Coordinated Ramp Metering Considering the Dynamics of Mixed-Autonomy Traffic

Hongxin Yu, Lihui Zhang (), Meng Zhang, Fengyue Jin and Yibing Wang
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Hongxin Yu: College of Civil Engineering and Architecture, Balance Architecture Research Center, Zhejiang University, Hangzhou 310058, China
Lihui Zhang: College of Civil Engineering and Architecture, Architectural Design and Research Institute, Zhejiang University, Hangzhou 310058, China
Meng Zhang: College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
Fengyue Jin: College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
Yibing Wang: College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China

Sustainability, 2024, vol. 16, issue 22, 1-26

Abstract: The introduction of connected autonomous vehicles may bring opportunities and challenges to traditional traffic control instruments, like ramp metering. This paper starts by constructing the fundamental diagram for mixed-autonomy traffic based on the car-following behaviors of both connected autonomous vehicles and human-driven vehicles. Then, analyses are performed on the main factors that influence the critical velocity, critical density, and road capacity under mixed-autonomy traffic. Two methods named COE-HERO and TRLCRM are developed to support the implementations of coordinated ramp metering for freeways with mixed-autonomy traffic. The COE-HERO method enhances the HERO method by incorporating a critical occupancy estimation module. Both COE-HERO and TRLCRM consider dynamic traffic flow parameters of mixed-autonomy traffic. The TRLCRM method is a reinforcement learning-based approach with a two-stage training framework, enabling it to adapt to varying mixed-autonomy demand scenarios. Extensive microscopic simulations show that the learning-based TRLCRM method can effectively alleviate bottleneck congestion and is robust to deal with various traffic scenarios. The COE-HERO method performs better than the HERO method, indicating the necessity of critical occupancy estimation in the implementations of coordinated ramp metering.

Keywords: coordinated ramp metering; mixed-autonomy traffic; connected autonomous vehicles; critical occupancy estimation; multi-agent reinforcement learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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