Incorporating risky driving behaviour in the injury severity analysis of at-fault and not-at-fault novice driver involved crashes: a correlated random parameters logit model with heterogeneity in means

Abstract

In order to gain a more detailed understanding of novice driver involved crashes, this paper compares the injury severity of at-fault and not-at-fault novice driver involved crashes with the consideration of risky driving behaviours. The crash data of novice drivers in Shenyang, China, from 2012 to 2017 is used. The correlated random parameters logit model with heterogeneity in means is built for the whole dataset, at-fault dataset, and not-at-fault dataset to explore the significant factors determining the injury severity, and obtain insight into hidden and common factors. The hidden factors indicate factors that are only significant in one dataset, while the common factors indicate factors that are significant in two or more datasets. The results show that nine common factors significantly affect injury severity of novice driver involved crashes. Among them, speeding, legal but unsafe driving behaviours, and improper operation are related to risky driving behaviour. Moreover, road type, functional zone, time interval, and conflict and improper passing are hidden factors, among which conflict and improper passing is related to risky driving behaviour. Finally, some valuable insights for regulatory authorities in formulating targeted policies are provided to mitigate the severity of novice drivers involved crashes.