This article delves into urban road signal intersections, employing a combination of field research and survey methods. It synergizes with the logic regression theory to distill the critical influencing factors that could trigger pedestrian red-light violations, further subjecting their impact to a fitting analysis. Building upon this foundation, it leverages the principles of supervised learning and technology, utilizing five classifiers: random forests, decision trees, multilayer perceptrons, logistic regression, and naive Bayes. This comprehensive approach is aimed at training a pedestrian red-light violation classifier, facilitating automated detection of such behavior at signal intersections.


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    Titel :

    Analysis and Automatic Detection of Pedestrians Running Red Light at Signal Intersection


    Beteiligte:
    Sun, Ao (Autor:in) / Zhao, Xia (Autor:in) / Sun, Xu (Autor:in)

    Kongress:

    24th COTA International Conference of Transportation Professionals ; 2024 ; Shenzhen, China


    Erschienen in:

    CICTP 2024 ; 2109-2118


    Erscheinungsdatum :

    11.12.2024




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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