Aktivitetsbaseret modellering af transportefterspørgsel Trafikdage 2016 Goran Vuk, Vejdirektoratet

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

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

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

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

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

COMPAS 2.0 long term decisions such as car ownership Long Term Models person day activity pattern: 1. work 2. theater Day Activity Models Tour Models Trip Models Goran Vuk – AB Demand Modelling August 2016

COMPAS 2.0 long term decisions such as car ownership Long Term Models person day activity pattern: 1. work 2. theater 1. home-work-supermarket-home Day Activity Models Tour Models Trip Models Goran Vuk – AB Demand Modelling August 2016

COMPAS 2.0 long term decisions such as car ownership Long Term Models person day activity pattern: 1. work 2. 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

COMPAS 2.0 long term decisions such as car ownership Long Term Models person day activity pattern: 1. work 2. 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

COMPAS 2.0 long term decisions such as car ownership Long Term Models person day activity pattern: 1. work 2. 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

COMPAS 2.0 long term decisions such as car ownership person day activity pattern: 1. work 2. 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

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

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

COMPAS 2.0; Day Activity Models Home based shared household activities 1. PFPT model Day activity pattern type model 2. Household Day Pattern Type model 3. Person Day Pattern Type model Work at home model 4. Work at home model Mandatory pattern model 5. Mandatory tour generation model 6. Mandatory stop presence model Half joint tour model 7. 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 11. 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

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

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

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

COMPAS 2.0; Trip time sub-model Iteration 1 Function = -12386.2688 Conv.(3) = 15.8 Iteration 2 Not improving Iteration 3 Function = -12369.9597 Conv.(3) = 12.1 Iteration 4 Not improving Iteration 5 Function = -12360.2800 Conv.(3) = 9.48 Iteration 6 Function = -12349.8016 Conv.(3) = 4.20 Iteration 7 Function = -12340.8360 Conv.(3) = .916 Iteration 8 Function = -12340.3511 Conv.(3) = .262 Iteration 9 Function = -12340.3144 Conv.(3) = .296E-01 Iteration 10 Function = -12340.3140 Conv.(3) = .412E-03 Convergence achieved after 10 iterations Analysis is based on 4.758 observations Likelihood with Zero Coefficients = -14802.7451 Likelihood with Constants only = -13964.0398 Initial Likelihood = -12386.2688 Final value of Likelihood = -12340.3140 Rho-Squared = .1663 Goran Vuk – AB Demand Modelling August 2016

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

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 .0519 .1042 Std. Error .0405 .0407 "T" Ratio 1.3 2.6 Goran Vuk – AB Demand Modelling August 2016

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

COMPAS 2.0; calibration Calibration of COMPAS 1.1 (Sep. 2013): Total trips == 5.933.010 (as in OTM 5.4) Bike trips == 1.101.669 (as in OTM 5.4) Walk trips == 910.175 (as in OTM 5.4) Calibration of COMPAS 2.0 (Aug. 2016), first run: Total trips == 7.678.800 Bike trips == 2.808.300 Walk trips == 2.733.600 Day Patterns Tours Trips Goran Vuk – AB Demand Modelling August 2016

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

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

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 9.700 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

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

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

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

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

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