Machine Learning for Recognizing Driving Patterns of Drivers of Large Commercial Trucks

Abstract

Commercial large truck crashes are more likely to involve fatalities and significant costs than passenger vehicle crashes are. To reduce fatigue-related crashes of large trucks caused by drivers' irregular work schedules, FMCSA has enforced hours-of-service rules to regulate the activities of drivers of commercial large trucks. The complex influence of drivers' multiday driving activity patterns on crash risk was examined with data collected from two national truckload carriers. A machine learning approach, k-means clustering, was used to classify large truck drivers into 10 clusters according to their 15-min driving activities over multiple days. Then, the crash risk and driving activity pattern were identified for each cluster. Discrete-time logistic regression models were used to quantify the relationships between driving activity patterns and crash risk. Results indicated that the driving pattern with the lowest crash risk could be daytime driving between 4:00 a.m. and noon, with rest breaks in the late afternoon (4:00 to 6:00 p.m.). Drivers with high proportions of afternoon on-duty time after a long off-duty period experienced significantly higher crash risk. A representative day concept is proposed as a complementary method to identify relationships between driving patterns and crash risk. Moreover, on-duty hours can be a useful indicator of crash risk for drivers of large trucks. High proportions of on-duty hours in the early morning and late afternoon often are associated with high crash risk.