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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    Title :

    Calibration-free Traffic Signal Control Method Using Machine Learning Approaches


    Contributors:
    Zhang, Liang (author) / Lin, Wei-Hua (author)


    Publication date :

    2022-07-20


    Size :

    1224763 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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