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Despite the evident spatial character of road crashes, limited research has been conducted in road safety analysis to account for spatial correlation; further, the practical consequences of this omission are largely unknown. The purpose of this research is to explore the effect of spatial correlation in models of road crash frequency at the segment level. Different segment neighboring structures are tested to establish the most appropriate one in the context of modeling crash frequency in road networks. A full Bayes hierarchical approach is used with conditional autoregressive effects for the spatial correlation terms. Analysis of crash, traffic, and roadway inventory data from a rural county in Pennsylvania indicates the importance of including spatial correlation in road crash models. The models with spatial correlation show significantly better fit to the data than the Poisson lognormal model with only heterogeneity. Parameters significantly different from zero included annual average daily traffic (AADT) and shoulder widths less than 4 ft and between 6 and 10 ft. In four models with spatial correlation, goodness of fit was improved compared with the model including only heterogeneity. More important yet is the potential of spatial correlation to reduce the bias associated with model misspecification, as shown by the change in the estimate of the AADT coefficient and other parameters.
Agüero-Valverde et al. (Tue,) studied this question.
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