Recent advancements in connected automated vehicles (CAVs) and reinforcement learning (RL) hold significant promise for enhancing intelligent traffic control systems. This paper conducts a systematic review of studies on RL-based urban traffic control at signalised intersections, highlighting the significant impact of CAVs on traffic control performance improvement. We first review the fundamental concepts of RL algorithms, establishing a foundational understanding for subsequent RL-based traffic control methods. We then review recent progress in RL-based traffic signal control using CV/CAV trajectory data, RL-based CAV trajectory planning, and the cooperative control of both traffic signals and CAVs at signalised intersections. Our aim is to provide researchers with a comprehensive roadmap for future research in RL-based traffic control at signalised intersections.


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    Title :

    A survey on reinforcement learning-based control for signalized intersections with connected automated vehicles


    Additional title:

    TRANSPORT REVIEWS
    K. ZHANG ET AL.


    Contributors:
    Zhang, Kaiwen (author) / Cui, Zhiyong (author) / Ma, Wanjing (author)

    Published in:

    Transport Reviews ; 44 , 6 ; 1187-1208


    Publication date :

    2024-11-01


    Size :

    22 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English