- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
- Chinese S&T Journal Citation Reports
- Chinese Science Citation Database
| Citation: | HU Liwei, HOU Zhi, ZHAO Xueting, LIU Bing, CHEN Chen, HE Yu, ZHANG Ruijie. Improvement of Highway Traffic Risk Prediction Method Based on Traffic Accident Text Mining[J]. Journal of Southwest Jiaotong University, 2025, 60(6): 1487-1498. doi: 10.3969/j.issn.0258-2724.20230290
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To effectively solve the problems of long highway inspection mileage and control difficulty, the applicability of the existing bidirectional long and short-term memory network (BiLSTM) text classification model and convolutional neural network (CNN) risk prediction model was improved, and the historical road traffic accident text data were analyzed and mined. The road segmentation method was introduced to accurately predict the distribution of highway driving risks and realize the scientific control of highway driving safety. Firstly, the text of traffic accidents was classified by the improved BiLSTM based on a self-attention mechanism (BiLSTM-AT), and the corresponding accident risk level of each accident was obtained. Second, the highway was divided into segments in ArcGIS, and the driving risk level within each segment was counted; kernel density analysis was performed to visualize the text classification results and show the risk level in different areas. Finally, the CNN based on LSTM (CNN-LSTM) was used to conduct time series prediction for the classified risk levels, obtaining the spatial distribution of future highway driving risks and drawing the cloud map of highway driving risk levels. The results show that the accuracy of the BiLSTM-AT model reaches 95.03% in terms of accident text classification, which is 0.91% and 0.67% higher than that of the BiLSTM and gate recurrent unit (GRU), respectively; the average relative error and root mean square error of the CNN-LSTM are 0.04 and 0.07, respectively, in terms of risk prediction, which are lower than that of the suboptimal LSTM model by 9.05% and 6.84%, respectively. The proposed method that closely connects accident text classification, segment division, driving risk prediction, and result visualization can effectively extract and analyze the driving risk information in the traffic accident text and provide a reference for optimizing the highway inspection routes and the traffic control of key segments.
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