Deep reinforcement learning has seen significant progress in traffic signal control. However, existing research still lacks the ability to effectively capture the correlation of road network information and the perception capability of traffic signal states. To address this gap, we propose a multi-intersection traffic signal control method that integrates a graph attention network, named the graph attention network-deep deterministic polcy gradient (GAT-DDPG) algorithm. This algorithm incorporates the restart random walk into the attention mechanism, exploring graph information through global random walks, reducing reliance on local nodes, and enhancing the model’s comprehensive understanding of graph structure features, thereby improving the modeling capability of traffic network structures. Moreover, the algorithm can automatically identify and extract key features from the complex data of the traffic network without manual intervention, adapting to different traffic network topologies, and can update and adjust the traffic signal control system in real-time to accommodate actual traffic flow and congestion situations. Experimental results indicate that the GAT-DDPG algorithm reduces average vehicle travel time significantly across three real road networks (Hangzhou and Jinan in China, and New York, U.S.) and two synthetic road network datasets. Additionally, it demonstrates optimal convergence speed and performance in these real datasets, attributed to its capability to capture global information and deeply comprehend the intricate structures of traffic networks. The research proves that this model has significant advantages in the field of traffic signal control, improving the operational efficiency of urban area intersections. Future work will incorporate additional road environment factors to better adapt to complex urban traffic.


    Access

    Download

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Multi-Agent Deep Reinforcement Learning with Graph Attention Network for Traffic Signal Control in Multiple-Intersection Urban Areas


    Additional title:

    Transportation Research Record: Journal of the Transportation Research Board


    Contributors:
    Yang, Guoqing (author) / Wen, Xin (author) / Chen, Fuqiang (author)


    Publication date :

    2025-02-02




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Multi-intersection traffic signal control method based on deep reinforcement learning

    DENG HENG / WANG YULONG / GAO YANG et al. | European Patent Office | 2024

    Free access


    Multi-intersection traffic signal control method based on deep reinforcement learning

    LIU LIJUAN / BAI GUANGMING | European Patent Office | 2023

    Free access

    Multi-agent reinforcement learning traffic signal cooperative control method considering intersection heterogeneity

    BIE YIMING / JI YUTING / JI JINHUA et al. | European Patent Office | 2024

    Free access