Injury Severity Analysis for Large Truck-Involved Crashes: Accounting for Heterogeneity

Abstract

This study explores the crash injury severity of large truck-involved crashes, where the truck driver was identified as the at-fault driver. The paper focuses on vehicle-in-motion crashes that occurred on Florida’s state highways between 2007 and 2016. A random parameter ordered logit (RPOL) model was developed to identify random parameters and interaction effects. Results indicated that not using restraint systems, running a red light, wrong-way driving, failing to yield the right of way, tire or brake defects, and dark conditions had positive associations with higher levels of crash injury severity. The random variables—straight alignment, paved shoulders, and unpaved shoulders—showed significant random effects among the observations. For straight alignment, running red lights, following too closely, vision obstruction caused by fixed objects, and vision obstruction caused by fog were the sources of heterogeneity. Unpaved shoulders, running red lights, wrong-way driving, and the presence of parked or stopped vehicles were found as interaction effects. Results showed that accounting for heterogeneity and interaction effects significantly improved the goodness of fit of the model. This study provides more comprehensive knowledge of the influencing factors of large truck crashes by considering the role of heterogeneity and its potential sources in crash injury severity.