Traffic Accident Risk Prediction of Tunnel Based on Multi-Source Heterogeneous Data Fusion

Abstract

In order to improve the prediction accuracy, this paper proposes a traffic accident risk prediction method of tunnel based on multi-source heterogeneous data fusion. Firstly, the feature extraction and coding model based on Gabor cloud image is constructed, and the unstructured cloud image is enhanced by amplitude feature level and then encoded by fusion. Secondly, the long sequence formed after the sample concatenation of the multi-source heterogeneous data of tunnel is used as the input of the prediction model GRU (Gate Recurrent Unit), and AdaBoost (Adaptive Boosting) is introduced to learn the prediction results to further improve the robustness of the prediction model. The experimental results show that the proposed GRU-AdaBoost model achieves the prediction accuracy of 84.59% when the data volume is 3 years and the time interval is 5 weeks. When the data volume is 1 year and the time interval is 3 weeks, the prediction accuracy can reach 80.85%, which is 9.61% higher than traditional prediction models.

Keywords

• Traffic accident risk prediction
• multi-source heterogeneous data fusion
• cloud image feature extraction