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Synchronizing transport networks and activities of individuals: a supernetwork approach

Theo Arentze

Urban Planning GroupEindhoven University of Technology The NetherlandsSAR project synchronizing networksTU DelftCoordination (E. Molin)Postdoc project: scenario development (W. Bothe, J-W v.d. Pas)PhD project: user behavior (C. Chen, C. Chorus, E. Molin, B v Wee)

TU EindhovenPhD project: modeling supernetworks (F. Liao, T. Arentze, H. Timmermans)

University of NijmegenPhD project: governance (S. Levy, K. Martens, R. Vd Heijden)OutlineActivity-based approach

Supernetwork model

Rotterdam case study illustration of an application

Outlook new issues and research topics

Conclusions

Travel demand modelsMicro simulation modelsAggregate modelsActivity-based modelsTour-based modelsDaily activity-patternsTrip recordsOD trip matrixDynamic/static traffic simulation/assignment modelsPredicting peoples response to policies is notoriously difficult Travel demand modelsMicro simulation modelsAggregate modelsActivity-based modelsTrip/tour-based modelsDaily activity-patternsTrip recordsOD trip matrixDynamic/static traffic simulation/assignment modelsModels are now making the transition to practice New model development started in early nineties

The activity-based approachActivity-based versus trip-based approachTrip-basedActivity-basedFocus is on tripsFocus is on activitiesUnit is a tripUnit is a daySpace-time constraints ignoredSpace-time constraints taken into accountLow resolution time and placeHigh resolution time and placeDecision unit is individualDecision unit is householdPredicts when, where, transport modePredicts which activities, when, where, for how long, trip-chaining and transport modeAdvantages of the activity-based approachBetter predictions

Sensitivity to broader range of policy scenarios

Higher level of precision in time and space

Transparency models tell the full storyApproachesConstraints-basedStems from time geography (Hagerstrand)Basic concept is space-time prismsPurpose is accessibility analysis not predictionExamples: Carla, Mastic

Nested-logit modelsExtension of trip and tour-based modelsStarted with the work of Bowman and Ben Akiva (2001)Rather course classification of activities and modes

Approaches - continuedActivity-scheduling modelsTake scheduling process and constraints into accountUtility-based models versus rule-based modelsSome pioneering modelsFamos, Albatross, Cemdap, Tasha, Adapts Simulation / optimization modelsTraffic oriented models (Transims, Matsim)Operations Research models (Happs)Supernetwork models

Synchronizing networksCan we improve accessibility by synchronizing networks?Existing capacity of networks stays the sameBetter mutual adjustmentBetween networks of different modalitiesVis-a-vis locations of peoples activitiesVirtual links ICT

Synchronization = all you can do to improve accessibility without increase of capacity

What is accessibility?

AccessibilityHow much does it costs to implement a given activity program?

Preferences and choice behavior of people need to be taken into accountWhich planning and policy measures?SynchronizationFrequencies and time tables of public transportTransfer locations e.g., P + RFacilities at or near stations and stopsFacilities at work places, etc.ICT facilities (teleworking, internet facilities)Spatial development near nodes of transport networksGoal of the proposed supernetwork modelIntegrated approachSpatial development / Transport / ICTMultimodal networksComplete activity programs

TransparancyIndividual approach micro-simulationVery high level of detail

The new tool is sensitive to synchronization strategies

The supernetwork modelTraditional concept: multi-modal networksTransfer locationsA path is a multimodal trip

supernetwork

Extension with activity programs

Take the busWork activityGo by bike to bus stopTake busShoppingTake bikeBicycle back homeLiao, F.Example of a scheduleModeFromToLineCar_atBike_atTimebike from home110home00bike140home020bike430home02park bike330home30board331home38transit381home318transit891home33transfer993home33transit9103home35alight10100home34walk10110home38work11110home30walk11100home38board10103home34transit1093home35transfer991home36transit981home33transit831home318alight330home38get bike330home00bike340home02park bike440home40shop440home40get bike440home00bike410home020bike to home110homehome01. Besluit om met fiets te gaan2. Kiest parkeerplaats voor fiets3. Reist met bus lijn 14. Stapt over op bus lijn 35. Loopt naar werk locatie6. Werk activiteit7. Terugweg (buslijn 1 en 3)8. Haalt fiets van parkeerplaats9. Kiest een winkellocatie10. Fietst terug naar huisExample of a scheduleModeFromToLineCar_atBike_atTimebike from home110home00bike140home020bike430home02park bike330home30board331home38transit381home318transit891home33transfer993home33transit9103home35alight10100home34walk10110home38work11110home30walk11100home38board10103home34transit1093home35transfer991home36transit981home33transit831home318alight330home38get bike330home00bike340home02park bike440home40shop440home40get bike440home00bike410home020bike to home110homehome0Simultaneous choice of Modes and transfers Routes Parking places Activitity locationsFor a complete trip-chain(a tour)Schedule is consistentVery high level of detailDecisions are based on utility maximization

Edge of city center

Trade-offs?Comprehensive large-scale experiments have been conducted

Choice experiments: preference measurement

Rotterdam case studyLiao, F., T. Arentze, E. Molin, W. Bothe Illustration of an applicationAn activity-based supernetwork model

Study area delineation

Synthetic population

Corridor: 2.5 million residents (2009) Total: 21,117 agentsAgents : Residents = 1 : 118Activity programs were takenfrom a survey Activity programs

Average per person 2.46 activities per day 1.57 tours per day#trips2 3 4567>7% 47 %7.3 %26 %4.9 %9.1 %2.5 %3.0 %work20.5%business3.3%education4.9%transportas work0.2%pick & drop6.2%service5.1%shopping24.5%Leisuregoing-out17.1%culture4.8%sports4.9%touring8.3%24P + R locations

P+R locations (9 in Rdam) Train stations (10 locations ) Actual tariffsPublic transport upgrades

New tram line has stop in Rdam stadion stationHigh frequent trains between Randstad citiesIncrease parking price at activity locations

Parking costs doubleSpatial developments realistic

Shopping Going out Culture SportsSpatial developments city center

Shopping Going out Culture SportsAll concentrate in city centerSpatial developments near nodes

Concentrate near transport nodes Shopping Going out Culture SportsExample of a caseAn individual lives North-east of center and has a non-daily shopping activity on the agenda

Example of a case

The person considers five options - three close to home and the other two in Rotterdam centerExample of a case

BikeBefore spatial development, the person always takes bike and does shopping at the same postcode areaExample of a case

After city center investment, the person switches to use car, parks car at P+R Capselse brug and then takes PT to centerCarPTTotal costs (disutility) of implementing activitiesStadionBaanTramParkRealCityNodeWorkPT upgrades improve utilityParking price increase causes strong decline in utilityCity scenario biggest utility improvementEntire areaTotal car kilometersStadionBaanTramParkRealCityNodeWorkPT upgrades no influenceWith Park car kilometers decreaseWith Real car kilometers increaseWith City car kilometers decreaseRotterdamTransport mode choice (Rdam)StadionBaanTramPrijsRealCityNodeWorkPark increases P+R considerablyPark decreases car in favor ofP+R and bikeCity decreases car use the mostand increases PT useRotterdamNumber of people making use of P+REntire areaStadionBaanTramParkRealCityNodeWorkCity causes decrease in P+R useMore often entire trip by PTPark strong increase P+R Node leads to most P+R useP+R use for which activities?StadionBaanTramParkRealCityNodeWorkUsed most often for workingand shopping tripsWork trips react more stronglyto parking priceSpatial development hasan impactPT upgradeshave minor impactSome findings (1)Upgrade of public transport frequency and new connectionsImprovement of utility, decrease of travel time. Small influence on patterns

Parking price increaseBig influence on P+R useRelatively big influence on public transport useInfluence on location choice? still to be looked atSome findings (2)Spatial developmentsBig influence on utility City the mostBig increase in travel distance Node the leastBig increase in travel time Real the mostReal increases car use City decreases car use and increases PT useCity least P+R use Node most P+R use

P+R useParticularly for work and shopping tripsPT upgrades have little influenceParking price has big influenceSpatial development has influence City decreases P+R, Node increases P+RAre synchronization strategies effective?Some preliminary conclusionsTo support P+R use costs advantage seem important to compensate for the inconvenience of the transferIntegrated spatial and transport planning pays-off: spatial developments need to be planned simultaneously with transport networksFurther research needed:What synchronisation measures are effectiveTo what extent are they effective to achieve accessibility goals?Conclusions supernetwork modelActivity-based approachComplete activity programs

Preferences of travelers are taken into accountLocations, modes, transfers, etc.

Integrated approachSpatial / Transport / Pricing / ICTMultimodal networks

TransparencyMicro-simulation - individual approachVery high degree of detail and coherence

Outlook of issues and research topics

Trends and developments in societyICT revolutionSocial mediaAugmented realityMobile traveler information systemsFlexible work times and work placesNew modes of transportElectrical vehicles (bicycles, cars)Car sharingMulti-modal transport networks

Trends and developments in society contdNew modes of traffic managementIndividual / personalizedNew requirements and concernsAgeing populationTransition to renewable forms of energyUrbanization scarcity of spaceQuality of life air quality, health, mobility

New methods of data collection and Big DataGPS-based survey technologySmart phonesSocial media

Conclusions overallActivity-based models show a large diversity of approaches

New GPS-based survey technology and Big Data offers new perspectives

An important current objective of the field is to develop dynamic models (longer time frames)

Thank you for your attention

QuestionsLiterature referencesActivity-based modelingArentze, T.A., H.J.P. Timmermans (2004) A Learning-Based Transportation Oriented Simulation System, Transportation Research B, 38, 613 - 633.Arentze, T.A. and H.J.P. Timmermans (2009), A Need-Based Model of Multi-Day, Multi-Person Activity Generation, Transportation Research B, 43, 251-265.Auld, J., A. Mohammadian (2011) Planning-Constrained Destination Choice in Activity-Based Model, Transportation Research Record, 2254 / 2011, 170-179.Balmer, M., K.W. Axhausen, K. Nagel (2006) Agent-Based Demand-Modeling Framework for Large-Scale Microsimulations, Transportation Research Record, 1985 / 2006, 125-134.Bhat, C.R., J.Y. Guo, S. Srinivasan, A. Sivakumar (2004) Comprehensive Econometric Microsimulator for Daily Activity-Travel Patterns, Transportation Research Record, 1894 / 2004, 57-66.Bowman, J.L., M.E. Ben-Akiva (2001) Activity-based disaggregate travel demand model system with activity schedules. Transportation Research Part A, 38, 1-28.Pendyala, R.M., R. Kitamura, A. Kikuchi, T. Yamamoto, S. Fujii (2005) Florida Activity Mobility Simulator: Overview and preliminary validation results. Transportation Research Record, 1921 / 2005, 123-130.Roorda, M.J. and B.K. Andre (2007) Stated Adaptation survey of activity rescheduling: Empirical and preliminary results. Transportation research Record, 2021, 45-54.Survey technologyEttema, D., T. Grling, L.E. Olsson and M. Friman (2010) Out-of-home activities, daily travel, and subjective well-being. Transportation Research Part A, 44, 723-732.Rieser-Schssler, N. (2012) Capitalising modern data sources for observing and modelling transport behaviour. Transportation Letters, 4, 115-128.Moiseeva, A., J. Jessurun and H.J.P. Timmermans (2010) Semi-automatic imputation of activity-travel diaries using GPS traces, prompted recall and context-sensitive learning algorithms. In: Proceedings of the 89th TRB Annual Meeting. Washington, D.C.: (CD-Rom, 13 pp.).

Literature references contdSupernetworksArentze, T.A., H.J.P. Timmermans (2004) A Multi-State Supernetwork Approach To Modeling Multi-Activity, Multi-Modal Trip Chains, International Journal of Geograhical Information Science, 18, 631-651.Liao, F., T. Arentze, H. Timmermans (2012) A supernetwork approach for modeling traveler response to park-and-ride. Transportation research Record, 2012 Vol.2 (nr 2323), 10-17.