University of California Transportation Center

Using two years of crash and average annual daily traffic data we examine the locations and conditions linked to truck-involved crashes (accidents). A binomial logit model is used to describe how the probability that a crash involves a truck is a function of the percentage of annual average daily traffic that is accounted for by trucks, time of day, day of the week, weather conditions, mix of truck types, and the absolute level of average annual daily traffic. That model can then be used to identify locations with higher or lower than expected truck involved accident rates, controlling for all of the factors that influence truck crash rates. A multinomial logit model was then estimated in order to better understand patterns of truck-involved crashes by separating crashes by type, with the main types being rear-end, lane-change, and run-off collisions. We propose that results from applications of these kinds of models, applied in a specific region, can be useful to public agencies seeking to identify and remedy problem areas either with better driver education or investments in physical or intelligent transportation system infrastructure.