A data-driven dynamic route choice model under uncertainty using connected vehicle trajectory data

Document Type

Article

Publication Date

1-8-2018

Department

Department of Civil, Environmental, and Geospatial Engineering; Center for Cyber-Physical Systems

Abstract

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.

Publisher's Statement

This paper was sponsored by TRB committee ADB30 Standing Committee on Transportation Network Modeling.

Publication Title

Transportation Research Board 97th Annual Meeting

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