synchronizing transport networks and activities of individuals: a supernetwork approach theo arentze...

Synchronizing transport networks and activities of individuals: a supernetwork approach Theo Arentze Urban Planning Group Eindhoven University of Technology The Netherlands

SAR project synchronizing networks TU Delft Coordination (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 Eindhoven PhD project: modeling supernetworks (F. Liao, T. Arentze, H. Timmermans) University of Nijmegen PhD project: governance (S. Levy, K. Martens, R. Vd Heijden)

Outline Activity-based approach Supernetwork model Rotterdam case study illustration of an application Outlook new issues and research topics Conclusions

Travel demand models Micro simulation models Aggregate models Activity-based models Tour-based models Daily activity-patterns Trip records OD trip matrix Dynamic/static traffic simulation/assignment models Predicting peoples response to policies is notoriously difficult

Travel demand models Micro simulation models Aggregate models Activity-based models Trip/tour-based models Daily activity-patterns Trip records OD trip matrix Dynamic/static traffic simulation/assignment models Models are now making the transition to practice New model development started in early nineties

Activity-based versus trip-based approach Trip-basedActivity-based Focus is on tripsFocus is on activities Unit is a tripUnit is a day Space-time constraints ignoredSpace-time constraints taken into account Low resolution time and placeHigh resolution time and place Decision unit is individualDecision unit is household Predicts when, where, transport mode Predicts which activities, when, where, for how long, trip- chaining and transport mode

Advantages of the activity-based approach Better predictions Sensitivity to broader range of policy scenarios Higher level of precision in time and space Transparency models tell the full story

Approaches Constraints-based Stems from time geography (Hagerstrand) Basic concept is space-time prisms Purpose is accessibility analysis not prediction Examples: Carla, Mastic Nested-logit models Extension of trip and tour-based models Started with the work of Bowman and Ben Akiva (2001) Rather course classification of activities and modes

Approaches - continued Activity-scheduling models Take scheduling process and constraints into account Utility-based models versus rule-based models Some pioneering models Famos, Albatross, Cemdap, Tasha, Adapts Simulation / optimization models Traffic oriented models (Transims, Matsim) Operations Research models (Happs) Supernetwork models

Synchronizing networks Can we improve accessibility by synchronizing networks? Existing capacity of networks stays the same Better mutual adjustment Between networks of different modalities Vis-a-vis locations of peoples activities Virtual links ICT Synchronization = all you can do to improve accessibility without increase of capacity What is accessibility?

Accessibility How much does it costs to implement a given activity program? Preferences and choice behavior of people need to be taken into account

Which planning and policy measures? Synchronization Frequencies and time tables of public transport Transfer locations e.g., P + R Facilities at or near stations and stops Facilities at work places, etc. ICT facilities (teleworking, internet facilities) Spatial development near nodes of transport networks

Goal of the proposed supernetwork model Integrated approach Spatial development / Transport / ICT Multimodal networks Complete activity programs Transparancy Individual approach micro-simulation Very high level of detail The new tool is sensitive to synchronization strategies

Traditional concept: multi-modal networks Transfer locations A path is a multimodal trip supernetwork

Extension with activity programs

Take the bus Work activity Go by bike to bus stop Take bus Shopping Take bike Bicycle back home Liao, F.

Example of a schedule ModeFromToLineCar_atBike_atTime bike from home110home00 bike140home020 bike430home02 park bike330home30 board331home38 transit381home318 transit891home33 transfer993home33 transit9103home35 alight10 0home34 walk10110home38 work11 0home30 walk11100home38 board10 3home34 transit1093home35 transfer991home36 transit981home33 transit831home318 alight330home38 get bike330home00 bike340home02 park bike440home40 shop440home40 get bike440home00 bike410home020 bike to home110home 0 1. Besluit om met fiets te gaan 2. Kiest parkeerplaats voor fiets 3. Reist met bus lijn 1 4. Stapt over op bus lijn 3 5. Loopt naar werk locatie 6. Werk activiteit 7. Terugweg (buslijn 1 en 3) 8. Haalt fiets van parkeerplaats 9. Kiest een winkellocatie 10. Fietst terug naar huis

Example of a schedule ModeFromToLineCar_atBike_atTime bike from home110home00 bike140home020 bike430home02 park bike330home30 board331home38 transit381home318 transit891home33 transfer993home33 transit9103home35 alight10 0home34 walk10110home38 work11 0home30 walk11100home38 board10 3home34 transit1093home35 transfer991home36 transit981home33 transit831home318 alight330home38 get bike330home00 bike340home02 park bike440home40 shop440home40 get bike440home00 bike410home020 bike to home110home 0 Simultaneous choice of - Modes and transfers - Routes - Parking places - Activitity locations For a complete trip-chain (a tour) Schedule is consistent Very high level of detail Decisions are based on utility maximization

Edge of city center Trade-offs? Comprehensive large-scale experiments have been conducted Choice experiments: preference measurement

Illustration of an application An activity-based supernetwork model

Study area delineation

Synthetic population Corridor: 2.5 million residents (2009) Total: 21,117 agents Agents : Residents = 1 : 118 Activity programs were taken from a survey

Activity programs Average per person 2.46 activities per day 1.57 tours per day #trips23 4 5 67>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%

P + R locations P+R locations (9 in Rdam) Train stations (10 locations ) Actual tariffs

Public transport upgrades New tram line has stop in Rdam stadion station High frequent trains between Randstad cities

Increase parking price at activity locations Parking costs double

Spatial developments realistic Shopping Going out Culture Sports

Spatial developments city center Shopping Going out Culture Sports All concentrate in city center

Spatial developments near nodes Concentrate near transport nodes Shopping Going out Culture Sports

Example of a case An 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 center

Example of a case Bike Before spatial development, the person always takes bike and does shopping at the same postcode area

Example 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 center Car PT

Total costs (disutility) of implementing activities Stadion BaanTram Park Real City Node Work PT upgrades improve utility Parking price increase causes strong decline in utility City scenario biggest utility improvement Entire area

Total car kilometers Stadion BaanTram Park Real City Node Work PT upgrades no influence With Park car kilometers decrease With Real car kilometers increase With City car kilometers decrease Rotterdam

Transport mode choice (Rdam) Stadion BaanTram Prijs Real City Node Work Park increases P+R considerably Park decreases car in favor of P+R and bike City decreases car use the most and increases PT use

Rotterdam Number of people making use of P+R Entire area Stadion BaanTram Park Real City Node Work City causes decrease in P+R use More often entire trip by PT Park strong increase P+R Node leads to most P+R use

P+R use for which activities? Stadion BaanTram Park Real City Node Work Used most often for working and shopping trips Work trips react more strongly to parking price Spatial development has an impact PT upgrades have minor impact

Some findings (1) Upgrade of public transport frequency and new connections Improvement of utility, decrease of travel time. Small influence on patterns Parking price increase Big influence on P+R use Relatively big influence on public transport use Influence on location choice? still to be looked at

Some findings (2) Spatial developments Big influence on utility City the most Big increase in travel distance Node the least Big increase in travel time Real the most Real increases car use City decreases car use and increases PT use City least P+R use Node most P+R use P+R use Particularly for work and shopping trips PT upgrades have little influence Parking price has big influence Spatial development has influence City decreases P+R, Node increases P+R

Are synchronization strategies effective? Some preliminary conclusions To support P+R use costs advantage seem important to compensate for the inconvenience of the transfer Integrated spatial and transport planning pays-off: spatial developments need to be planned simultaneously with transport networks Further research needed: What synchronisation measures are effective To what extent are they effective to achieve accessibility goals?

Conclusions supernetwork model Activity-based approach Complete activity programs Preferences of travelers are taken into account Locations, modes, transfers, etc. Integrated approach Spatial / Transport / Pricing / ICT Multimodal networks Transparency Micro-simulation - individual approach Very high degree of detail and coherence

Outlook of issues and research topics

Trends and developments in society ICT revolution Social media Augmented reality Mobile traveler information systems Flexible work times and work places New modes of transport Electrical vehicles (bicycles, cars) Car sharing Multi-modal transport networks

Trends and developments in society contd New modes of traffic management Individual / personalized New requirements and concerns Ageing population Transition to renewable forms of energy Urbanization scarcity of space Quality of life air quality, health, mobility New methods of data collection and Big Data GPS-based survey technology Smart phones Social media

Conclusions overall Activity-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 Questions

Literature references Activity-based modeling Arentze, 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 technology Ettema, 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 contd Supernetworks Arentze, 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.

synchronizing transport networks and activities of individuals: a supernetwork approach theo arentze...

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