Modeling Multiple Vehicle Occupant Injury Severity: a Copula-based Multivariate Approach

Previous research in crash injury severity analysis has largely focused on level of injury severity sustained by the driver of the vehicle or the most severely injured occupant of the vehicle. While such studies are undoubtedly useful, they do not provide a comprehensive picture of the injury profile of all vehicular occupants in crash-involved vehicles. This limits the ability to devise safety measures that enhance the safety and reduce the injury severity associated with all vehicular occupants. Moreover, such studies ignore the possible presence of correlated unobserved factors that may simultaneously influence and impact the injury severity levels of multiple occupants in the vehicle. This paper aims to fill this gap by presenting a simultaneous model of injury severity that can be applied to crashes involving any number of occupants. A copula-based methodology, that can be effectively used to estimate such complex model systems, is presented and applied to a data set of crashes drawn from the 2007 General Estimates System (GES) in the United States. The model estimation results provide strong evidence of the presence of correlated unobserved factors that affect injury severity levels among vehicle occupants. The correlation exhibits heterogeneity across vehicle types with greater level of interoccupant dependency in heavier sport utility vehicles and pickup trucks. The study also sheds light on how numerous exogenous factors including occupant characteristics, vehicle characteristics, environmental factors, roadway attributes, and crash characteristics affect injury severity levels of occupants in different seat positions. The findings confirm that rear seat passengers are less vulnerable to severe injuries than front row passengers pointing to the need to enhance vehicular design features that promote front row occupant safety.