Incorporating Road Network Connectivity in Neighboring Structures for Crash Prediction Models at the Area Level

Abstract

Spatial correlation models have been traditionally used in road safety to account for spatial effects resulting from unmeasured or unknown risk factors that induce spatial correlation between neighboring areas. In transportation, the interaction between neighboring areas is highly influenced by the number of roads that connect those areas and the importance of those roads. This paper proposes an approach in which the weights of the spatial interaction (and therefore the spatial correlation) between areas depends on the number of road connections between those areas and the importance of those connections. The results using districts in Costa Rica show that the inclusion of road network connectivity in the models of spatial correlation significantly improves model fit, even after accounting for model complexity using the deviance information criterion (DIC) and widely applicable information criterion (WAIC). The inclusion of higher weights for national roads compared to municipal or local roads further improved the model fit. The best three models with respect to the posterior deviance, DIC, and WAIC are those that give at least three times more weight to national roads compared to local roads. With respect to site ranking, those three models present similar results, which also highlights the consistency among those models.