Adverse weather has a major safety impact on travelers on highways. Weather events and their impact on highways can be viewed as predictable, non-recurring incidents that display strong geographic patterns. This study attempts to address the Wisconsin counties with high crash relative risk (RR)under various inclement_weather conditions such as snow, rain and fog. Within a Bayesian hierarchical modeling framework, a Poisson model with a loglink function including a spatial random effect is proposed. In particular, two types of spatial models are considered. One is a conditional autoregressive (CAR) model that specifies spatial_dependence via autoregressionamong neighboring counties. The other is an exponential model that assumes an exponential decline of spatial dependence as the distance between twocounties increases. A spatially independent model is also considered as abaseline model. Bayesian statistical inference results show fairly consistentcrash patterns with weather impact. Higher-than expected_snow-relatedcrashes occurred in the northern Wisconsin counties where more snowfall was experienced throughout the long winter. Rain-related crashes clustered in the areas close to Lake Michigan with more rainfall than other parts ofthe state. The counties in the southwestern region have overrepresented fog-related crashes partially because of more foggy days in the mountainousvalleyterrain. Our modeling_approach can be recommended to rank the counties for road weather safety planning and programming. (Author/publisher).
Abstract