Many existing traffic signal control strategies are operated with data from roadside surveillance systems. In recent years, vehicle-based data have become more and more accessible for various applications. In this paper, we propose a calibration-free traffic signal control scheme using vehicle-based data as input. Traffic conditions are characterized as discrete queue cycle state (DQCS) which are then used as input to the calibration-free traffic signal control scheme with the reinforcement learning approach. The k-nearest neighbor algorithm is applied in our calibration-free model. The effectiveness of the proposed model is examined with different traffic scenarios.


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    Titel :

    Calibration-free Traffic Signal Control Method Using Machine Learning Approaches


    Beteiligte:
    Zhang, Liang (Autor:in) / Lin, Wei-Hua (Autor:in)


    Erscheinungsdatum :

    20.07.2022


    Format / Umfang :

    1224763 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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