The fatal rate of truck-involved crashes is increasing and crashes become more severe than passenger vehicles in recent years. Much research has been dedicated to exploring the truck crash factors while scarce research focused on the intersection scenarios. This study investigates the factors that affect the severity level of truck-involved crashes at cross- and T-intersections. Due to the unobserved heterogeneity inherent in crash data, latent class analysis is firstly conducted to divide the crash dataset into relatively homogeneous clusters. Considering the ordinal feature of the severities, general ordered logit models are subsequently developed to further explore the specific factors within each cluster.
This study uses the North Carolina’s truck-involved crash at intersection data during 2005 to 2017 from the Highway Safety Information System (HSIS). The estimated parameters and associated marginal effects are combined to interpret the impact of the significant variables within specific clusters. Many factors are found to contribute to the severities, and T-intersection is found to be safer than cross-intersection. For driving behaviors, followed too closely, disregarded signs, disregarded signals, failed to yield, and exceeded speed are found to be top five factors that increase the crash severity at intersections. These results indicate that distraction and speed limits violation always result in severe injury for humans involved in the truck crashes at the intersections. The results of this research provide more reliable analysis for the impact factors of truck-involved crashes at intersections to engineering practitioners and researchers.
Song, L. & Fan, W.D. (2021). Exploring truck driver-injury severity at intersections considering heterogeneity in latent classes: A case study of North Carolina. International Journal of Transportation Science and Technology 10(2), 110-120.