Comparative analysis of cost-weighted site ranking using alternate distance-based neighboring structures for spatial crash frequency modeling

Abstract

ABSTRACTThere are significant cost differences between alternate crash outcomes based on severity levels. This study aims to expand existing literature with the inclusion of spatial correlations among a group of intersections to compare alternate distance-based weight matrices for cost-weighted hotspot identification (HSID) purpose. Multivariate-Poisson-lognormal-spatial (MVPLNS) method was employed to develop five crash prediction models (pure-distance and decay) to jointly estimate four severity levels (fatal and severe injury, other visible injury, complaint of pain, and noninjury). Model comparison for assessment of goodness-of-fit indicated that relatively subtle matrix structures assign consistent weights that reduce model complexity and eventually enhances the overall fit. The assessment of predictive accuracy of model estimates indicated that the model fit may be correlated with a superior performance at prediction as witnessed in the case of a pure-distance model that consistently exhibited least discrepancy from actual crash counts. The evaluation of HSID performance was based on rankings obtained from cost-weighted severities. The pure-distance models were overall superior at HSID, but among these models, the more subtle model performed significantly better, which hints at the presence of correlation between model fit and HSID performance as it may be possible that benefits of superior fit transfer to equivalent HSID capabilities.