Classification of Driving Tendency of Commercial Truck Drivers Based on AdaBoost Algorithm

In order to study the differences in the driving behavior of truck drivers, a classified management of truck drivers is implemented. Obtain the vehicle driving data of 51 commercial truck drivers in natural driving conditions through the on-board OBD device, and preprocess the original data, including detecting abnormal values, time processing, filling missing values, deleting parking data, etc. On the basis of standardizing the data of commercial cargo vehicles, the index is reduced by factor analysis to obtain the speed control behavior clusters of target vehicle drivers. By extracting variable speed factors and acceleration factors and clustering them according to factor scores, three types of driver's light, medium and heavy driving behaviors are obtained. Based on the K-means cluster analysis of the data, the AdaBoost algorithm is used to establish a classification model for the safety tendency of commercial truck drivers, and the truck drivers are divided into radical drivers and conservative drivers. First, the factor analysis method is used to extract the indicators of the two directions of speeding and acceleration, and then the K-means algorithm is used to classify from two perspectives, and finally the driver's different driving conditions can be analyzed. In addition, through further screening of all driving behavior indicators through K-means clustering, the adaboost algorithm is finally used to verify and analyze the clustering results to determine driver styles with different tendencies. Data verification classification results show that the average accuracy of the driving tendency classification model of commercial truck drivers based on the AdaBoost algorithm can reach 95.74%, which can effectively distinguish radical truck drivers from conservative truck drivers.