Road accident outcomes are traditionally analyzed at state or road network level due to a lack of aggregated data and suitable analytical methods. The aim of this paper is to demonstrate usefulness of a simple spatiotemporal modeling of road accident outcomes at small-scale geographical level. Small-area spatiotemporal Bayesian models commonly used in epidemiological studies reveal the existence of spatial correlation in accident data and provide a mechanism to quantify its effect. The models were run for Belgium data for the period 2000-2005. Two different scale levels and two different exposure variables were considered under Bayesian hierarchical models of annual accident and fatal injury counts. The use of the conditional autoregressive (CAR) formulation of area specific relative risk and trend terms leads to more distinctive patterns of risk and its evolution. The Pearson correlation tests for relative risk rates and temporal trends allows researchers to determine the development of risk disparities in time. Analysis of spatial effects allowed the identification of clusters with similar risk outcomes pointing toward spatial structure in road accident outcomes and their background mechanisms. From the analysis of temporal trends, different developments in road accident and fatality rates in the three federated regions of Belgium came into light. Increasing spatial disparities in terms of fatal injury risk and decreasing spatial disparities in terms of accident risk with time were further identified. The application of a space-time model to accident and fatal injury counts at a small-scale level in Belgium allowed identification of several areas with outstandingly high accident (injury) records. This could allow more efficient redistribution of resources and more efficient road safety management in Belgium. (A) Reprinted with permission from Elsevier.
Abstract