Investigating varying effect of road-level factors on crash frequency across regions: A Bayesian hierarchical random parameter modeling approach

Abstract

This study aims to quantitatively examine the variations in effect of road-level factors on crash frequency across different regions. Treating the hierarchical structure existing in the crash data that road entity nested within the geographic region, a hierarchical random parameter model, which allows the coefficients of road-level variables to vary with regions, is proposed. A Poisson lognormal model and a hierarchical random intercept model are also built for the purpose of comparison. A specific roadway facility type, urban two-lane two-way roadway segments in Florida, with crash and road level data including traffic volume, road length, surface condition, and access density for three-year period are used for a case study. The result shows that, in the hierarchical-random parameter model, the local regression coefficients and marginal effects of the road level factors vary over a wide range in the selected counties, which clearly illustrates the non-stationary in the relationships between road level factors and crash frequency across the counties. In regard to the model comparison, the hierarchical random parameter model outperforms the Poisson lognormal model and the hierarchical random intercept model in term of deviance information criterion (DIC). This further confirms the necessity of the use of hierarchical random parameter model in analyzing the crash frequency for road entities in different regions. This study provides a potential in guidance of model construction that considers regional variations (heterogeneities) in safety effects of road-level factors.