IRF-HTID-BO-LSTM: Classification Model of Curve Shape Index for Mountainous Highways and Intelligent Traffic Incident Detection Method

Abstract

In response to the special geographical environment and traffic conditions of mountainous highways, reasonable highway structure design can significantly improve traffic safety and reduce traffic accidents. Therefore, a grading model and traffic incident detection method for mountainous highway curve line indicators are developed. By analyzing the traffic conditions and highway structure of mountainous highways, a classification algorithm based on highway curve structure indicators is proposed, and a mountainous highway curve structure grading model is constructed. Then, a long short-term memory network is introduced to design a highway traffic incident detection algorithm on the basis of Bayesian optimization. The results showed that the correlation fitting degree of curve index classification based on the classification model was 87.3%. With the increase of feature variables in the data set, the classification accuracy of the traffic incident detection method for different events showed a steady increase and reached a stable state of 92.8%. The accuracy of the most advanced method was only 90%, and the accuracy of the research method was higher than that of the most advanced method. The comprehensive performance showed that the area under the curve value of the proposed method was as high as 0.982, which was larger than other comparison algorithms. In addition, the area under the curve value of the most advanced method was 0.962. The above results demonstrate that the designed algorithm has good performance, which can effectively segment the curve shape indicators of highway structures, and accurately detect traffic incidents.