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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    Title :

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


    Contributors:
    Sun, Ao (author) / Zhao, Xia (author) / Sun, Xu (author)

    Conference:

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


    Published in:

    CICTP 2024 ; 2109-2118


    Publication date :

    2024-12-11




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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