Modeling accident risk at the road level through zero-inflated negative binomial models: A case study of multiple road networks

Abstract

This paper presents a case study carried out in multiple cities of the Valencian Community (Spain) to determine the effect of sociodemographic and road characteristics on traffic accident risk. The analyzes are performed at the road segment level, considering the linear network representing the road structure of each city as a spatial lattice. The number of accidents observed in each road segment from 2010 to 2019 is taken as the response variable, and a zero-inflated modeling approach is considered. Count overdispersion and spatial dependence are also accounted for. Despite the complexity and sparsity of the data, the fitted models performed considerably well, with few exceptions. The results show that the effect of most of the covariates is highly heterogeneous across cities. In particular, the impact of roundabouts and main roads on accident risk is overall consistent across the cities studied, whereas the role of sociodemography and other road features seems much more uncertain. Furthermore, the distribution of zeros and non-zeros displays a markedly spatial structure, whereas the magnitude of non-zeros is generally unstructured in space. These findings could be useful to establish preventive measures at the street level in the future.