Session 38 Oded Cats
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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.
Session 38 Oded Cats
- 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
- 6. Effects of transit route changes
- 9. Operations
- 10. Public transport performance and level of service analysis
- 11. Impacts of transit priority
- 12. Restoration from major disruptions
- 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
- 20. Aggregate behavior on links
- 22. Enables large scale applications
- 23. OOP (C++)
- 24. Open-sourceTransit operationsModeling sources of uncertainty
- 25. Suitable for APTS applications
- 27. Time-tables
- 29. Travel time
- 30. Boarding and alighting processes
- 31. Dwell time
- 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)
- 39. Static choice-set as a preliminary phaseWhat is an alternative?OD stops / OD TAZ
- 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
- 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