Networked traffic signal control (NTSC) is essential for intelligent transportation systems. How to control multiple intersections in a cooperative way based on traffic conditions is critical for the success of NTSC. This paper proposes a Transformer-based cooperation mechanism (TCM) with the consideration of dynamic modeling and scale requirements simultaneously for large-scale traffic network control. Considering the physical constraints in traffic scenarios, a relative position encoding is designed to embed into TCM to characterize traffic conditions better. With the shared TCM module, intersection controllers could adequately exploit spatial-temporal correlations and adaptively capture global traffic dynamics, guiding them to explore collaborative traffic strategies more efficiently. Experimental results on two real-world datasets demonstrate that the suggested strategy greatly outperforms the state-of-the-art methods.


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    Titel :

    Learning Transformer-based Cooperation for Networked Traffic Signal Control


    Beteiligte:
    Zhao, Chen (Autor:in) / Dai, Xingyuan (Autor:in) / Wang, Xiao (Autor:in) / Li, Lingxi (Autor:in) / Lv, Yisheng (Autor:in) / Wang, Fei-Yue (Autor:in)


    Erscheinungsdatum :

    08.10.2022


    Format / Umfang :

    1462706 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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