The influence of zonal configurations on macro-level crash modeling

Abstract

This study investigated the impacts of zonal configurations on macro-level traffic safety analysis for crashes of different severity levels. Bayesian multivariate Poisson-lognormal models with multivariate conditional auto-regressive priors were developed to account for the spatial autocorrelation between adjacent geographical units and correlations among crash types of four ordinal severity levels, i.e. fatality, severe injury, slight injury and no injury. For the purpose of evaluating the effects of zonal configurations on macro-level traffic safety analysis, the proposed model was calibrated using crash data of four types of geographical units, i.e. block group, traffic analysis zone, census tract and zip code tabulation area, in Hillsborough County of Florida. The study empirically revealed the extensive presence and the significance of MAUP in macro-level safety analysis based on the existing zonal configurations. It gave out a warning and encouraged more research efforts on rational application of macroscopic safety analysis with different zonal configurations.