Worldwide, road safety of pedestrians has been recognized as a public health concern. Previous studies have been conducted to analyze pedestrian safety at the macro level, but only a few were conducted in China. This study aims at investigating the impacts of potential contributing factors in pedestrian-involved collisions at zonal level in an urban area in China. The study included 138 traffic analysis zones. Both generalized linear regression and full Bayesian macro-level collision prediction models were developed to examine the correlations between pedestrian crashes and multiple variables. Results show that pedestrian crashes are positively correlated with exposure, population density, density of 4-way signalized intersections, and percentage of arterials, and they are negatively correlated with density of 3-way intersections and percentage of collectors. The findings of this study could help decision makers, planners and safety engineers gain better understanding of pedestrian crashes and develop countermeasures to enhance pedestrian safety.


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    Titel :

    Pedestrian Road Safety Analysis Based on Macro-Level Collision Prediction Models


    Beteiligte:
    Tian, Zhun (Autor:in) / Zhang, Shengrui (Autor:in)

    Kongress:

    21st COTA International Conference of Transportation Professionals ; 2021 ; Xi’an, China


    Erschienen in:

    CICTP 2021 ; 1771-1780


    Erscheinungsdatum :

    14.12.2021




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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