Causal analysis of injury severity of two-vehicle collision crashes: Insight from a correlated random parameters...

Two-vehicle collision crashes are always tough challenges for traffic management departments due to its severe consequences. This study aims to investigate risk mechanism of three types of two-vehicle collisions (i.e., head on, sideswipe, and rear end) in the same city from a comprehensive perspective.

A random parameters binary logit framework was employed to capture the unobserved heterogeneity across individual observations and reveal potential correlations between risk indicators of injury severity.

The results indicate that the correlated random parameters model performs best, and the impacts of risk indicators involving unsafe driving behavior and driver, vehicle, roadway, environment, and temporal characteristics on two-vehicle collisions are quite different. Based on the determinants of each two-vehicle collision, some recommendations have been proposed to improve the level of road traffic safety.

Fatal collisions are more likely to happen when driving on the roads with higher road function classification and involving the presence of heavy trucks. Road type, weather condition, and unsafe driving behavior are primary contributors to two-vehicle collisions. Besides, fatal head-on collisions are prone to occurring when exceeding speed limits and driving on rainy days. Illegal overtaking or lane changing significantly increases the risk of fatal injuries in both sideswipe and rear-end collisions. Moreover, significant correlations between the on-ramp of highways and violating traffic lights or signs, and between cloudy days and visibility range within 50-100 m, are identified in the injury severity models of sideswipe and rear-end collisions, respectively. Current findings suggest that more attention should be devoted to intervening unsafe driving behaviors for sideswipe collisions, as well as enhancing the provision of visibility range information to mitigate rear-end collisions in adverse weather conditions.