Session 38 Oded Cats

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Session 38 Oded Cats

  1. 1. BusMezzoDynamic Transit Operations Tool with Passenger Route Choice<br />Oded Cats<br />Centre for Traffic Research (CTR)<br />Kungliga Tekniska Högskolan (KTH)<br />2011-01-13<br />Transportforum 2011 Linköping<br />
  2. 2. Outline<br />Dynamic transit model<br />Model components:<br />Traffic Simulation Model<br />Transit operations<br />Passenger decisions<br /><ul><li>Choice-set generation
  3. 3. Dynamic route choice</li></ul>Case study: Real-time information<br />
  4. 4. Motivation and Objectives<br />Existing transit models are static <br />Simplifying assumptions regarding traveler behavior, traffic conditions and transit operations<br />Suitable for strategic planning of route network, time-tables, etc.<br />Not suitable for operations analysis, Advanced Public Transport Systems (APTS) evaluation<br />Need for dynamic transit modeling tool<br />Capturing the dynamics of traffic conditions, traveler behavior and transit operations at a network-wide level<br />Experimental tool for assessing how operations strategies address policy objectives <br />
  5. 5. Potential Applications<br /><ul><li>Planning
  6. 6. Effects of transit route changes
  7. 7. Time-table assessment
  8. 8. Service coordination
  9. 9. Operations
  10. 10. Public transport performance and level of service analysis
  11. 11. Impacts of transit priority
  12. 12. Restoration from major disruptions
  13. 13. Fleet assignment efficiency
  14. 14. Real-time
  15. 15. Evaluation of real-time control strategies
  16. 16. Real-time traveler information evaluation </li></li></ul><li>Transit model components<br />
  17. 17. Running part<br />Queue part<br />Mezzo<br />A mesoscopic traffic simulation<br /><ul><li>Event based
  18. 18. Stochastic processes
  19. 19. Traffic dynamics:
  20. 20. Aggregate behavior on links
  21. 21. Turn-specific queue servers
  22. 22. Enables large scale applications
  23. 23. OOP (C++)
  24. 24. Open-source</li></li></ul><li>Transit operations<br /><ul><li>Modeling sources of uncertainty
  25. 25. Suitable for APTS applications
  26. 26. Transit processes:
  27. 27. Time-tables
  28. 28. Vehicle scheduling
  29. 29. Travel time
  30. 30. Boarding and alighting processes
  31. 31. Dwell time
  32. 32. Capacity constraints
  33. 33. Holding control strategies</li></li></ul><li>Control strategies study<br />Toledo 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.<br />
  34. 34. Passenger demand modeling<br /><ul><li>Different levels of demand representation</li></ul>Boarding and alighting rates<br />Demand matrix per line<br />OD matrix in terms of stops<br />OD matrix in terms of zones<br />
  35. 35. Transit users’ decisions survey<br />Web-based questionnaire<br />Experimental design<br />Path-adaptive structure<br />RP & SP<br />Chosen transit route<br />Transit route choice (composing, selecting, rating)<br />Transit route choice experiments<br />Transfer stop choice<br />Analysis<br />Random utility model estimation<br />Testing behavioral thresholds, choice-set size<br />Estimating and validating choice-set composition method<br />
  36. 36. Survey results - examples<br />Estimated choice model coefficients<br />Choice-set size distribution<br />
  37. 37. Choice-set generation<br /><ul><li>Limited studies (Fiorenzo-Catalano et al. 2004; Van Nes et al. 2008)
  38. 38. Recursive search method
  39. 39. Static choice-set as a preliminary phase</li></li></ul><li>What is an alternative?<br /><ul><li>OD stops / OD TAZ
  40. 40. Connection distances
  41. 41. Clustering transfer stops
  42. 42. Clustering common lines </li></li></ul><li>Dynamic route choice<br />Traveler information<br />(prior-knowledge, type, location, comprehensiveness)<br />
  43. 43. 15<br />Stockholm Metro case studyExperiment description<br /><ul><li>3 lines, 7 branches, 100 stations with 210 platforms
  44. 44. 10 min headway, schedule-based holding control
  45. 45. Choice-set generation process: 14,699 alternative paths</li></li></ul><li>Stockholm Metro case study Scenarios design<br /><ul><li>Evaluating the effect of RTI provision on passenger route choice
  46. 46. Service disruptions
  47. 47. A 50% reduction in frequency
  48. 48. A 15 minutes delay in riding time</li></li></ul><li>RTI Metro case studyRoute choice alternatives<br />
  49. 49. RTI Metro case studyPassenger journey time under different operation conditions and levels of RTI<br />
  50. 50. Stockholm Metro case studyConclusions<br /><ul><li>RTI provision has the potential to yields substantial path choice shifts and time savings
  51. 51. Particularly significant time savings in case of irregular service conditions
  52. 52. A simple improvement in transfer coordination can be very beneficial
  53. 53. The incorporation of walking times is important in the context of transit route choice
  54. 54. Proof of concept</li></li></ul><li>On-going developments<br /><ul><li>Transit operation strategies</li></ul>Preparing a field study for testing the even-headway control strategy<br />Optimizing the number and location of time-point stops<br />Analyzing the case of a common corridor<br /><ul><li>Choice-set generation model</li></ul>Formulating an estimation method <br />Implementing the method using the survey data<br /><ul><li>Designing a validation study of the transit assignment model</li>

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