Investigating vehicle-vehicle and vehicle–pedestrian crash severity at street intersections with the latent class parameterized correlation bivariate generalized ordered probit

Abstract

Street intersection crashes often involve two parties: either two vehicles hitting each other (i.e., a vehicle-vehicle crash) or a vehicle colliding with a pedestrian (i.e., a vehicle–pedestrian crash). In such crashes, the severity of injuries can vary considerably between the parties involved. It is necessary to understand the injuries of both parties simultaneously to identify the causality of a vehicle–pedestrian or two-vehicle crash. While the latent class ordinal model has been used in crash severity studies to capture heterogeneity in crash propensity, most of these studies are univariate, which is inappropriate for crashes involving two parties. This study proposes a latent class parameterized correlation bivariate generalized ordered probit (LCp-BGOP) model to examine 32,308 vehicle-vehicle and vehicle–pedestrian crashes at intersections in Taipei City, Taiwan. The model parameterizes thresholds and within-crash correlations of crash severity involving two parties and classifies these crashes into two distinct risk groups: the “Ordinary Crash Severity” (OCS) group and the “High Crash Severity” (HCS) group. The OCS group is mainly two-vehicle crashes involving motorcycles. The HCS group comprises vulnerable road users such as pedestrians and cyclists, mainly in mixed traffic with heavy volumes. The results also show that the effects of party-specific factors contributing to injury severity are greater than those of generic factors. Our study provides invaluable insight into intersection crashes, helping to reduce the severity of injuries in vehicle-vehicle and vehicle–pedestrian crashes.