Road Traffic Accidents (RTAs) are a serious safety issue, especially in fast-growing cities, and have become one of the leading causes of death worldwide. This study takes Addis Ababa, Ethiopia, as a case study for the period from 2017 to 2020 and uses advanced interpretable machine learning techniques to analyse the key features that influence road safety. The results highlight the superior performance of the Random Forest model. Interestingly, findings indicate that a large number of accidents occurred under normal road and weather conditions, highlighting the significant influence of driver characteristics. This study provides relevant authorities with effective strategies to significantly reduce mortality in persistent RTAs.


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    Titel :

    Guardians of the road: machine learning solutions for safer commutes


    Beteiligte:
    Yao, Xinwei (Herausgeber:in) / Kumar, Neeraj (Herausgeber:in) / Peng, Qiao (Autor:in) / He, Honghao (Autor:in) / Gao, Ying (Autor:in) / Zhang, Taicheng (Autor:in)

    Kongress:

    Fourth International Conference on Smart City Engineering and Public Transportation (SCEPT 2024) ; 2024 ; Beijin, China


    Erschienen in:

    Proc. SPIE ; 13160


    Erscheinungsdatum :

    16.05.2024





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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