This document outlines BusMezzoDynamic, a dynamic transit modeling tool that simulates traffic conditions, transit operations, and passenger route choices. The model has several components: (1) a mesoscopic traffic simulation, (2) modeling of transit operations and uncertainties, and (3) modeling of passenger demand and dynamic route choices. It is intended to evaluate impacts of transit operations strategies and real-time traveler information provision. A case study of the Stockholm metro system demonstrates how the model can simulate passenger route choices and travel times under normal operations and service disruptions with and without real-time information. Ongoing work aims to further develop transit operations strategies and the passenger choice-set generation model.

1. BusMezzoDynamic Transit Operations Tool with Passenger Route ChoiceOded CatsCentre for Traffic Research (CTR)Kungliga Tekniska Högskolan (KTH)2011-01-13Transportforum 2011 Linköping

2. OutlineDynamic transit modelModel components:Traffic Simulation ModelTransit operationsPassenger decisionsChoice-set generation

3. Dynamic route choiceCase study: Real-time information

4. Motivation and ObjectivesExisting transit models are static Simplifying assumptions regarding traveler behavior, traffic conditions and transit operationsSuitable for strategic planning of route network, time-tables, etc.Not suitable for operations analysis, Advanced Public Transport Systems (APTS) evaluationNeed for dynamic transit modeling toolCapturing the dynamics of traffic conditions, traveler behavior and transit operations at a network-wide levelExperimental tool for assessing how operations strategies address policy objectives

5. Potential ApplicationsPlanning

6. Effects of transit route changes

7. Time-table assessment

8. Service coordination

9. Operations

10. Public transport performance and level of service analysis

11. Impacts of transit priority

12. Restoration from major disruptions

13. Fleet assignment efficiency

14. Real-time

15. Evaluation of real-time control strategies

16. Real-time traveler information evaluation Transit model components

17. Running partQueue partMezzoA mesoscopic traffic simulationEvent based

18. Stochastic processes

19. Traffic dynamics:

20. Aggregate behavior on links

21. Turn-specific queue servers

22. Enables large scale applications

23. OOP (C++)

24. Open-sourceTransit operationsModeling sources of uncertainty

25. Suitable for APTS applications

26. Transit processes:

27. Time-tables

28. Vehicle scheduling

29. Travel time

30. Boarding and alighting processes

31. Dwell time

32. Capacity constraints

33. Holding control strategiesControl strategies studyToledo T., Cats O., Burghout W. and Koutsopoulos H.N. (2010). Mesoscopic simulation for transit operations. Transportation Research Part C – Emerging Technologies, 18(6), 896-908.

34. Passenger demand modelingDifferent levels of demand representationBoarding and alighting ratesDemand matrix per lineOD matrix in terms of stopsOD matrix in terms of zones

35. Transit users’ decisions surveyWeb-based questionnaireExperimental designPath-adaptive structureRP & SPChosen transit routeTransit route choice (composing, selecting, rating)Transit route choice experimentsTransfer stop choiceAnalysisRandom utility model estimationTesting behavioral thresholds, choice-set sizeEstimating and validating choice-set composition method

36. Survey results - examplesEstimated choice model coefficientsChoice-set size distribution

37. Choice-set generationLimited studies (Fiorenzo-Catalano et al. 2004; Van Nes et al. 2008)

38. Recursive search method

39. Static choice-set as a preliminary phaseWhat is an alternative?OD stops / OD TAZ

40. Connection distances

41. Clustering transfer stops

42. Clustering common lines Dynamic route choiceTraveler information(prior-knowledge, type, location, comprehensiveness)

43. 15Stockholm Metro case studyExperiment description3 lines, 7 branches, 100 stations with 210 platforms

44. 10 min headway, schedule-based holding control

45. Choice-set generation process: 14,699 alternative pathsStockholm Metro case study Scenarios designEvaluating the effect of RTI provision on passenger route choice

46. Service disruptions

47. A 50% reduction in frequency

48. A 15 minutes delay in riding timeRTI Metro case studyRoute choice alternatives

49. RTI Metro case studyPassenger journey time under different operation conditions and levels of RTI

50. Stockholm Metro case studyConclusionsRTI provision has the potential to yields substantial path choice shifts and time savings

51. Particularly significant time savings in case of irregular service conditions

52. A simple improvement in transfer coordination can be very beneficial

53. The incorporation of walking times is important in the context of transit route choice

54. Proof of conceptOn-going developmentsTransit operation strategiesPreparing a field study for testing the even-headway control strategyOptimizing the number and location of time-point stopsAnalyzing the case of a common corridorChoice-set generation modelFormulating an estimation method Implementing the method using the survey dataDesigning a validation study of the transit assignment model