Traffic light control plays a crucial role in intelligent transportation systems. This paper introduces Temporal Difference-Aware Graph Convolutional Reinforcement Learning (TeDA-GCRL), a decentralized RL-based method for efficient multi-intersection traffic signal control. Specifically, we put forward a new graph architecture using each lane as a node for considering intersection relations. Additionally, we propose two new rewards by considering temporal information, namely Temporal-Aware Pressure on Incoming Lanes (TAPIL) and Temporal-Aware Action Consistency (TAAC), which enhance learning efficiency and time-interval sensitivity. Experimental results on five datasets show the superiority of TeDA-GCRL over state-of-the-art methods by at least 9.5% in average travel time.


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    Titel :

    Temporal Difference-Aware Graph Convolutional Reinforcement Learning for Multi-Intersection Traffic Signal Control


    Beteiligte:
    Lin, Wei-Yu (Autor:in) / Song, Yun-Zhu (Autor:in) / Ruan, Bo-Kai (Autor:in) / Shuai, Hong-Han (Autor:in) / Shen, Chih-Ya (Autor:in) / Wang, Li-Chun (Autor:in) / Li, Yung-Hui (Autor:in)


    Erscheinungsdatum :

    01.01.2024


    Format / Umfang :

    1732065 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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