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

Siyao Xiao

doi:10.1117/12.2614987

15 October 2021 Traffic accident duration prediction based on natural language processing and a hybrid neural network architecture

Siyao Xiao

Author Affiliations +

Proceedings Volume 11933, 2021 International Conference on Neural Networks, Information and Communication Engineering; 119330W (2021) https://doi.org/10.1117/12.2614987
Event: 2021 International Conference on Neural Networks, Information and Communication Engineering, 2021, Qingdao, China

Abstract

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.

Citation Download Citation

Siyao Xiao "Traffic accident duration prediction based on natural language processing and a hybrid neural network architecture", Proc. SPIE 11933, 2021 International Conference on Neural Networks, Information and Communication Engineering, 119330W (15 October 2021); https://doi.org/10.1117/12.2614987

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KEYWORDS

Data modeling

Neural networks

Social networks

Evolutionary algorithms

Convolutional neural networks

Feature extraction

Roads

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