An accelerated hierarchical Bayesian crash frequency model with accommodation of spatiotemporal interactions

Although spatial and temporal correlations of crash observations have been well addressed in the literature, the interactions between them are rarely studied. This study proposes a Bayesian spatiotemporal interaction (BSTI) approach for crash frequency modeling with an integrated nested Laplace approximation (INLA) method to greatly expedite the Bayesian estimation process. Manhattan, which is the most densely populated urban area of New York City, is selected as the study area. Hexagons are used as the basic geographic units to capture crash, transportation, land use, and demo-economic data from 2013 to 2019. A series of Bayesian models with various spatiotemporal specifications are developed and compared. The BSTI model with Type II interaction, which assumes that the structured temporal random effect interacts with the unstructured spatial random effect is found to outperform the others in terms of goodness-of-fit and the ability to reduce the dependency of residuals. It is also found that the unobserved heterogeneity is mostly attributed to the spatial effects instead of temporal effects. In addition, the BSTI Type II model also yields the lowest predictive error when the last year's data are used as the test set. The proposed BSTI approach can potentially advance safety analytics by achieving high prediction accuracy and computational efficiency while maintaining its interpretability on the effects of contributing factors and the unobserved heterogeneity.