Predicting local road crashes using socioeconomic and land cover data

ABSTRACT

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.

KEYWORDS:

• cluster analysis
• crash count
• local road
• safety performance function

Acknowledgments

The authors would like to thank Mrs. Judy B. Raymond of the Connecticut Department of Transportation for kindly providing demographic data to support this effort. This article was peer-reviewed by the Transportation Research Board and presented at the 95th annual meeting of the Transportation Research Board, January 2016, Washington, DC. The authors would also like to thank all reviewers for providing constructive comments to help us improve the article. The contents reflect the views of the authors who are responsible for the accuracy of the information presented herein. The contents do not necessarily reflect the official views or policies of the University of Connecticut or the Connecticut Department of Transportation.

Funding

This research was sponsored by the Joint Highway Research Advisory Council of the University of Connecticut and the Connecticut Department of Transportation through Project 14-1 of the Connecticut Cooperative Transportation Research Program.