Analysis Of Truck-Involved Rear-End Crashes Using Multinomial Logistic Regression

Rear-end collisions have the second largest crash frequency and result in the third largest number of fatalities among the types of truck-involved crashes. This paper investigates both overall and fatal truck-involved rearend collisions using two national crash databases (2000-2004), General Estimates System (GES) and Fatality Analysis Reporting System (FARS). In this study, two-vehicle rear-end collisions were classified into three groups. The categories were based on the vehicle\u27s striking/struck role: Truck-Car (truck hitting car), Car- Truck (car hitting truck), and Car-Car (car hitting car) crashes. The occurrence conditions of the three rearend crash types were compared to each other to identify potential risk factors, such as road environments, highway designs, and driver characteristics, associated with the truck-involved crashes. The multinomial logistic regression results show that factors including lighting condition, divided/undivided highway, weather condition, interstate, day of week, speed related, alcohol use, driver age, and gender are significantly associated with overall truck-involved rear-end crashes. In addition factors including lighting condition, divided/undivided highway, day of week, alcohol use, driver age, and gender are also significantly associated with fatal truck-involved rear-end collisions. This study provides a better understanding of truck-related rearend crash risks and more information regarding effective crash countermeasures