Predicting the duration of traffic accidents can effectively help traffic management. To make a more accurate real-time prediction of traffic accident duration, and fully utilize the huge amount of traffic texts in social networks, in this paper, we consider this prediction task as a classification problem. First, the reported text of traffic accidents in social networks is obtained. After the data augmentation, the Bag-of-words model and Fisher optimal segmentation algorithm are combined to calculate the optimal classification threshold based on duration, and the accidents are classified into four classes. And then, the C-BiLSTM neural network is constructed by fusing convolutional neural network (CNN) and bidirectional long short term memory (Bi-LSTM) to predict the classes of accident durations, and the prediction accuracy of final trained model can reach 96.09%. Through experiments, the proposed method is proved to be practical and effective in solving traffic accident duration prediction.


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    Titel :

    Traffic accident duration prediction based on natural language processing and a hybrid neural network architecture


    Beteiligte:
    Xiao, Siyao (Autor:in)

    Kongress:

    2021 International Conference on Neural Networks, Information and Communication Engineering ; 2021 ; Qingdao,China


    Erschienen in:

    Proc. SPIE ; 11933


    Erscheinungsdatum :

    15.10.2021





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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