1. Aktivitetsbaseret modellering af transportefterspørgsel
Trafikdage 2016
Goran Vuk, Vejdirektoratet

2. Theory of travel demand
Person travel demand covers whole day
Spending family quality time at home impacts activities/travel of the household members
Person activities are executed in the HH-context
An additional activity requires re-scheduling of the existing plan; concept of time windows (Time-Space prism)
Travel choices are in function of the time/money budget (Time- Money constrains)
Goran Vuk – AB Demand Modelling
August 2016

3. COMPAS 2.0 Long Term Models Day Activity Models Tour Models
Trip Models
Goran Vuk – AB Demand Modelling
August 2016

4. COMPAS 2.0 Long Term Models Day Activity Models Tour Models
Trip Models
Goran Vuk – AB Demand Modelling
August 2016

5. COMPAS 2.0 long term decisions such as car ownership Long Term Models
Day Activity Models
Tour Models
Trip Models
Goran Vuk – AB Demand Modelling
August 2016

6. COMPAS 2.0 long term decisions such as car ownership Long Term Models
person day activity pattern:
Day Activity Models
Tour Models
Trip Models
Goran Vuk – AB Demand Modelling
August 2016

7. COMPAS 2.0 long term decisions such as car ownership Long Term Models
person day activity pattern: 1. work theater
Day Activity Models
Tour Models
Trip Models
Goran Vuk – AB Demand Modelling
August 2016

8. COMPAS 2.0 long term decisions such as car ownership Long Term Models
person day activity pattern: 1. work theater
1. home-work-supermarket-home
Day Activity Models
Tour Models
Trip Models
Goran Vuk – AB Demand Modelling
August 2016

9. COMPAS 2.0 long term decisions such as car ownership Long Term Models
person day activity pattern: 1. work theater
1. home-work-supermarket-home
- HH joint activities are also modelled
Day Activity Models
Tour Models
Trip Models
Goran Vuk – AB Demand Modelling
August 2016

10. COMPAS 2.0 long term decisions such as car ownership Long Term Models
person day activity pattern: 1. work theater
1. home-work-supermarket-home
- HH joint activities are also modelled
Day Activity Models
Tour Models
whole work tour: destination/mode/time
Trip Models
Goran Vuk – AB Demand Modelling
August 2016

11. COMPAS 2.0 long term decisions such as car ownership Long Term Models
person day activity pattern: 1. work theater
1. home-work-supermarket-home
- HH joint activities are also modelled
Day Activity Models
Tour Models
whole work tour: destination/mode/time
e.g. home-work trip:
destination/mode/time
Trip Models
Goran Vuk – AB Demand Modelling
August 2016

12. COMPAS 2.0 long term decisions such as car ownership
person day activity pattern: 1. work theater
1. home-work-supermarket-home
- HH joint activities are also modelled
whole work tour: destination/mode/time
e.g. home-work trip:
destination/mode/time
Goran Vuk – AB Demand Modelling
August 2016

13. COMPAS 2.0 Input: Synthetic pop. LoS Zonal data Long Term Models
Day Activity Models
Tour Models
Trip Models
Output:
Trip tables,
LogSums
Goran Vuk – AB Demand Modelling
August 2016

14. COMPAS 2.0; Long Term Models
1. Car ownership model
2. PT pass ownership model
(3. Work location model)
(4. School location model)
Goran Vuk – AB Demand Modelling
August 2016

15. COMPAS 2.0; Day Activity Models
Home based shared household activities PFPT model
Day activity pattern type model Household Day Pattern Type model
3. Person Day Pattern Type model
Work at home model Work at home model
Mandatory pattern model Mandatory tour generation model
6. Mandatory stop presence model
Half joint tour model Half joint tour generation model
8. Full joint half tour participation model
9. Partial joint half tour participation model
10. Partial joint half tour chauffeur model
Fully joint tour model Fully joint tour generation model
12. Fully joint tour participation model
Individual person day pattern model 13. Person day pattern model
14. Person tour generation model
Work based sub-tour generation model 15. Work based sub-tour generation model

16. COMPAS 2.0; Tour/Trip Models
Tour model 1. Tour mode time model
2. Tour destination model
Goran Vuk – AB Demand Modelling
August 2016

17. COMPAS 2.0; Tour/Trip Models
Tour model 1. Tour mode time model
2. Tour destination model
Trip model 3. Intermediate stop generation model
4. Intermediate stop destination/location model
5. Trip mode model
6. Trip time model
Goran Vuk – AB Demand Modelling
August 2016

18. COMPAS 2.0; Trip time sub-model
// return departure period constants
alternative.AddUtilityTerm(21, time.DeparturePeriod.Middle.IsBetween(Global.Settings.Times.ThreeAM, Global.Settings.Times.SevenAM).ToFlag());
alternative.AddUtilityTerm(22, time.DeparturePeriod.Middle.IsLeftExclusiveBetween(Global.Settings.Times.SevenAM, Global.Settings.Times.TenAM)
alternative.AddUtilityTerm(23, time.DeparturePeriod.Middle.IsLeftExclusiveBetween(Global.Settings.Times.TenAM, Global.Settings.Times.OnePM)
alternative.AddUtilityTerm(24, time.DeparturePeriod.Middle.IsLeftExclusiveBetween(Global.Settings.Times.OnePM, Global.Settings.Times.ThreePM)
alternative.AddUtilityTerm(124, time.DeparturePeriod.Middle.IsLeftExclusiveBetween(Global.Settings.Times.ThreePM, Global.Settings.Times.Four
alternative.AddUtilityTerm(25, time.DeparturePeriod.Middle.IsLeftExclusiveBetween(Global.Settings.Times.FourPM, Global.Settings.Times.FivePM)
alternative.AddUtilityTerm(26, time.DeparturePeriod.Middle.IsLeftExclusiveBetween(Global.Settings.Times.FivePM, Global.Settings.Times.SixPM)
alternative.AddUtilityTerm(27, time.DeparturePeriod.Middle.IsLeftExclusiveBetween(Global.Settings.Times.SixPM, Global.Settings.Times.SevenPM)
alternative.AddUtilityTerm(28, time.DeparturePeriod.Middle.IsLeftExclusiveBetween(Global.Settings.Times.SevenPM, Global.Settings.Times.NinePM
alternative.AddUtilityTerm(29, time.DeparturePeriod.Middle.IsLeftExclusiveBetween(Global.Settings.Times.NinePM, Global.Settings.Times.Midnigh
alternative.AddUtilityTerm(30, time.DeparturePeriod.Middle.IsLeftExclusiveBetween(Global.Settings.Times.Midnight, Global.Settings.Times.Minu
alternative.AddUtilityTerm(31, durationShiftMinutes.IsRightExclusiveBetween(Global.Settings.Times.ZeroHours, Global.Settings.Times.OneHour)
alternative.AddUtilityTerm(32, durationShiftMinutes.IsRightExclusiveBetween(Global.Settings.Times.OneHour, Global.Settings.Times.TwoHours)
alternative.AddUtilityTerm(33, durationShiftMinutes.IsRightExclusiveBetween(Global.Settings.Times.TwoHours, Global.Settings.Times.ThreeHours
alternative.AddUtilityTerm(34, durationShiftMinutes.IsRightExclusiveBetween(Global.Settings.Times.ThreeHours, Global.Settings.Times.FiveHours
alternative.AddUtilityTerm(35, durationShiftMinutes.IsRightExclusiveBetween(Global.Settings.Times.FiveHours, Global.Settings.Times.SevenHours
alternative.AddUtilityTerm(36, durationShiftMinutes.IsRightExclusiveBetween(Global.Settings.Times.SevenHours, Global.Settings.Times.NineHours
alternative.AddUtilityTerm(37, durationShiftMinutes.IsRightExclusiveBetween(Global.Settings.Times.NineHours, Global.Settings.Times.TwelveHour
alternative.AddUtilityTerm(38, durationShiftMinutes.IsRightExclusiveBetween(Global.Settings.Times.TwelveHours, Global.Settings.Times.Fourteen
alternative.AddUtilityTerm(39, durationShiftMinutes.IsRightExclusiveBetween(Global.Settings.Times.FourteenHours, Global.Settings.Times.Eighte
alternative.AddUtilityTerm(40, (durationShiftMinutes >= Global.Settings.Times.EighteenHours).ToFlag());
alternative.AddUtilityTerm(41, partTimeWorkerFlag * departureShiftHours);
alternative.AddUtilityTerm(43, nonworkingAdultFlag * departureShiftHours);
alternative.AddUtilityTerm(45, universityStudentFlag * departureShiftHours);
alternative.AddUtilityTerm(47, retiredAdultFlag * departureShiftHours);
alternative.AddUtilityTerm(49, femaleFlag * departureShiftHours);
alternative.AddUtilityTerm(51, childAge5Through15Flag * departureShiftHours);
alternative.AddUtilityTerm(53, childUnder5Flag * departureShiftHours);
alternative.AddUtilityTerm(61, jointTourFlag * departureShiftHours);
alternative.AddUtilityTerm(67, primaryFamilyTimeFlag * departureShiftHours);
Goran Vuk – AB Demand Modelling
August 2016

19. COMPAS 2.0; Trip time sub-model
Iteration 1 Function = Conv.(3) = 15.8
Iteration 2 Not improving
Iteration 3 Function = Conv.(3) = 12.1
Iteration 4 Not improving
Iteration 5 Function = Conv.(3) = 9.48
Iteration 6 Function = Conv.(3) = 4.20
Iteration 7 Function = Conv.(3) = .916
Iteration 8 Function = Conv.(3) = .262
Iteration 9 Function = Conv.(3) = .296E-01
Iteration 10 Function = Conv.(3) = .412E-03
Convergence achieved after 10 iterations
Analysis is based on observations
Likelihood with Zero Coefficients =
Likelihood with Constants only =
Initial Likelihood =
Final value of Likelihood =
Rho-Squared =
Goran Vuk – AB Demand Modelling
August 2016

20. COMPAS 2.0; Trip time sub-model
82 coefficients estimated:
Arrival times, e.g. 6-7 am
Duration times
Socio-economy, e.g. full-time worker, female, students
Travel purpose, e.g. work, shopping, escort
Goran Vuk – AB Demand Modelling
August 2016

21. COMPAS 2.0; Trip time sub-model
82 coefficients estimated:
Arrival times, e.g. 6-7 am
Duration times
Socio-economy, e.g. full-time worker, female, students
Travel purpose, e.g. work, shopping, escort
PFPT-starttime PFPT-duration
Estimate
Std. Error
"T" Ratio
Goran Vuk – AB Demand Modelling
August 2016

22. COMPAS 2.0; calibration Day Patterns Tours Trips
Goran Vuk – AB Demand Modelling
August 2016

23. COMPAS 2.0; calibration Calibration of COMPAS 1.1 (Sep. 2013):
Total trips == (as in OTM 5.4)
Bike trips == (as in OTM 5.4)
Walk trips == (as in OTM 5.4)
Calibration of COMPAS 2.0 (Aug. 2016), first run:
Total trips ==
Bike trips ==
Walk trips ==
Day Patterns
Tours
Trips
Goran Vuk – AB Demand Modelling
August 2016

24. Case 1: Modelling of biking
OTM 6
1. Distance
Goran Vuk – AB Demand Modelling
August 2016

25. Case 1: Modelling of biking
OTM 6
OTM 7
1. Distance
1. FF travel time
2. CGN travel time
3. Rutevalg;
grønne områder
vandet
stigning på ruten
4. Demand;
alder
køn
beskæftigelse …
Goran Vuk – AB Demand Modelling
August 2016

26. Case 1: Modelling of biking
OTM 6
OTM 7
COMPAS 2.0
1. Distance
1. FF travel time
2. CGN travel time
3. (Rutevalg) - grønne områder, vandet, stigning på ruten
4. (Demand) - socioøkonomiske baggrundsdata – alder, køn, beskæftigelse, …
1. Time constraints affect mode choice
2. Tour mode constrains trip mode for intermediate stops on tours
3. Bike mode probability depends on purpose
4. Joint travel modelled
5. Destination model based on zones. LOS files provide distance between zones on a bike-specific network.
6. Bike-PT mode choices: a) bike(park)-PT-walk; b) bike(park)-PT-bike; c) bike on board PT, d) walk-PT-bike
7. Day-level choices, such as tour generation (induced traffic), depend on bike LOS in neighborhood surrounding home or workplace
Goran Vuk – AB Demand Modelling
August 2016

27. Case 2: Modelling of road pricing
In OTM, a road pricing policy impacts:
Induced traffic
Dest. Choice, Modal split, and ToD
Goran Vuk – AB Demand Modelling
August 2016

28. Case 2: Modelling of road pricing
In COMPAS, a road pricing policy impacts:
Long term decisions (e.g. car ownership)
Staying (and working) at home
Activity pattern for the whole day
Activity pattern for a particular tour (e.g. home-work)
Joint activities
In-home family quality time (see next slide)
induced traffic, dest. choice, modal split, and ToD (as in OTM)
Goran Vuk – AB Demand Modelling
August 2016

29. Case 2: Modelling of road pricing
In COMPAS, a road pricing policy impacts:
Long term decisions (e.g. car ownership)
Staying (and working) at home
Activity pattern for the whole day
Activity pattern for a particular tour (e.g. home-work)
Joint activities
In-home family quality time (see next slide)
induced traffic, dest. choice, modal split, and ToD (as in OTM)
The effect of road pricing is larger in OTM than in COMPAS
Goran Vuk – AB Demand Modelling
August 2016

30. Case 2: Modelling of road pricing
Goran Vuk – AB Demand Modelling
August 2016

31. COMPAS 2.0 DaySim Visualizer
Goran Vuk – AB Demand Modelling
August 2016