A hierarchical Bayesian spatiotemporal random parameters approach for alcohol/drug impaired-driving crash frequency analysis

In this study, hierarchical Bayesian random parameters models with various spatiotemporal interactions are developed to address this issue. Selected for analysis are the yearly county-level alcohol/drug impaired-driving related crash counts data of three different injury severities including minor injury, major injury, and fatal injury in Idaho from 2010 to 2015. The variables, including daily vehicle miles traveled (DVMT), the proportion of male (MALE), unemployment rate (UR), and the percentage of drivers of 25 years and older with a bachelor's degree or higher (BD), are found to have significant impacts on crash frequency and be normally distributed in certain crash severities. Significant temporal and spatial heterogenous effects are also detected in all three crash severities. These empirical results support the incorporation of temporal and spatial heterogeneity in random parameters models.
Source: Analytic Methods in Accident Research - Category: Accident Prevention Source Type: research