Predicting Local Road Crashes Using Socio-economic and Land Cover Data

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 paper describes estimation of SPFs for local road intersections and segments in Connecticut using socio-economic 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. One aggregate SPF using the entire dataset was also estimated to compare with the individual cluster SPFs. The number of intersections and the total local roadway length were also used as exposure in the intersection and segment SPFs, respectively. Total population, retail and non-retail employment and average household income are found to be significant variables. 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.

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• Supplemental Notes:
  • This paper was sponsored by TRB committee ANB25 Standing Committee on Highway Safety Performance.
• Corporate Authors:

  Transportation Research Board

  500 Fifth Street, NW
  Washington, DC  United States  20001
• Authors:
  • Wang, Kai
  • Ivan, John N
  • Burnicki, Amy C
  • Mamun, Sha A
• Conference:
  • Transportation Research Board 95th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2016-1-10 to 2016-1-14
• Date: 2016

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: 18p
• Monograph Title: TRB 95th Annual Meeting Compendium of Papers

Subject/Index Terms

• TRT Terms: Cluster analysis; Crash risk forecasting; Estimating; Land use; Population density; Socioeconomic factors; Traffic analysis zones
• Identifier Terms: Safety Performance Functions
• Geographic Terms: Connecticut
• Subject Areas: Highways; Planning and Forecasting; Safety and Human Factors;

Filing Info

• Accession Number: 01594598
• Record Type: Publication
• Report/Paper Numbers: 16-1114
• Files: PRP, TRIS, TRB, ATRI
• Created Date: Mar 29 2016 9:34AM