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:
  • Transportation Research Board 97th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2018-1-7 to 2018-1-11
• Date: 2018

Language

• English

Media Info

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

Subject/Index Terms

• TRT Terms: Choice models; Connected vehicles; Dynamic models; Risk assessment; Route choice; Trajectory; Travel behavior; Uncertainty
• Identifier Terms: Safety Pilot Model Deployment
• Geographic Terms: Southeast Michigan
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting;

Filing Info

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