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Title: Managing mixed traffic with autonomous vehicles – a day-to-day routing allocation scheme
Authors: Guo, Zhihong
Wang, David Zhi Wei
Wang, Danwei
Keywords: Engineering::Civil engineering
Issue Date: 2022
Source: Guo, Z., Wang, D. Z. W. & Wang, D. (2022). Managing mixed traffic with autonomous vehicles – a day-to-day routing allocation scheme. Transportation Research Part C: Emerging Technologies, 140, 103726-.
Project: MOE2021-T1-002-062
Journal: Transportation Research Part C: Emerging Technologies
Abstract: In presence of the emerging technology of automated vehicles, it is anticipated that the future traffic system would be comprised of mixed traffic with both self-driving autonomous vehicles (AVs) and human-driven conventional vehicles. It is imperative to propose new traffic management measures to manage the future traffic system, as complement of the existing ones such as road pricing schemes. This study seeks to take advantage of the controllable property of AVs’ routing choices to develop a day-to-day routing allocation scheme for a certain number of autonomous vehicles so as to drive the mixed traffic system into a desired traffic state. Specifically, we assume that all travelers are bounded rational and AV users are willing to accept route allocation with route travel cost not exceeding HV users’ indifference band. Therefore, the best-case bounded rationality user equilibrium (BRUE) flow pattern is in principle the most desirable traffic state out of all the BRUE solutions. This study proposes a day-to-day AVs’ routing allocation scheme by which the traffic system would be directed to evolve towards the desired best-case BRUE. The day-to-day traffic dynamics of HVs are proposed to follow a general framework, which can be further proved to be the BRUE rational behavior adjustment process (BRUE-RBAP). The condition for convergence is investigated under general assumptions. Numerical examples are provided to demonstrate the effectiveness of this traffic management scheme.
ISSN: 0968-090X
DOI: 10.1016/j.trc.2022.103726
Rights: © 2022 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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