Applying spatial analysis to detect traffic crash patterns in a rural county and statistical analysis to associate contributing factors

The capacity to identify hazardous roadway segments quantitatively can guide remedial engineering efforts and make smarter use of limited government resources. This research evaluates spatial analytic tools, employing the local network Moran's I indicator, to statistically identify hazardous roadway segments. Hazardous segments are defined as those with crashes occurring in greater proportion to traffic volume and in relation to adjacent roadway segments proportions of crashes.;In a traditional non-uniform analysis high incident segments correlate with traffic volume masking. Transformation of the roadway network into a uniform network reveals more high incident segments. The veracity of the findings were tested using the Chi-square test, Spearman's Rho and binary logistic regression.;The analysis identified 27 hazardous segments, four were statistically analyzed. Hazardous segments displayed significant explanatory variables and statistical dependencies. Scene visitations revealed evidence of contributing factors. This methodology can be employed to identify crash clustering and understand causal factors.