Exploring injury severity of bicycle-motor vehicle crashes: A two-stage approach integrating latent class analysis and random parameter logit model

Abstract Bicycle–motor vehicle (BMV) crashes have been identified as a major type of traffic accident affecting transportation safety. In order to determine the characteristics of BMV crashes in cold regions, this study presents an analysis using police-reported data from 2015 to 2017 on BMV crashes in Shenyang, China. A two-stage approach integrating latent class analysis (LCA) and the random parameter logit (RP-logit) model is proposed to identify specific crash groups and explore their contributing factors. First, LCA was used to classify data into several homogenous clusters, and then the RP-logit model was established to identify significant factors in the whole data model and the cluster-based model from LCA. The proposed two-stage approach can maximize the heterogeneity effects both among clusters and within clusters. Results show that three significant factors in the cluster-based model are obscured by the whole data model in which male cyclists are associated with a higher risk of fatality, especially in the winter. Additionally, differences exist in the exploration of factors due to the characteristics of clusters; thus, countermeasures for specific crash groups should be implemented. This research can provide references for regulators to develop targeted policies and reduce injury severity in BMV crashes in cold regions.