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Optimal traffic management policies for mixed human and automated traffic flows

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  • Bahrami, Sina
  • Roorda, Matthew J.

Abstract

Although Autonomous Vehicles (AVs) will enhance mobility and safety, their impact on congestion is not clear yet. AVs may increase roadway capacity due to their connectivity features. The capacity enhancement highly depends on the AV proportion in traffic. This study models user equilibrium traffic assignment when the link capacity is a function of AV proportion of traffic. The mixed traffic flow of AVs and human-driven vehicles is considered as a multiclass traffic assignment problem. This problem is formulated as a non-linear complementarity problem which is solved to find optimal traffic management policies. We show that simple policies such as AV exclusive links can improve network performance in mixed traffic of AVs and human-driven vehicles. We also show that if these policies are implemented the network performance would be very close to system optimal condition even when users choose their routes selfishly following a user equilibrium. Results of numerical examples for a real size network show that management policies can decrease the gap between user equilibrium and system optimal to less than 1%.

Suggested Citation

  • Bahrami, Sina & Roorda, Matthew J., 2020. "Optimal traffic management policies for mixed human and automated traffic flows," Transportation Research Part A: Policy and Practice, Elsevier, vol. 135(C), pages 130-143.
    • Handle: RePEc:eee:transa:v:135:y:2020:i:c:p:130-143
    DOI: 10.1016/j.tra.2020.03.007
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    4. Bowen Gong & Fanting Wang & Ciyun Lin & Dayong Wu, 2022. "Modeling HDV and CAV Mixed Traffic Flow on a Foggy Two-Lane Highway with Cellular Automata and Game Theory Model," Sustainability, MDPI, vol. 14(10), pages 1-18, May.
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    7. Liang, Qingnan & Li, Xin-an & Chen, Zhibin & Pan, Tianlu & Zhong, Renxin, 2023. "Day-to-day traffic control for networks mixed with regular human-piloted and connected autonomous vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 178(C).

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