This study aims to improve the accuracy and interpretability of traffic accident severity nowcasting by introducing a stacked Recurrent Neural Network (RNN) deep learning model. Accurately predicting traffic accident severity is crucial for enhancing traffic management and reducing the impact of accidents. We employed a stacked Bidirectional Gated Recurrent Unit (GRU) - Long Short Term Memory (LSTM) model with an attention mechanism, integrating multivariate accident data to capture complex temporal dynamics. The use of SHapley Additive exPlanations (SHAP) values enhances the interpretability of the model. The model demonstrates high reliability and effectiveness, achieving an accuracy of 88.06% and an F1-score of 0.867 in real-time applications. It provides valuable insights into the factors influencing predictions, making the decision-making process transparent. This framework not only advances predictive performance but also aligns with ethical AI deployment, making it a valuable tool for traffic management and policy formulation.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Explainable Traffic Accident Severity Prediction with Attention-Enhanced Bidirectional GRU-LSTM


    Beteiligte:

    Erschienen in:

    Erscheinungsdatum :

    02.12.2024


    Format / Umfang :

    620927 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Comparison of traffic accident injury severity prediction models with explainable machine learning

    Cicek, Elif / Akin, Murat / Uysal, Furkan et al. | Taylor & Francis Verlag | 2023


    Fusion attention mechanism bidirectional LSTM for short-term traffic flow prediction

    Li, Zhihong / Xu, Han / Gao, Xiuli et al. | Taylor & Francis Verlag | 2024


    Traffic flow prediction method based on LSTM-Attention

    QIN XIAOLIN / LIU JIACHEN / SONG LIXIANG et al. | Europäisches Patentamt | 2021

    Freier Zugriff

    Unidirectional and Bidirectional LSTM Models for Short-Term Traffic Prediction

    Rusul L. Abduljabbar / Hussein Dia / Pei-Wei Tsai | DOAJ | 2021

    Freier Zugriff