Validating the Bayesian hierarchical extreme value model for traffic conflict-based crash estimation on freeway segments with site-level factors

Abstract

This study validates the Bayesian hierarchical extreme value model that is developed for estimating crashes from traffic conflicts. The model consists of a generalized extreme value distribution that characterizes the behavior of block maxima extremes and a Bayesian hierarchical structure that incorporates the non-stationarity and unobserved heterogeneity into the extreme analysis. In addition to the block-level factors, the site-level factors are also included in the model development for the first time. The model was applied to data of lane change conflicts collected from 11 basic freeway segments in Guangdong Province, China. Block-level factors such as traffic volume per 10 min, number of lane change events per 10 min, and proportion of oversized vehicles per 10 min and site-level factors such as segment length, curvature, and grade were considered. Two types of Bayesian hierarchical extreme value models were developed, including models without site-level factors and models with site-level factors. These models were also compared to at-site models that were developed for 11 segments separately. The results show that Bayesian hierarchical extreme value models significantly outperform the at-site models in terms of crash estimation accuracy and precision. As well, including site-level factors further improves the model performance in terms of goodness-of-fit. This demonstrates the validity of the Bayesian hierarchical extreme value model. The results also show that number of lane change events, segment length, and grade are significant factors which have adverse effect on the safety of lane changes on freeway segments.