title: Modeling occupant-level injury severity: An application to large-truck crashes.
abstract: Most of the injury-severity analyses to date have focused primarily on modeling the most-severe injury of any crash, although a substantial fraction of crashes involve multiple vehicles and multiple persons. In this study, we present an extensive exploratory analysis that highlights that the highest injury severity is not necessarily the comprehensive indicator of the overall severity of any crash. Subsequently, we present a panel, hetroskedastic ordered-probit model to simultaneously analyze the injury severities of all persons involved in a crash. The models are estimated in the context of large-truck crashes. The results indicate strong effects of person-, driver-, vehicle-, and crash-characteristics on the injury severities of persons involved in large-truck crashes. For example, several driver behavior characteristics (such as use of illegal drugs, DUI, and inattention) were found to be statistically significant predictors of injury severity. The availability of airbags and the use of seat-belts are also found to be associated with less-severe injuries to car-drivers and car-passengers in the event of crashes with large trucks. Car drivers' familiarity with the vehicle and the roadway are also important for both the car drivers and passengers. Finally, the models also indicate the strong presence of intra-vehicle correlations (effect of common vehicle-specific unobserved factors) among the injury propensities of all persons within a vehicle.
authors.affiliation: Dept. of Civil and Coastal Engineering, University of Florida, 512 Weil Hall, PO Box 116580, Gainesville, FL 32611, USA. shuxy03@ufl.edu
authors.forename: Xiaoyu
authors.initials: X
authors.lastname: Zhu
authors.forename: Sivaramakrishnan
authors.initials: S
authors.lastname: Srinivasan
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D006801:Humans; D015601:Injury Severity Score; D016013:Likelihood Functions; D015233:Models, Statistical; D018986:Motor Vehicles