A CONDITIONAL AUTOREGRESSIVE MODEL FOR SPATIAL ANALYSIS OF PEDESTRIAN CRASH COUNTS ACROSS NEIGHBORHOODS

This work examines the relationship between 3-year pedestrian crash counts across Census tracts in Austin, Texas, while controlling for land use, network, and demographic attributes, such as land use balance, residents’ access to transit, sidewalk density, lane-mile densities by roadway classes, and population and employment densities (by type). The model specification allows for both region-specific heterogeneity and spatial autocorrelation via a Poisson-based conditional auto-regressive (CAR) framework and is estimated using Bayesian Markov chain Monte Carlo method. Least-squares regression estimates of walk-miles traveled per zone serve as the exposure measure. Model results suggest that higher shares of residences near transit stops are associated with greater pedestrian crash risks, ceteris paribus, presumably since such access encourages more walking activity and more potential conflict between pedestrian and vehicles movements. Sidewalk provision is associated with lower pedestrian crash rates, presumably due to lower speeds and narrower roadways in network-dense and sidewalk-prominent settings, though exposure is likely higher.