The purpose of this study is to minimize the negative influences of the severe traffic accidents in China by profoundly analyzing the complex coupling relations among accident factors contributing to the single-vehicle and multivehicle traffic accidents with the Bayesian network (BN) crash severity model. The BN model was established by taking the critical factors identified with the improved grey correlation analysis method as node variables. The severe traffic accident data collected from accident reports published in China were used to validate this model. The model’s efficiency was validated objectively by comparing the conditional probability obtained by this model with the actual value. The result shows that the BN model can reflect the real relations among factors and can be seen as the target network for the severe traffic accidents in China. Besides, based on BN’s junction tree engine, five-factor combination sequences for the number of deaths and three-factor combination sequences for the number of injuries were ranked according to the severity degree to reveal the critical reasons and reduce the massive traffic accidents damage.


    Access

    Download


    Export, share and cite



    Title :

    Critical Factors Analysis of Severe Traffic Accidents Based on Bayesian Network in China


    Contributors:
    Hong Chen (author) / Yang Zhao (author) / Xiaotong Ma (author)


    Publication date :

    2020




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown





    The Analysis of Urban Traffic Accidents Based on Bayesian Network

    Li, Wenyong / Zheng, Shuqing / Lu, Yuan | ASCE | 2018


    Analysis of Factors Influencing Road Traffic Accidents’ Severity Based on Bayesian Networks

    Wei, Panyi / Huang, Jianling / Chen, Yanyan et al. | ASCE | 2022


    Seat belt wearing in severe traffic accidents

    Tolonen,J. / Santavirta,S. / Kviluoto,O. et al. | Automotive engineering | 1983


    Analysis of Roadway Traffic Accidents Based on Rough Sets and Bayesian Networks

    Xiaoxia Xiong / Long Chen / Jun Liang | DOAJ | 2018

    Free access