The accurate prediction of accident severity has become an active area of research in recent years, although studies in certain regions such as South Asia and Sub-Saharan Africa are comparatively less. In this study, we aim to contribute in many ways: (i) we conduct an analytical review of the literature to gauge the interest and scope of existing studies and identify the direction for further research, and (ii) a mixture of old and relatively new artificial intelligence (AI) techniques is applied to road accident data of India (iii) we employ shapley additive explanations (SHAP) for interpretation of AI model predictions, and (iv) an AI-enabled accident management system is proposed. The findings suggest that AI models are capable of predicting the accident severity. Precisely, the gradient boosting machine attains the best test accuracy. Among features, commercial vehicles, excess speed, national highways, and pedestrians’ fault are responsible for accidental road killings.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Predicting and explaining severity of road accident using artificial intelligence techniques, SHAP and feature analysis


    Beteiligte:

    Erschienen in:

    Erscheinungsdatum :

    04.03.2023


    Format / Umfang :

    16 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




    Using LightGBM with SHAP for predicting and analyzing traffic accidents severity

    Li, Jinqiang / Guo, Yuying / Li, Li et al. | IEEE | 2023




    Evaluation of database balancing techniques for road accident severity classification employing Artificial Neural Network

    Maria Lígia Chuerubim / Leonardo N. Ferreira / Alan D.B. Valejo et al. | DOAJ | 2020

    Freier Zugriff

    Explainable Aircraft Crash Severity Prediction Using XGBoost and SHAP

    Benfaress, Ilyass / Bouhoute, Afaf / Zinedine, Ahmed | IEEE | 2025