This paper proposes a machine learning method based on a two-layer Stacking ensemble learning strategy to predict the severity of traffic accidents. In the first layer, the prediction results obtained by learning from three base learners, namely KNN, XGBoost, and LightGBM, are combined and input as new features into the second-layer meta-learner LR, thus establishing a combined prediction model. The experiment selects traffic accident data from the UK for verification and analysis. The results show that this prediction model has more superior prediction accuracy and generalization performance compared to other single models. The prediction accuracy of the traffic accident severity on the test set reaches 0.889, the AUC value is 0.905, and it outperforms other models in terms of precision, recall, and F1 value. This research can assist traffic management departments in preventing traffic accidents, thereby reducing the severity of traffic accidents and decreasing the occurrence of traffic accidents.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Ensemble learning-based prediction of road traffic accident severity


    Beteiligte:
    Feng, Zhengang (Herausgeber:in) / Mikusova, Miroslava (Herausgeber:in) / Yu, Yue (Autor:in) / Chen, Zhi (Autor:in)

    Kongress:

    International Conference on Smart Transportation and City Engineering (STCE 2024) ; 2024 ; Chongqing, China


    Erschienen in:

    Proc. SPIE ; 13575


    Erscheinungsdatum :

    28.04.2025





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Prediction of road traffic accident severity based on multi-model fusion

    Lu, Wenting / Huang, Mingxia / Yu, Rongze | British Library Conference Proceedings | 2023


    Ensemble Learning Algorithms based on Road Accident Data Prediction

    Reddy Gondi Konda / Vullam Nagagopi Raju / Sekhar G. Soma et al. | DOAJ | 2025

    Freier Zugriff


    Traffic accident severity prediction based on interpretable deep learning model

    Pei, Yulong / Wen, Yuhang / Pan, Sheng | Taylor & Francis Verlag | 2025


    Road traffic accident prediction method

    WANG SHUNSHUN / YAN CHANGSHUN / SHAO YONG | Europäisches Patentamt | 2023

    Freier Zugriff