Commercial Vehicle Driving Risk Recognition Model Based on XGBoost Algorithm
Drivers are an important cause of traffic accidents,and a series of risky driving behaviors have an important impact on road traffic safety.Aiming at the current problems of unreasonable classification of driving risk levels and low identification accu-racy,a commercial vehicle driving risk recognition model is proposed.Firstly,from three aspects of vehicle state,driving state and driver operation,18 characteristic parameters that can represent the driving risk of commercial vehicles are established.Fac-tor analysis method(FA)is used to optimize the dimensionality reduction of characteristic parameters,and comprehensive vari-ables containing more explicit information of risky driving behaviors are generated.Then,the K-means clustering algorithm is applied to cluster risky driving behavior characteristics into 2,3 and 4 categories and make a comparative analysis.Combined with elbow rule and contour coefficient,the best number of clusters is determined to eliminate the subjective defect of artificial em-pirical determination of k value.Finally,the Extreme Gradient Boosting(XGBoost)algorithm is used to recognize the driving risk of commercial vehicles,and the accuracy is compared with decision tree,random forest,k-nearest neighbor and other algo-rithms.The research results show that under the above research conditions,XGBoost algorithm has the highest theoretical identi-fication rate of commercial vehicle driving risk,up to 98%,which is of great significance for the design of automatic driving as-sistance system and the improvement of road traffic safety.
NETL
NSTL
万方数据
