data is about the actual variable, this notation is not a proxy.
344,320,-1
228,32
2,462,63,476,224
It should be noted that the current notation in Analytica pyrkilo is about the content of the variable, not about its purpose. It is therefore problematic to represent the ideas of proxy and key variable in a coherent way with the existing notation.
336,120,-1
212,32