Beyond centralization: Non-cooperative perimeter control with extended mean-field reinforcement learning in urban road networks

Xinghua Li, Xinyuan Zhang, Xinwu Qian, Cong Zhao, Yuntao Guo and Srinivas Peeta

Transportation Research Part B: Methodological, 2024, vol. 186, issue C

Abstract: Perimeter control is a traffic management approach aimed at regulating vehicular accumulation within urban regional networks by managing flows on all border-crossing roads. Methods based on the macroscopic fundamental diagram (MFD) fall short in providing specific metering for individual roads. Recent advancements in the cell transmission model (CTM) have attempted to address this limitation but are hindered by their reliance on centralized control, which requires the availability of full information and authority over traffic generation sites. Our study proposes an innovative decentralized, game-theoretical framework for perimeter control to address these practical challenges. It is structured around two key groups of agents: perimeter agents, tasked with managing border roads, and interior agents, focused on traffic within generation sites. The framework also incorporates mechanisms for interactions between these agents and the road network, aiming to optimize their individual utilities. Additionally, we have developed a multi-agent reinforcement learning (RL) algorithm, extending the mean-field theory concept, to address the complexity of simultaneous learning by multiple agents.

Keywords: Perimeter control; Macroscopic fundamental diagram; Cell transmission model; Decentralized control framework; Mean-field approximation; Multi-agent reinforcement learning (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.trb.2024.103016

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