Predicting local road crashes using socioeconomic and land cover data

• Published in: Journal of transportation safety & security 2017-07, Vol.9 (3), p.301-318
• Main Authors: Wang, Kai, Ivan, John N., Burnicki, Amy C., Mamun, Sha A.
• Format: Article
• Language: English
• Subjects: Cluster analysis; Counting; crash count; Crashes; Exposure; Intersections; local road; Mathematical models; Predictions; Roads; Roads & highways; safety performance function; Segments
• ISSN: 1943-9962; 1943-9970
• DOI: 10.1080/19439962.2016.1206048

Estimating and applying safety performance functions (SPFs), or models for predicting expected crash counts, for roads under local jurisdiction is often challenging due to the lack of vehicle count data to be used for exposure, which is a critical variable in such functions. This article describes estimation of SPFs for local road intersections and segments in Connecticut using socioeconomic and network topological data instead of traffic counts as exposure. SPFs are developed at the traffic analysis zone (TAZ) level, where the TAZs are categorized into six homogeneous clusters based on land-cover intensities and population density. SPFs were estimated for each cluster to predict the number of intersection and segment crashes occurring in each TAZ. The number of intersections and the total local roadway length were also used as exposure in the intersection and segment SPFs, respectively. One aggregate SPF using the entire data set was also estimated to compare with the individual cluster SPFs. Ten percent of the observed data points were reserved for out-of-sample testing, and in all cases these out-of-sample predictions were as good as the in-sample predictions. Models including total population, retail and nonretail employment, and average household income are found to be the best on the basis of model fit and out-of-sample prediction.