Predicting Truck Crash Involvement: Developing a Commercial Driver Behavior Model and Requisite Enforcement Countermeasures

The American Transportation Research Institute (ATRI) undertook this research to develop an overall driver performance-based model for predicting future crash involvement based on prior driver history. ATRI’s research team included North Dakota State University Upper Great Plains Transportation Institute (NDSU/UGPTI) and the Commercial Vehicle Safety Alliance (CVSA). Several available subsets of driver-specific data were used by the research team to design and test the model. The model includes specific violations discovered during roadside inspections, driver traffic conviction information and past accident involvement. A secondary component of the research identifies effective enforcement actions to counteract the identified problem driving behaviors/events. The analysis shows that eight separate moving violations were significant with an associated crash likelihood increase between 21 and 325 percent. Four driver violations were associated with a crash likelihood increase between 18 and 56 percent. Twelve convictions were significant with an associated crash likelihood increase between 24 and 100 percent. Furthermore, drivers who had a past crash increase their likelihood of a future crash by 87 percent. According to the states identified as having more traffic enforcement and lower crashes, successful enforcement strategies for addressing problem driver behaviors are those that exhibit one or more of the following components: creating aggressive driving apprehension programs/initiatives; focusing on both CMV and non-CMV driver behavior patterns; conducting highly visible enforcement activities; using a performance-based approach to identifying specific crash types, driver behaviors and locations; and conducting covert enforcement activities.