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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