ActivityActivity--Based Based Travel Demand ModelsTravel Demand Models
Understanding Travel Behavior
Derivednatureoftraveldemand(Jones,1979) Travelgenerallyundertakentofulfillactivityneedsanddesires Activitiesdistributedintimeandspacenecessitatingtravel
Desiretraveldemandmodelsthatreflectthisfundamentalnotion
Roots of Activity Based Paradigm
Microsimulation ofindividualactivitytravelpatterns
Bringtogethertwoschoolsofthought Hgerstrands ConstraintsSchool(1969) ChapinsActivitypull(NeedsandDesires)School(1971and1974)
The Perfect Storm
Whyisvisionbeingrealizednow? Disaggregatemodelingoftravelbehavior Advancesinstatisticalandeconometricestimationmethods
Quantumleapsincomputationalpower Policyquestionswithcomplexbehavioralimplications Availabilityofdatabases(landuse,travelbehavior,GPS,networks)
An Evolutionary Stream of Research Chapin(1971and1974)studiedhumanactivitypatternsinurbancontexts
Hgerstrand (1969)examinedactivitypatternsinthetimespacedomain(continuum)andidentifiedseriesofconstraints
Activitybasedanalysistracesitsrootstotheseschoolsofthought
Hgerstrands Constraints
Threeprimarytypesofconstraintsidentified Authoritative:Timespaceconstraints Capability:Biologicalneedsandresources(sleep,income)
Coupling:Interagentinteractions(children)
Activity-Based Paradigm in Transportation
Jones(1979)explicitlyidentifiedrelationshipsamongactivities,travel,time,andspace Traveldemandisaderiveddemand
Followedwithaconferencein1981onTravelDemandAnalysis:ActivityBasedandOtherNewApproaches
Impetusgrewinlate1980sandinto1990s KeylegislativeactssuchasISTEA(1991),CAAA(1990),andTEA21(1998)
MajorlawsuitfiledbySierraClubagainstSanFranciscoBayAreaMTC
FederalTravelModelImprovementProgram(TMIP)
Activity-Based Paradigm in Transportation
EarlyresearcheffortsunderTMIP DevelopmentofTRANSIMSatLosAlamosNationalLaboratory Activitybasedmodeldevelopmentresearch(AMOS)
FourkeypaperspublishedinTransportation(1996)offeredframeworksforactivitymodels
Activity-Based Paradigm in Transportation
FourpapersinTransportation LateKitamura/Pas BenAkiva/Bowman Stopher/Hartgen Slavin
Otherkeypublications Kitamura(1988);Axhausen andGrling (1992);Jonesetal(1990);Bhat andKoppelman (1999)
Activity-Based Paradigm in Transportation
Otherkeyevents 1996ActivityBasedModelingConference(NewOrleans)organizedbyTMIP everybodyfromMissouri?
1995and2004NetherlandsConferencesinProgressinActivityBasedAnalysis
TRBTaskForceonMovingActivityBasedApproachestoPractice(20032008) chairedbyProf.KostasGoulias
Activity-Based Paradigm in Transportation
A Daily Activity Itinerary
Home
WorkRestaurant
Shop
Kids School7:15 am
7:30 am
8:00 am
7:35 am
12:30 pm12:35 pm
1:00 pm
1:05 pm5:00 pm
5:30 pm6:00 pm
6:30 pm
drivedrive
drive drive
walk
walk
Interactions and Constraints Howdoesafoursteptripbasedmodelviewthisitinerary?
Drive
Drive
Drive
Drive
Walk
Walk
O1 D1
O2
O3
O4
O5
O6
D2
D3
D4
D5
D6
Two Home-based tripsFour Non home-based trips
Peak
Peak
Peak
Peak
Off Peak
Off Peak
Influence of Trip Chaining Consideratransitenhancement
Work
Shopping
Drive alone
Drive alone Drive alone
Enhanced Transit Service
Home
Influence of Trip Chaining Howcanaswitchtotransitbeaccommodated?
Shopping
TransitDrive alone
Drive alone
Home Work
Transit
Activity-Travel Inter-relationships
Noteseriesofinterrelatedbehavioralresponses Shiftinmodechoicetowork Shiftindestinationchoiceforshopping Shiftintimingofshoppingactivity Impactontripgeneration(4tripsinsteadof3trips)
Limits of Four-Step Trip-Based Models
Considerachangeinsystemconditions Increaseincapacity Increaseincongestion(traveltime) Changeinfuelprice
Whatdopeopledo? AccordingtoUSAToday/GallopPollinJune2008,>80%consolidateerrands Directimpactontripgeneration
However,activityparticipationremainedlargelyunaltered
Tripgenerationmodelsinpractice Largelyinsensitivetosystemconditions
Limits of Four-Step Trip-Based Models
Insensitivityoftripgenerationtosystemconditions Inabilitytomodelsuppresseddemandorinduceddemand
Couldincorporateaccessibilitymeasuresintripgenerationmodels,but Stillverylimitedinabilitytoreflectinteractionsandconstraints
Limits of Four-Step Trip-Based Models
Social Equity and Quality of Life Issues
Qualityoflifetightlyconnectedtohumanactivitypatternsandhowpeoplespendtime
Activitybasedparadigmoffersabilitytoconstructutilitymeasuresthatdirectlyaddresstheseissues
Planning Issues Traditional/novelmultimodalcapacityadditions/subtractions Transit/PedestrianOrientedDevelopment Bicyclefacilityenhancements HOV/HOTlanes,CongestionPricing,VariablePricing,Parking
Pricing
Telecommuting(Telecommunications),FlexibleWorkSchedules ITSdeployments Equity,SocialExclusion,EnvironmentalJustice Energy(FuelPrices)andEnvironment(AirQuality) Homelandsecurityanddisastermanagement
Basis for Model Design Policy issuesandquestionsofinterest Realisticbehavioral paradigm/representation Computationallyfeasibleandtractable
Estimation Implementation
Data availability(presentandfuture)
A Focus on Behavioral Considerations
Multitudeofchoicesdefineactivitytravelbehavior Activitytype/purpose Activitytiming(timeofday) Travelmodeanddestination Activityduration Activitylinkage(tripchaining) Accompanyingpersons Networklevelchoices
Behavioral Decision Processes Multitudeofdecisionhierarchiespossible
Whatisthesequenceinwhichchoicesaremade? Virtuallyallmodelsystemsimplyacertaindecisionhierarchy
Towhatextentarechoicesmadesequentiallyversussimultaneously/jointly?
Decision Hierarchies Largevarietyofdecisionhierarchiespossible
Heterogeneityinthepopulation Carefulmarketsegmentationbasedondecisionprocesses
Growingevidenceofsimultaneityinchoicedecisions Peoplechooseanactivitytravel(lifestyle)package
Ifchoiceprocessissequential,moreconstrainedchoiceprecedeslessconstrainedchoice Inhouseholdwithvehicleownershipconstraints,wouldmodechoice
precededestinationchoice?
The Role of Time
Thenotionoftimeandtimeuseiscentraltotheactivitybasedmodelingparadigm Timeisnotjustacost tobeminimized Rather,itisafiniteresourcewhoseuse peoplestrivetooptimize
Timeisanallencompassingentityinactivitybasedmodels
Timeappearsinactivitytravelagendasinnumerousways Dailytimeallocationtoactivitiesandtravel Thedurationofsingleactivityandtravelepisodes Thetiming(timeofday)ofactivities/trips Multiday(weekly)activityscheduling
The Role of Time
Considerationofrelationshipsbetweeninhomeandoutofhomeactivitytimeuse
Evidenceofincreasedavailabilityofleisuretime Evidenceofincreaseintraveltimeexpenditures
Productivityefficienciesbroughtaboutbyspecializedservicesandtechnologydeployment
The Role of Time
Dopeopletreattimeasacontinuousentityoradiscreteentity? Discretetimeofdaychoicemodels(breakthedayintodiscreteperiods)
Continuousdurationmodelswhereactivitytimingismodeledalongthecontinuoustimeaxis
Schedulingmaybediscretewhiletimeallocationmaybecontinuous
The Role of Time
Agent Interactions Taskallocationandjointactivitytravelengagement
Withwhomandforwhom? Activitydependency(children) Householdvehicleallocation Residentialandworkplacelocationchoices Realtimeactivityscheduling
Influenceofmobiletechnologies Generateactivitiesontheflyinmodel?
Time-Space Interactions
Gainrealismbyincorporatingtimespaceprismconstraints
Constraintsonmodaltransition,publictransitavailability,anddestinationchoices
Generatework/schoolschedulesandtoursfirst(defineanchorpoints) Discretionaryactivitiessimulatedalongthetimeaxisrecognizingconstraintsimposedbyworkandschool
PrismConstrainedActivityTravelSimulator(PCATS)ofKitamura nowembeddedinOpenAMOS
Dividesadayintoopenperiodsandblockedperiods DefinesaHgerstrandsprismforeachopenperiodandsimulatesactivitiesandtravelwithinit
Time-Space Interactions
Home Work
Activity 1 (Fixed)
Activity 2 (Fixed)
T
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m
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Urban Space
1
vHome Activity
A
Activity atLocation A
Activity 1
Activity 2
Time-Space Interactions
Sequentialstructure Theattributesofanactivityandtriptoitaresimulatedactivitybyactivity,conditionalonpastactivityengagement
Operationalhierarchy:activitytypemodedestinationactivityduration
Representationofprismconstraints Activitytypechoice/generation remainingtimeinprism Modedestinationchoice constrainedchoiceset Durationchoice remainingtimeinprism
Time-Space Interactions
Decision Time Points for Discretionary Activities
Decision Point 1
Time
Open Period
Decision Point 2 Decision Point 3
Travel
Travel
Travel
Fixed ActivityActivity 1 Activity 2
Blocked Period
PCATS Model Components
Prismvertexmodels Stochasticfrontiermodelstodetermineunobservedprismvertices
Activitytypechoicemodels Multinomiallogitmodelsthatdetermineactivityengagementineachopenperiod
DestinationModechoicemodels Nestedlogitmodelsthatassignadestinationmodepairtoeachactivitywithinaprism
Activitydurationmodels Splitpopulationsurvivalmodelsthatdeterminelengthofeachactivitywhileconsideringtheprismsize
PCATS Model Components
Representing Prism Constraints
Home Work Urban Space
In-home Activity
T
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1v
PM Work Activity
In-home Activity
AM Work Activity
Prism vertices generated by stochastic frontier models
A prism configured assuming the fastest travel mode in the choice set
Travel mode availability by time of day and mode continuity checked within and across prisms
Activity Generation Process
Time Left for Activities?
Activity Type Choice
Destination-Mode Choice
Activity Duration Choice
More Activities?
Mode Choice (to Fixed Activity)
Adjust Activity Duration
Mode Choice toNext Fixed Activity
YesNo
YesNo
Open Period Begins
Open Period Ends
Prism ConstraintsPrism Constraints
Prism ConstraintsPrism Constraints
Prism ConstraintsPrism Constraints
Prism ConstraintsPrism Constraints
Prism ConstraintsPrism Constraints
Integrate with Dynamic Traffic Simulator
Maximizeuseofinformationfromactivitybasedmodelsystem Activitiesandtripsgeneratedalongthecontinuoustimeaxis Loadtripsonthenetworkastheyaregenerated(atoneminuteresolution)
Dynamicinterfaceandconcurrentexecution,alongthetimeaxis,oftheactivitysimulatorandanetworksimulator
Nopostprocessingofmodeloutputs
Integration with Traffic SimulatorDecision Processor
Traffic Simulator
Event Manager
Time Axis
Decision to engage in some activity
Decision to engage in some activity
Determine destination and
mode
Determine destination and
mode
Travel
Scanning Interval (1)
Given arrival time, determine
activity duration
Given arrival time, determine
activity duration
Activity duration
Agent on Process Waiting
List
Agent on Traveler List
Agent on Traveler List
Agent on Actor List
Decision to engage in some activity
Decision to engage in some activity
Enhancing Behavioral Realism
Exacttripdurationsnotknownuntiltripsarecompleted
Needtoconsiderissuesofunmetmobility Anagentmaybelateforwork,cannotfinisherrand,cannotreturnhome,etc.
Prismconstraintsmaynotalwaysbesatisfied Prismconstraintsincreasinglyfuzzy?(technologyeffects)
Activity-Based Model Systems
Numerousactivitybasedmodelsystemsdevelopedinresearcharena
Modelshavematuredtovaryingdegrees Attempttoincorporateaspectsofbehaviorhighlightedinpresentation
Activity-Mobility Simulator (AMOS, FAMOS)
HouseholdAttributesGenerationSystemHouseholdAttributesGenerationSystem
SyntheticPopulation(HouseholdsandPersons)
SyntheticPopulation(HouseholdsandPersons)
HouseholdTravelSurvey
Data
HouseholdTravelSurvey
Data
PrismConstrainedActivityTravelSimulator
PrismConstrainedActivityTravelSimulator
NetworkLevelofServiceDataNetworkLevelofServiceData
ActivityTravelRecordsforEachPerson
ActivityTravelRecordsforEachPerson
OutputProcessorOutput
Processor
OutputReportsOutputReports
GISVisualizationGISVisualization
ODFlowsbyPurposeandTimeofDay
ODFlowsbyPurposeandTimeofDay
CensusSocioEconomic
Data
CensusSocioEconomic
Data
DynamicEventBasedNetwork
Simulator
DynamicEventBasedNetwork
Simulator
Model Design: SimTRAVEL
Model Design
Model Design
Model Design
SimTRAVEL: Simulator of Transport, Routes, Activities, Vehicles, Emissions, and Land
Funded by FHWA through Exploratory Advanced Research ProgramSee: http://simtravel.wikispaces.asu.edu
Integrated Model: Supply and Demand
OpenAMOS
DynusT/MALTA
t =1 mint =0
Origin,Destination,
VehicleInfoforVehicleTrip1
ArrivalTime
VehicleisloadedandthetripisSimulated
Person(s)reachdestinationandpursueactivity
Origin,Destination,
VehicleInfoforVehicleTrip2
24 hr duration
Update Set of Time-Dependent Shortest Paths 1440 paths per O-D Pair
ODTravelTimesforDestinationandModeChoiceModeling
6sec.interval
CEMDAP (Bhat)
Input Data Coordinator
SimulationCoordinator
Model Modules
Household
Person
Pattern
Tour
Stop
Internal Data Entities
Decision to work
Work duration
Work start time
HH activity generation
Activity stop location
LOS & Zonal data queries
Comprehensive Econometric Microsimulator of Daily Activity-travel Patterns
Comprehensive Econometric Microsimulator of Daily Activity-travel Patterns
CEMDAPApplication of the Generation-Allocation Model System
Work and school activity participation and timing decisions
Childrens travel needs and allocation of escort responsibilities to parents
Independent activity participation decisions
Application of the Scheduling Model SystemWork-to-home and home-to-work commute characteristics
Drop-off tour of the nonworker escorting children to school
Pick-up tour of the nonworker escorting children from school
School-to-home and home-to-school commutes
Joint tour of the adult pursuing discretionary activity with children
Independent home-based and work-based tours for each worker
Independent home-based tours for each non-worker
Independent discretionary activity tour for each child
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CEMDAP: Parent-Child Interaction
Children'sPattern
Parent(s)pattern
Go to School?
School Timing
Go to Work?
Work Timing
Mode to/from School
Drive by parent
Allocate to Parent(s)
Adjust Work Timingof Parent(s)
Integrated Activity-Travel Demand and Dynamic Traffic Assignment Model System (CEMDAP-VISTA: Bhat/Waller)
Activitytravelsimulator(CEMDAP)
Individualactivitytravelpatterns
Aggregatesociodemographics
Interface:ConvertPersonTourstoODTripTablesbyTimeofDay
Syntheticpopulation
generator(SPG)andInputgeneration(CEMSELTS)
Activitytravelenvironment(LOS)
Interface:LinkVolumesand
Speeds
Update LOS
ODTripTablesbyTimeofDay
LinkVolumesandSpeeds
Traffic SimulationFind 6 sec. traffic flowsAccounts for ramp metering, information provision, traffic incidents, etcOutput: Travel times per interval and road segment
Optimal routingFinds optimal rout for all OD pairs and departure timesSolve time-dependent shortest pathAccounts for non-travel time costs (tolls, stochasticity)Output: Optimal route per OD pair and departure time
Path AssignmentAssign paths to each individual vehicle on the networkOutput: vehicle path
VISTA
ConvergenceCheck
After Convergence
SimAGENT for SCAG
Networks & Attributes
Accessibility (aggregate)
Land Use Design and Forecasting (including demographics)
Population Synthesis: PopGen
Population in Zones
(centroids)
Daily Scheduling
Parcels/Zones & Attributes
Long Term Choices
Daily AllocationAirports & Ports
External Trips
Passenger & Highway and TransitAll Other
(commercial)
REPORT
Origin Destination Trip Interchange Matrices
Network Assignment
Post Processor
Person Daily
Tours-stops & trips
EMFAC
ADAPTED CEMDAP MODEL
SimAGENTSimulator of Activities, Greenhouse Emissions, Networks, and Travel
Addressprovisions/mandatesofSB375 Requiresmetropolitanplanningorganizations(MPOs)toinclude
sustainablecommunitiesstrategies(SCS)forthepurposeofreducinggreenhousegasemissions
Addresswiderangeofpolicies,e.g.: Economicanalysis:locationbasedwelfare,wages,andexports Equityanalysis:changeinwelfarebyhouseholdincomeclass EvaluatetheenergyuseandGHGsproducedbyhouseholdsandworkers
inbuildingspace
Comprehensivelyevaluateeconomicdevelopmentimpacts Evaluatetimeofdayroadwaytolls
SimAGENT Phased Implementation Plan for SCAG
Phase Title Description
1 Model Development Plan and Strategy
Work closely with SCAG staff to finalize model development plan, model structure, model implementation path, and software and data requirements and specifications
2Development and Implementation of SimAGENT Version 1
Adapt CEMDAP to SCAG regionAdd Synthetic Population GeneratorCompare to 2003 Trip-Based ModelExtensive Validation and Sensitivity TestingConduct Hands-on Staff Training SessionsEstimate GHG using EMFAC
3Development and Implementation of SimAGENT Version 2
Enhanced CEMDAP Model SpecificationsMore Detailed Spatial/Network ResolutionFull Incorporation of Time-Space ConceptsExtensive Validation and Sensitivity Testing
4 Training and Reports Submission of Final Deliverables Conduct Hands-on Staff Training Sessions
3:00 AM-4:00AM
4:00 AM-5:00AM
5:00 AM-6:00AM
6:00 AM-7:00AM
7:00 AM-8:00AM
8:00 AM-9:00AM
9:00 AM-10:00AM
10:00 AM-11:00AM
11:00 AM- Noon
Noon-1:00PM
1:00 PM-2:00PM
2:00 PM-3:00PM
3:00PM-4:00PM
4:00PM-5:00PM
5:00PM-6:00PM
6:00PM-7:00PM
7:00PM-8:00PM
8:00PM-9:00PM
9:00PM-10:00PM
10:00PM-11:00PM
11:00PM-Midnight
Midnight-1:00 AM
1:00 AM-2:00AM
OPUS FRAMEWORK
URBANSIM
SpecialGenerators(eg, airport)
Trip Aggregator
Network traffic assignmentOD Matrices Network performance(skims)
External trips
HH/Personday-tour-trip list
Commercialmovements
AB HOUSEHOLD TRAVELDEMAND SIMULATOR
TRANSPORT MODEL SYSTEM
Person Day Simulator
Mobility Choice Simulator
Parcel Attributes(Land Development)
SyntheticPopulation Accessibility
TRANSPORT PLANNING
TransportNetworks
Parcel Attributes(Transport
Development)
REPORTINGAND QUERYSUBSYSTEM
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Activity-Based Model System - DASH(Gliebe)MetroScopeLandUseDataMetroScopeLandUseData
PopulationSynthesisPopulationSynthesis
WorkplaceLocationChoiceWorkplaceLocationChoice School/College
LocationChoiceSchool/CollegeLocationChoice
AutoOwnershipChoice
AutoOwnershipChoice
LongTermChoices
DayActivityPattern/RoleChoice
DayActivityPattern/RoleChoice
StartingTimeChoiceStartingTimeChoice
InitialConditions(Day)
TripListsTripListsAssignmentAssignment
TripTablesTripTables
PostProcessor
NextStopPurposeNextStopPurpose
NextStopLocationChoice
NextStopLocationChoice
DynamicActivityPatternGeneration
TourModeChoiceTourModeChoice
NextStopModeChoice
NextStopModeChoice
TourFirstStopPurposeChoice
TourFirstStopPurposeChoice
ADAPTS (Mohammadian)
Agent-based Dynamic Activity Planning and Travel Scheduling
Household Planning
Individual Planning
Household Schedule
Household Memory
Social Network
Individual Schedules
Individual Memory
Land Use
Network LOS
Institutional Constraints
Initialize SimulationInitialize WorldSynthesize PopulationGenerate routineactivities
For each timestep
Write Trip Vector
Traffic Assignment
Information Flow
Simulation Flow
ADAPTS (Mohammadian)
At timestep t
Plan new activities
Update existing activity(s)
Execute activity
Attribute Planning Order model
Planned Activity
Schedule
Time-of-Day model
t = Ttime
Party composition
model
t = Twith
Mode Choice model
t = Tmode
Destination choice model
t = Tloc
Executed Schedule
Resolve Conflicts Conflict Resolution Model
Set Plan Flags:(Ttime,Twith, Tloc,
Tmode)
Yes
Decision
Logical test
Model
Simulated events
Yes
No
Yes
No
No
Activity Generation
Activity Planning
Activity Scheduling
ALBATROSS (Timmermans/Arentze)
Alearningbasedactivitybasedtravelmodelsystem Employstheoriesofchoiceheuristicstorepresentbehavioralprocesses
Decisiontreeapproachesusedtoformalizeheuristicsandpredictchoicebehavior
Explicitconsiderationofmultitudeofconstraints
Albatrossassumesthatchoicebehaviorisbasedonrules thatareformedandcontinuouslyadapted throughlearning
Individualinteractswiththeenvironment(reinforcementlearning)orcommunicateswithothers(sociallearning)
Albatrossisbasedonalearningtheorywhichimpliesthatrulesgoverningchoicebehaviorare:
heuristic contextdependent adaptiveinnature
ALBATROSS (Timmermans/Arentze)
Components: amodelofthesequentialdecisionmakingprocess modelstocomputedynamicconstraintsonchoice
options
Asetofdecisiontreesrepresentingchoicebehaviorofindividualsrelatedtoeachstepintheprocessmodel
[a-priori definedderived from observed choice behavior
ALBATROSS (Timmermans/Arentze)
ALBATROSS (Timmermans/Arentze)
ALBATROSS (Timmermans/Arentze)
RuleRule--Based HeuristicsBased Heuristics
ALBATROSS (Timmermans/Arentze)
Classification of ActivitiesClassification of Activities
ALBATROSS (Timmermans/Arentze)
TRANSIMS (FHWA/LANL)
TransportationAnalysisandSimulationSystem Generatesandsimulatesactivity/travelpatternsforindividualsinaregionovera24hourperiod
Supportshighlydetailedroadandtransitnetworks Timedependentlinkdelaysareconsideredforroutingtripsthroughthenetwork
TRANSIMS: Framework
PopulationSynthesis
ActivityPatterns
ModePreference
Router Microsimulator
Stabilization
RefineModes
ChangeActivityTimesorPatterns
RouteAttributesAttractionBalancing
TRANSIMS: Activity Generator Generatesactivityengagementpatternsforeachmemberofahouseholdovera24hourperiod
Outofhomeactivitylocationsdeterminedusingadestinationchoicemodel
Activityengagementpatternsgeneratedbysamplingfromactivitypatternsofindividualsinatravelsurvey Inthecurrentimplementation,ClassificationandRegressionTreesare
used
TRANSIMS: Activity Generator (continued)
TRANSIMS: ApplicationHighwayNetwork
Conversion
TransitNetworkConversion
NetworkEditing
TripTableConversion
CensusDataConversion
PopulationSynthesisActivityGeneration
RouterandRouterFeedback
Microsimulator
ActivityGeneration
PopulationSynthesis
CensusDataConversionTripTableConversion
Tripbasedmodel Hybridmodel
Tourbasedmodel
NetworkPreparation
TRANSIMS: Feedback Processes
Router
PlanPrep(Merge)
PlanSum
PlanSelect
Done?
RouterStabilization
MicrosimulatorStabilization
UserEquilibrium
Router
PlanPrep(Merge/Sort)
PlanSelect/ProblemSelect
Microsimulator
PlanCompare
Done?
PlanPrep(Merge/Sort)
Microsimulator
Router
Done?
No
Yes Yes
Yes
NoNoStop
MATSIM-T (Axhausen/Nagel)
MultiAgentTransportSimulationToolkit Iterativeagentbasedtrafficsimulationframework Onlyautosaresimulated Involvestwomaincomponents
Agentgeneration(groupedashouseholds) ActivityScheduling
MATSim-T: Scheduling Tasks
TaskFrequencyper
runModeltype
Number,sequenceandtypeofactivities
Once Conditionalprobability
Startanddurationofactivities PeriterationBestresponsemodel(GAbasedoptimizer)
Compositionofthegroupundertakingtheactivity
Expenditureanditsallocationamongtheparticipants
Secondarylocationchoice OnceImputed(Proportionaltosizeanddistance)
Mode/vehiclechoice PeriterationImputed
(ChainbasedMNL)
Traveldemandqisgeneratedandmicrosimulated Resultinggeneralizedcostskareusedtoadjustschedules,
capacitiesandpricesoffacilities
Route(r)adaptationprocessalsoextendstowardstimechoice(t),modechoice,locationchoice(j),etc
MATSim-T: Framework
Competitionforslotsonnetworksandinfacilities
MentalMap
ActivityScheduling
Population
Parameters
Scenario k(t,r,j)
q(t,r,j)
ILUTE (Miller) IntegratedLandUse,Transportation,Environment
(ILUTE)modelsystem
ILUTEmainlytriestomodelthespatialmarketsandthepersons dailydecisionmakingwithinahouseholdbasedcontext
Simulatestheevolutionofagentsandobjectsovertime Agentsandobjectsincludeindividuals,transportationnetworks,
thebuiltenvironment,theeconomy,andthejobmarket
ILUTE: Framework
Demographics LandUse
LocationChoice
Activity/Travel&Goods
Movement
DynamicTrafficAssignment
VehicleOwnership
RegionalEconomics
GovernmentPolicies
ExternalImpactsFlows,Times,etc.
TransportSystem
ILUTE: Structure and Current ImplementationObservedBaseYear
AggregateDistributionsofAgentsandAttributes
AGENTSYNTHESIS
SyntheticAgentPopulationT=0
T=T+T
DemographicUpdate
LabourMarket
HousingMarket
AutoOwnership
ActivityBasedDailyTravel(TASHA)
RoadandTransitNetworkAssignments
TransportationEmissions&DispersedPollutionConcentrations GHGEmissions
Link&ODTravelTimes/CostsLink,
CongestionLevels,Etc.
CommercialVehicleMovements
Employment@timeT
Road&TransitNetworks@TimeT
PopulationExposuretoPollutantsbyLocation
andTimeofDay
ExogenousInputs@timeT:
InterestRatesEnergyRatesVehicletechnologyZoningIn/outmigrationrates
ILUTE: Evolutionary Engine
ExogenousInputs,TimeTInmigrationPolicyChanges
EMME/2TransportationNetworkModel(Computetraveltimes/costsbymode)
ILUTEEvolutionaryEngine
ForT=T0+1,T0+NTdo:DemographicUpdate
DemographicsFamily/householdcompositionupdateSchoolparticipationupdate
BuildingStockUpdateResidentialHousingCommercialFloorspace
Firm/JobLocationUpdateWorkParticipation&LocationUpdateResidentialLocationUpdateAutoOwnershipUpdateCommercialVehicleMovementUpdateActivity/TravelUpdate(TASHA)
SynthesizeBaseYearPopulation,Employment,
Dwellings,etc.
BaseYearCensusData,OtherAggregate
Data T0=BasetimepointT=CurrenttimepointbeingsimulatedNT=Numberofsimulationtimesteps
TASHA (Miller/Roorda) Travel/ActivitySchedulerforHouseholdAgents Simulatesoutofhomeactivityandtravelpatternsforindividualsrecognizinghouseholdlevelinteractionsandconstraints
TASHAusestheconceptofproject introducedbyAxhausen(1998)
TASHAcomprisesof: Anactivityepisodegenerator Anactivityscheduler Arandomutilitytourbasedmodechoicemodel
TASHA: Class Structure and Project Definitions
TASHA: Activity Generation, Scheduling and Mode Choice
Time Use Utility Measures
Timeuseallocationiscentraltotheactivitybasedmodelingparadigm
Offersstrongframeworkforanalyzingmeasuresofwelfarethatpeoplederivefromtheiractivitytravelpatterns
Addresssocialequityandqualityoflifeissues
Formulation of Time Use Utility Measure
Utilityformulation)1ln(])1ln([ qqqqq TSxU
Uq isutilityderivedfromactivityoftypeq
Tq iscumulativedailytimeexpenditureonactivityoftypeq
Sq iscumulativedailytimeexpenditureontravelforactivityoftypeq
xq isavectorofcovariatesaffectingutilityUq isascalarcoefficientassociatedwithln(Sq+1) isavectorofcoefficientsassociatedwithxqq isani.i.d.randomerrorterminUq.
Utilityformulation)1ln( ss TU
sq
q UUU
Us istheutilityderivedfromsleepU isthetotalutilityderivedfromthetimeusepatternTs iscumulativedailytimeexpenditureonsleepTf isthetotaltimeavailableinaday.
Maximize
Subjectto fsq
q TTST
Formulation of Time Use Utility Measure
Baseline Activity Pattern
Activity Type Daily Duration (min)
Sleep 472In-home maintenance 202Out-of-home maintenance 53Travel for out-of-home maintenance 37In-home discretionary 166Out-of-home discretionary 76Travel for out-of-home discretionary 16Commute time (round trip) 60
Modified Activity Pattern: After Telecommuting
Activity Type Daily Duration (min)
Sleep 492 (+20)In-home maintenance 202Out-of-home maintenance 53Travel for out-of-home maintenance 37In-home discretionary 186 (+20)Out-of-home discretionary 90 (+14)Travel for out-of-home discretionary 22 (+6)Commute time (round trip) 0
Example Timeuseutilitymeasureformulatedasafunctionof: Socioeconomicanddemographiccharacteristics Traveldurationstoandfromactivities Activitydurationsfordifferentactivitytypes/episodes
TimeUseUtilitybeforecapacityenhancement=25.570 TimeUseUtilityaftercapacityenhancement=27.531 Couldtranslateintomonetarybenefits Alsoexamineequityacrossmarketsegments
Activity Type Utility Value Before
Telecommuting
Utility Value After
Telecommuting
Sleep 6.159 6.201In-home maintenance 2.939 2.939Out-of-home maintenance 0.777 0.777In-home discretionary 2.882 2.946Out-of-home discretionary 12.813 14.669Total 25.570 27.531
Example
Key Considerations
Representationoffuzzytimespaceprismconstraints,interagentinteractions,andtimeusebehavior
Greaterlevelofsimultaneityinchoiceprocessestoreflectchoiceoflifestylepackage
Recognitionofheterogeneityinpopulation behavioralstructure,decisionhierarchy,parameters/coefficients Carefulmarketsegmentation,trippurposedefinition,representationof
time,space,andnetworks
Centralroleoftimeandspace Disaggregaterepresentationoftimespacedomain Continuousrepresentationoftime Disaggregatespatialrepresentation
Maximizeuseofinformationfromactivitybasedtravelmodel
Key Considerations
Things to Think About Feedbackprocesses
Feedbackwithinactivitytravelsimulatorfromdestination/modechoicetotimeofdaychoicetoactivitytype/generation
Feedbackfromnetworkassignmenttoactivitytype/generation(tourstops),andmodeanddestinationchoice
Criteriaforconvergenceandequilibriumconditions
Stochasticsimulation Onerunrepresentsonerealizationofstochasticprocess Howmanyrunsarerequiredtoachievestableresults? Impactsoncomputationtimeandhardware/softwarerequirements
Datarequirements Travelsurveydata Multimodalnetworkdatabytimeofday Detailedlandusedata Greaterlevelofdisaggregationforactivitymicrosimulation
Things to Think About
Inhouseresources Stafftrainingandexpertise Computationalresources Phaseddevelopmentplan
Comprehensivemodeldesignupfrontwithstageddevelopmentandimplementationschedule
Things to Think About