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.

Keywords:

• bicycle–motor vehicle crashes
• injury severity
• latent class analysis
• random parameter logit model

Additional information

Funding

This work was supported by Beijing Natural Science Foundation (Grant No. 9194023, L201007), MOE (Ministry of Education in China) Liberal Arts and Social Sciences Foundation (Grant No. 18YJC630158), National Key R&D Program of China (Grant No. 2017YFC0803903), National Natural Science Foundation of China (Grant No. 71801161), and Science and Technology Program of Beijing Municipal Education Commission funded project (Grant No. KM201810005035).