Bayesian spatial-temporal model for the main and interaction effects of roadway and weather characteristics on freeway crash incidence.

This study attempts to examine the main and interaction effects of roadway and weather conditions on crash incidence, using the comprehensive crash, traffic and weather data from the Kaiyang Freeway in China in 2014. The dependent variable is monthly crash count on a roadway segment (with homogeneous horizontal and vertical profiles). A Bayesian spatio-temporal model is proposed to measure the association between crash frequency and possible risk factors including traffic composition, presence of curve and slope, weather conditions, and their interactions. The proposed model can also accommodate the unstructured random effect, and spatio-temporal correlation and interactions. Results of parameter estimation indicate that the interactions between wind speed and slope, between precipitation and curve, and between visibility and slope are significantly correlated to the increase in the freeway crash risk, while the interaction between precipitation and slope is significantly correlated to the reduction in the freeway crash risk, respectively. These findings are indicative of the design and implementation of real-time traffic management and control measures, e.g. variable message sign, that could mitigate the crash risk under the adverse weather conditions.