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PROTRIP: Probabilistic Risk-Aware Optimal Transit Planner.

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This study introduces a new routing method for urban transit that balances travel time and predictability. It helps passengers choose routes with a higher chance of on-time arrival, considering real-world traffic correlations.

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Area of Science:

  • Operations Research
  • Transportation Science
  • Urban Planning

Background:

  • Traditional transit routing often uses least expected travel time (LET), ignoring travel time variability and spatial correlations in urban congestion.
  • High travel time variability can lead to unpredictable arrival times, which is undesirable for transit users.
  • Congestion effects cascade across urban networks, creating spatial correlations in travel times.

Purpose of the Study:

  • To develop a methodology and tool for optimal online route choice in transit networks.
  • To balance the objectives of maximizing on-time arrival probability and minimizing expected travel time.
  • To account for correlated travel times and utilize real-time information for improved routing.

Main Methods:

  • Proposing a novel routing framework that considers passenger tolerance for uncertainty and travel time budgets.
  • Incorporating the correlation between travel times of different network edges.
  • Updating downstream travel time distributions using upstream real-time traffic information.

Main Results:

  • Demonstrated the utility and performance of the proposed algorithm through realistic numerical experiments.
  • Showcased the ability to provide optimal routes that balance expected travel time and on-time arrival probability.
  • Validated the approach on a fixed-route bus system in the Champaign-Urbana metropolitan area.

Conclusions:

  • The proposed methodology offers a more realistic and user-centric approach to urban transit routing.
  • Accounting for travel time correlations and real-time data significantly improves route choice.
  • The tool provides a valuable solution for enhancing passenger experience and transit system efficiency.