Commercial Truck Risk Assessment and Factor Analysis Based on Vehicle Trajectory and In-Vehicle Monitoring Data

Abstract

Truck crashes are generally more serious than passenger vehicle crashes, and they cause more deaths per crash worldwide per the U.S. Department of Transportation’s Fatality Analysis Reporting System. Risk assessment and factor analysis are the keys to preventing truck crashes, but research on commercial trucks has been limited. Currently, freight and insurance companies have collected extensive operating data, now making it possible to obtain deep insights into truck crashes. Vehicle trajectory data and in-vehicle monitoring data were collected for 596 large commercial trucks traveling in Shanghai, China, during 2019. A total of 22 variables were extracted, falling into three aspects: driving behavior, travel characteristics, and warning characteristics. The random forest algorithm was used to select the most important variables for further analysis. Four machine learning models and a mixed effects logistic regression model were developed to link the high-importance variables with crash risk. Results showed that the machine learning models had good predictive performance; the bagging tree model performed best overall, having achieved good performance in the majority of the metrics, with an accuracy of 96.1% and area under the characteristic curve of 0.866. The specific variables significantly associated with crash risk were: average freeway speed, average percentage of time spent speeding, driving hours, percentage of nighttime trips, percentage of freeway trips, and frequency of smoking warnings per 100 km. This study’s findings can be used to support proactive safety management for freight companies and policy formulation for insurance companies.