Developing Safety Performance Functions for Kansas Safety Studies

Safety money spent for any transportation/traffic improvement should be cost-effective. There should be some evidence that the improvement will (or has been) be a positive benefit and improve traffic safety, i.e., reduce crashes, injuries, and/or deaths. To show the dollars have been well spent and safety has been improved, documentation of the results and proper analysis of the changed safety is essential. Unfortunately, good data for documentation of positive (or negative) "after" safety benefits has been generally lacking. In addition, nationally recognized experts such as Ezra Hauer and others have been telling the highway safety community for years that the usual before-after analysis has not been properly done. This usually has involved taking the number of crashes after some safety improvement has been made, subtracting this number from the number of crashes before the improvement was constructed and assuming the after is less than the before, claiming the sum is the safety benefit- or dividing this sum by the before number and claiming a percent reduction in crashes as the benefit. This approach is indefensible as it is naive to believe that the result of this approach can only be attributed to the safety improvement, i.e., nothing else has changed, and it is sometimes called a naive before and after study. It is easy but indefensible and open to criticism. It is essential to be able to make a reliable, defensible analysis. This is especially important in evaluating any safety improvement, e.g. such as roundabouts of various AADT, legs and lanes. The proper state-of-the art-approach, called the Empirical Bayes (EB) method involves calculating the expected number of crashes that would have occurred in the after period without the safety improvement (call it "B") and subtracting the number of reported crashes in the after period. The expected number of crashes, B, requires using a Safety Performance Function (SPF) to estimate a number of predicted crashes "P" that is combined in a weighted function with the actual count of the number of crashes observed in "N" years before improvement to get a site-specific estimate of the expected crashes, i.e. the EB estimate. The "problem" is very few transportation organizations, including KDOT, have SPFs. For proper state-of-the-art, Empirical Bayes (EB), before-after analysis of any safety improvement, these need to be developed locally, i.e., with Kansas data for specific safety improvement functions, and calibrated for local, Kansas conditions.

• Record URL:
  http://transport.ksu.edu/files/transport/imported/Reports/2009/KSUTC-09-4.pdf
• 

Language

• English

Project

• Status: Active
• Funding: $40000.00
• Contract Numbers:

  RE-0430-05; HPD-R043

  KSUTC-09-4

• Sponsor Organizations:

  Kansas State University Transportation Center

  Kansas State University
  Department of Civil Engineering
  Manhattan, KS  United States  66506
• Project Managers:

  Stokes, Robert

• Performing Organizations:

  Kansas State University Transportation Center

  Kansas State University
  Department of Civil Engineering
  Manhattan, KS  United States  66506
• Principal Investigators:

  Dissanayake, Sunanda

• Start Date: 20080701
• Expected Completion Date: 0
• Actual Completion Date: 20110630
• Source Data: RiP Project 20998

Subject/Index Terms

• TRT Terms: Annual average daily traffic; Crash avoidance systems; Crash data; Crash injuries; Fatalities; Research projects; Roundabouts; Traffic safety
• Uncontrolled Terms: Safety improvement projects
• Geographic Terms: Kansas
• Subject Areas: Highways; Operations and Traffic Management; Safety and Human Factors;

Filing Info

• Accession Number: 01462207
• Record Type: Research project
• Source Agency: Kansas State University Transportation Center
• Contract Numbers: RE-0430-05; HPD-R043, KSUTC-09-4
• Files: RIP
• Created Date: Jan 3 2013 2:00PM