CoSLight: Co-optimizing Collaborator Selection and Decision-making to Enhance Traffic Signal Control

Effective multi-intersection collaboration is pivotal for reinforcement-learning-based traffic signal control to alleviate congestion. Existing work mainly chooses neighboring intersections as collaborators. However, quite an amount of congestion, even some wide-range congestion, is caused by non-ne...

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Veröffentlicht in:arXiv.org 2024-06
Hauptverfasser: Ruan, Jingqing, Li, Ziyue, Hua, Wei, Jiang, Haoyuan, Lu, Jiaming, Xiong, Xuantang, Mao, Hangyu, Zhao, Rui
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Sprache:eng
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Zusammenfassung:Effective multi-intersection collaboration is pivotal for reinforcement-learning-based traffic signal control to alleviate congestion. Existing work mainly chooses neighboring intersections as collaborators. However, quite an amount of congestion, even some wide-range congestion, is caused by non-neighbors failing to collaborate. To address these issues, we propose to separate the collaborator selection as a second policy to be learned, concurrently being updated with the original signal-controlling policy. Specifically, the selection policy in real-time adaptively selects the best teammates according to phase- and intersection-level features. Empirical results on both synthetic and real-world datasets provide robust validation for the superiority of our approach, offering significant improvements over existing state-of-the-art methods. The code is available at https://github.com/bonaldli/CoSLight.
ISSN:2331-8422