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.
Analysis and Automatic Detection of Pedestrians Running Red Light at Signal Intersection
24th COTA International Conference of Transportation Professionals ; 2024 ; Shenzhen, China
CICTP 2024 ; 2109-2118
2024-12-11
Conference paper
Electronic Resource
English
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