Synchronizing transport networks and activities of individuals: a supernetwork approach

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

2. 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)

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

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

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

6. The activity-based approach

7. Activity-basedversus trip-basedapproach Trip-based Activity-based Focus is on trips Focus is on activities Unit is a trip Unit is a day Space-time constraints ignored Space-time constraints taken into account Low resolution time and place High resolution time and place Decision unit is individual Decision unit is household Predicts when, where, transport mode Predicts which activities, when, where, for how long, trip-chaining and transport mode

8. 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

9. 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

10. 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

11. 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 people’s activities • Virtual links – ICT • Synchronization = all you can do to improve accessibility without increase of capacity • What is accessibility?

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

13. 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

14. 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

15. The supernetwork model

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

17. Extension with activity programs

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

19. Example of a schedule 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

20. Example of a schedule 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

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

22. Rotterdam case study Liao, F., T. Arentze, E. Molin, W. Bothe Illustration of an application An activity-based supernetwork model

23. Study area delineation

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

25. Activity programs • Average per person • 2.46 activities per day • 1.57 tours per day

26. P + R locations • P+R locations (9 in R’dam) • Train stations (10 locations ) • Actual tariffs

27. Public transport upgrades New tram line has stop in Rdam stadion station High frequent trainsbetween Randstad cities

28. Increase parking price at activity locations Parking costs double

29. Spatial developments – realistic • Shopping • Going out • Culture • Sports

30. Spatial developments – city center • Shopping • Going out • Culture • Sports All concentrate in city center

31. Spatial developments – near nodes • Shopping • Going out • Culture • Sports Concentrate near transport nodes

32. Example of a case • An individual lives North-east of center and has a non-daily shopping activity on the agenda

33. Example of a case The person considers five options - three close to home and the other two in Rotterdam center

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

35. Example of a case After city center investment, the personswitches to use car, parks car at P+R Capselsebrug and then takes PT to center Car PT

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

37. Total car kilometers Rotterdam PT upgrades no influence With Park car kilometers decrease With Real car kilometers increase With City car kilometers decrease Baan Tram Real Node Stadion Park City Work

38. Transport mode choice (R’dam) Park decreases car in favor of P+R and bike City decreases car use the most and increases PT use Baan Tram Real Node Park increases P+R considerably Stadion Prijs City Work

39. Number of people making use of P+R Entirearea Park – strong increase P+R City causes decrease in P+R use Rotterdam More often entire trip by PT Node leads to most P+R use Baan Tram Real Node Stadion Park City Work

40. P+R use – for which activities? 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 Baan Tram Real Node Stadion Park City Work

41. 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

42. 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

43. 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?

44. 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

45. Outlook of issues and research topics

46. 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

47. Trends and developments in society – cont’d • 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

48. 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)

49. Thank you for your attention Questions

50. 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. Gärling, 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-Schüssler, 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.).