Abstract Traffic signal control plays a crucial role in managing traffic flow and alleviating congestion on urban roads. This study proposes a deep reinforcement learning (DRL) approach to optimize traffic signal control and reduce traffic congestion in urban environments. Leveraging Perceiver transformers and a deep neural network, the approach uses traffic flow data — including speed, vehicle arrivals, and other relevant metrics — to enhance signal regulation. The DRL framework is based on the Q-learning algorithm and operates without relying on specific traffic models or rules. To evaluate different traffic control strategies, we introduce a benchmark map of Žilina city as a testing ground. Our method establishes a robust framework for optimising urban traffic, achieving significant improvements in traffic flow efficiency and congestion reduction, as demonstrated through simulations on this map.


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    Titel :

    Exploring Urban Traffic Dynamics: Introducing a Benchmark Map for Comprehensive Testing and Evaluation


    Weitere Titelangaben:

    Int. J. ITS Res.


    Beteiligte:
    Skuba, Michal (Autor:in) / Janota, Aleš (Autor:in) / Kuchár, Pavol (Autor:in) / Kafková, Júlia (Autor:in) / Hruboš, Marián (Autor:in) / Michálik, Mário (Autor:in) / Han, Longrui (Autor:in) / Kovačovič, Patrik (Autor:in)


    Erscheinungsdatum :

    13.06.2025




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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