Traffic violations of pedestrians at intersections are major causes of road crashes involving pedestrians, especially red-light crossing behaviors. To predict the pedestrians’ red-light crossing intentions, video data from real traffic scenes are collected. Using detection and tracking techniques in computer vision, some pedestrians’ characteristics, including location information, are generated. A long short-term memory neural network is established and trained to predict pedestrians’ red-light crossing intentions. The experimental results show that the model has an accuracy rate of 91.6% based on internal testing at one signalized crosswalk. This model can be further implemented in the vehicle-to-infrastructure communication environment and prevent crashes because of the pedestrians’ red-light crossing behaviors.


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    Titel :

    Prediction of Pedestrian Crossing Intentions at Intersections Based on Long Short-Term Memory Recurrent Neural Network


    Weitere Titelangaben:

    Transportation Research Record: Journal of the Transportation Research Board


    Beteiligte:
    Zhang, Shile (Autor:in) / Abdel-Aty, Mohamed (Autor:in) / Yuan, Jinghui (Autor:in) / Li, Pei (Autor:in)


    Erscheinungsdatum :

    03.03.2020




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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