Evaluating the Spatial Impacts of Environmental Factors on the Frequency of Urban Crashes using the Spatial Bayes Method based on Euclidean Distance and Contiguity

The occurrence of urban accidents depends on many human and environmental factors, so identifying the important factors influencing accidents and their spatial effects on each other is of great importance. The main goal of this study is to evaluate the spatial effects of environmental factors on the frequency of accidents in the city of Shiraz, Iran at the TAZ level. In the first step of the study, using component analysis models, important environmental factors affecting the accident were identified and composite indicators were produced as independent variables. In the second step, in order to control the effect of correlation and heterogeneity of model variables, spatial statistical models based on Euclidean distance such as geographically weighted Poisson regression (GWPR), geographically weighted negative binomial distribution (GWNBR) as well as Poisson and distribution models Negative binomial based on neighbor distance is used in spatial Bayes method with INLA approach. The results of the study showed that models based on distance and contiguity in order to evaluate the spatial effects of accident data and the factors affecting it at the TAZ level have higher accuracy than geographic weighted regression models, as well as indicators of land use diversity and access to The public transportation system is effective and in TAZs where this index is high, there is a higher probability of an accident.