A Data-Driven Dynamic Route Choice Model Under Uncertainty Using Connected Vehicle Trajectory Data

This paper proposes a data-driven dynamic route choice model to understand traveler’s routing behavior in a time-dependent network under uncertainty using connected vehicle trajectory data over many days. Different from existing efforts on stochastic route choice models using a random term with a given distribution, this paper directly uses connected vehicle trajectory data over many days without knowing the underlying distribution in a data-driven stochastic optimization model. Specifically, the authors apply a Bayesian risk formulation for parametric underlying distributions that optimizes a risk measure taken with respect to the posterior distribution estimated from the connected vehicle trajectory data. Two risk measures (i.e. Value-at-Risk and Conditional Value-at-Risk) of the travel time uncertainty are considered in the proposed data-driven dynamic route choice model. Based on the risk measures, the proposed model allows a flexible choice on the risk preferences of individual users (i.e. from risk-neutral to risk-averse). To test the data-driven dynamic route choice model in a large network, the authors implement the model in Southeast Michigan using a high-resolution (i.e. 0.1 seconds) trajectory dataset of connected vehicles from the Safety Pilot Model Deployment (SPMD) project over many days.

  • Supplemental Notes:
    • This paper was sponsored by TRB committee ADB30 Standing Committee on Transportation Network Modeling.
  • Authors:
    • Zhao, Shuaidong
    • Zhang, Kuilin
  • Conference:
  • Date: 2018


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; Maps; Photos; References;
  • Pagination: 6p

Subject/Index Terms

Filing Info

  • Accession Number: 01660424
  • Record Type: Publication
  • Report/Paper Numbers: 18-04210
  • Files: TRIS, TRB, ATRI
  • Created Date: Feb 20 2018 9:28AM