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

摘要: 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 use balance, residents’ access to transit, sidewalk density, lane-mile densities by roadway classes, population employment (by type). The model specification allows both region-specific heterogeneity spatial autocorrelation via a Poisson-based conditional auto-regressive (CAR) framework is estimated using Bayesian Markov chain Monte Carlo method. Least-squares regression estimates of walk-miles traveled per zone serve exposure measure. Model results suggest that higher shares residences near transit stops are associated with greater risks, ceteris paribus, presumably since encourages more walking activity potential conflict vehicles movements. Sidewalk provision lower rates, due speeds narrower roadways network-dense sidewalk-prominent settings, though likely higher.