In order to accurately predict the number of traffic accidents and better solve road safety problems, this paper presents a time series prediction model based on an J-LSTM + Attention mechanism, using road traffic accident data and meteorological data from the city of CURITIBA, Brazil, as the research object, and improving the internal gating unit structure of the LSTM model. The traffic accident dataset is fitted and predicted. The results show that the prediction effects of the road traffic accident prediction model based on the J-LSTM + Attention mechanism are all better than those of the classical LSTM model, BP neural network and SVR model, and the overall effect of the model is better, which is of great practical significance for improving road traffic management.


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    Titel :

    Road Traffic Accident Prediction Model Based on J-LSTM+Attention Mechanism


    Beteiligte:
    Wang, Shunshun (Autor:in) / Yan, Changshun (Autor:in) / Shao, Yong (Autor:in)


    Erscheinungsdatum :

    26.05.2023


    Format / Umfang :

    925305 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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