Revisiting crash spatial heterogeneity: A Bayesian spatially varying coefficients approach

• Published in: Accident analysis and prevention Vol. 98; pp. 330 - 337
• Main Authors: Xu, Pengpeng, Huang, Helai, Dong, Ni, Wong, S.C.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.01.2017
• Subjects: Accidents, Traffic - statistics & numerical data; Bayes Theorem; Bayesian inference; Conditional autoregressive prior; Crash frequency; Environment Design; Florida; Humans; Models, Statistical; Safety - statistics & numerical data; Spatial heterogeneity; Spatial Regression; Unobserved heterogeneity; Spatial heterogeneity; Conditional autoregressive prior; Bayesian inference; Crash frequency; Unobserved heterogeneity
• ISSN: 0001-4575; 1879-2057; 1879-2057
• DOI: 10.1016/j.aap.2016.10.015

•A BSVC model was developed to account for the crash spatial heterogeneity.•Ignoring spatially structured heterogeneity may produce biased parameter estimates.•Assuming regression slopes as spatially clustered only may induce over smoothness. This study was performed to investigate the spatially varying relationships between crash frequency and related risk factors. A Bayesian spatially varying coefficients model was elaborately introduced as a methodological alternative to simultaneously account for the unstructured and spatially structured heterogeneity of the regression coefficients in predicting crash frequencies. The proposed method was appealing in that the parameters were modeled via a conditional autoregressive prior distribution, which involved a single set of random effects and a spatial correlation parameter with extreme values corresponding to pure unstructured or pure spatially correlated random effects. A case study using a three-year crash dataset from the Hillsborough County, Florida, was conducted to illustrate the proposed model. Empirical analysis confirmed the presence of both unstructured and spatially correlated variations in the effects of contributory factors on severe crash occurrences. The findings also suggested that ignoring spatially structured heterogeneity may result in biased parameter estimates and incorrect inferences, while assuming the regression coefficients to be spatially clustered only is probably subject to the issue of over-smoothness.