This study explores the impact of long-term traffic violations on accident risk, a frequently overlooked aspect in existing research. Analyzing violation data from Guilin, China, over a decade (2010-2020), including accident records from 2016-2020, the study categorizes drivers into three groups based on their cumulative violations. Given the challenges posed by high-dimensional data and the need for interpretability, the study employs a combination of CatBoost and SHAP models to assess factors influencing accident severity across these driver groups. Results indicate that for drivers without cumulative violations, road type, vehicle type, household registration, and age significantly influence accident risk. Among those with 1–18 cumulative violations, road type, cumulative penalty points, vehicle type, and cumulative fines emerge as significant factors. Drivers with more than 19 cumulative violations are affected by road type, vehicle type, driving experience, and terrain. Notably, penalty points are more effective than fines in reducing the risk of serious accidents, and fines prove significant only for those with fewer than 19 cumulative violations over a decade. In summary, the cumulative violation count effectively distinguishes driver groups, highlighting marked variations in accident risk factors. This research offers valuable insights for devising region-specific measures to enhance road safety.


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    Titel :

    Cumulative effect of driver’s historical violations on traffic accidents: New evidences from China


    Weitere Titelangaben:

    T. Wang et al.
    Journal of Transportation Safety & Security


    Beteiligte:
    Wang, Tao (Autor:in) / Liu, Hao (Autor:in) / Yan, Xingchen (Autor:in) / Ye, Xiaofei (Autor:in) / Ma, Changxi (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.12.2024


    Format / Umfang :

    33 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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