Session 38 Oded Cats
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    Session 38 Oded Cats Session 38 Oded Cats Presentation Transcript

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