Rear-end crash risk estimation for freeway work zones using a Bayesian hierarchical random-parameters block maximum approach.

Abstract

Freeway work zones are high-risk locations due to the temporary lane closure. This study aims to estimate rear-end crash risk in these zones based on modeling traffic conflicts. A Bayesian hierarchical random-parameters block maximum (BM) approach, which can accommodate both the multilevel structure and heterogeneity, is proposed to model the extremes of rear-end conflicts. The proposed model is estimated by the traffic conflict data collected from six freeway work zones in Guangdong Province, China. The estimation results indicate that the proportion of oversized vehicles, average speed, and average acceleration have heterogeneous effects on crash risk, whereas traffic volume and the number of open lanes exhibit homogeneous effects. Substantial cross-site heterogeneity is captured by the Bayesian hierarchical framework. Furthermore, model comparison demonstrates that the proposed approach performs better than both the traditional BM approach and the Bayesian hierarchical random-effects BM approach. The findings support the proposed approach as an applicable tool for real-time rear-end crash risk estimation in freeway work zones, which can be readily incorporated into the optimization of proactive safety management strategies, such as variable speed limit and ramp metering.