To improve the traffic efficiency of city-wide road networks, we propose a traffic signal control framework that prioritizes the optimal control policies on critical nodes in road networks. In this framework, we first use a data-driven approach to discover the critical nodes. Critical nodes are identified as nodes that would cause a dramatic reduction in the traffic efficiency of the road network if they were to fail. This approach models the dynamic of road networks using a tripartite graph based on the vehicle trajectories and can accurately identify the city-wide critical nodes from a global perspective. Second, for the discovered critical nodes, we introduce a novel traffic signal control approach based on deep reinforcement learning; this approach can learn the optimal policy via constantly interacting with the road network in an iterative mode. We conduct several experiments with a transportation simulator; the results of experiments show that the proposed framework reduces the average delay and travel time compared to the baseline methods.


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    Titel :

    Network-wide traffic signal control based on the discovery of critical nodes and deep reinforcement learning


    Beteiligte:
    Xu, Ming (Autor:in) / Wu, Jianping (Autor:in) / Huang, Ling (Autor:in) / Zhou, Rui (Autor:in) / Wang, Tian (Autor:in) / Hu, Dongmei (Autor:in)


    Erscheinungsdatum :

    2020-01-02


    Format / Umfang :

    10 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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