Sponsor
The authors would like to acknowledge the National Science Foundation for funding this work through Award Number 1069141/ 1157294 “Collaborative Research: Stochastic and Dynamic Hyperpath Equilibrium Models” and Award Number 1562109/ 1562291 “Collaborative Research: Non‐additive Network Routing and Assignment Models.” This research was also partially supported by the U.S. Department of Transportation through the Data Supported Transportation Operations and Planning (D‐STOP) Tier 1 University Transportation Center.
Published In
Transportation Research Interdisciplinary Perspectives
Document Type
Article
Publication Date
6-5-2021
Subjects
Traffic monitoring, Real-time information
Abstract
Real-time information about traffic conditions is becoming widely available through various media and connected-vehicle technology. In such conditions, travelers have better knowledge about the system and adapt as the system evolves dynamically during their travel. Drivers may change routes during their travel in order to optimize their own objective of travel. Various travel objectives are captured in mathematical models via disutility functions. The focus of this research was to study the behavior of travelers with multiple trip objectives when they are provided real-time information, and assess their ability to determine “optimal” routing policies, compared to exact solutions based on the online shortest path problem. A web-based experiment was carried out to simulate a traffic network with limited information provision. The decision strategies of participants were analyzed and compared to a variety of decision policies established in the literature – optimal, greedy, and a priori – and a general model to describe the observed travelers’ decision strategies was calibrated from over 40,000 decision points extracted from the collected data. Apart from trip objective, other factors such as relative position in the network and experience gained were found to influence user decisions. This research is a step towards calibrating equilibrium models for adaptive behavior with multiple user classes.
Rights
Copyright (c) 2021 The Authors
This work is licensed under a Creative Commons Attribution 4.0 International License.
Locate the Document
DOI
10.1016/j.trip.2021.100395
Persistent Identifier
https://archives.pdx.edu/ds/psu/35786
Citation Details
Venkatraman, R., Boyles, S. D., James, R., Unnikrishnan, A., & Patil, P. N. (2021). Adaptive routing behavior with real-time information under multiple travel objectives. Transportation Research Interdisciplinary Perspectives, 10, 100395.