Using the multivariate spatio-temporal Bayesian model to analyze traffic crashes by severity

Publication date: March 2018 Source:Analytic Methods in Accident Research, Volume 17 Author(s): Chenhui Liu, Anuj Sharma Unobserved heterogeneity across space, time, and crash type is often non-negligible in crash frequency modeling. When multiple crash types with spatial and temporal features are analyzed, multivariate spatio-temporal models should be considered. For this study, we analyzed the yearly county-level fatal, major injury, and minor injury crashes in Iowa from 2006 to 2015 using a multivariate spatio-temporal Bayesian model. The model adopted a multivariate spatial structure, a multivariate temporal structure, and a multivariate spatio-temporal interaction structure to account for possible correlations across injury severities over space, time, and spatio-temporal interaction, respectively. Income and weather indicators were found to have no significant effects on crash frequencies in the presence of vehicle miles traveled and unemployment rate. Both spatial and temporal effects were found to be important, and they played nearly the same roles for all three crash types in the studied dataset. Counties located in north and southwest Iowa were found to tend to have fewer crashes than the remaining counties. All three crash types generally showed descending trends from 2006 to 2015. They also had significantly positive correlations between each other in space but not in time. The crude crash rates and predicted crash rates were generally consistent for major inj...
Source: Analytic Methods in Accident Research - Category: Accident Prevention Source Type: research