To analyze influencing factors of rider injury severity in E-bike-motor vehicle collision accidents, we have analyzed 1,246 accidents in the China In-depth Accident Study (CIDAS) dataset using the Light Gradient Boosting Machine (LightGBM) model, which is used to further explore primary factors affecting the injury of riders. Experimental results show that severity of injury is highly correlated with traffic environment, characteristics of electric vehicles and riders. Meanwhile, we find that the throwing distance of riders has an obvious threshold effect on death, and accidents occurred outside urban areas are more likely to lead to death of riders. Finally, suggestions are made for the causes of accident injuries. This research is beneficial to the understanding of complex nonlinear relationships between accident injuries and influencing factors. The experimental results and analysis provide more guidelines to investigate injury prevention measures.


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    Titel :

    Analysis of factors affecting riders injury severity in E-bike-motor vehicle collision accidents based on CIDAS data


    Beteiligte:
    Wang, Peng (Autor:in) / Lin, Miao (Autor:in) / Li, XiaoHu (Autor:in) / Wang, WenXia (Autor:in) / Wei, Wen (Autor:in)

    Kongress:

    Seventh International Conference on Electromechanical Control Technology and Transportation (ICECTT 2022) ; 2022 ; Guangzhou,China


    Erschienen in:

    Proc. SPIE ; 12302


    Erscheinungsdatum :

    2022-11-23





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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