Transportation safety data and analysis : Volume 2, Calibration of the highway safety manual and development of new safety performance functions.

Details

• Alternative Title:
  Transportation safety data and analysis Volume 2 ; Calibration of the highway safety manual and development of new safety performance functions
• Creators:
  Saito, Mitsuru ; Brimley, Bradford K. ; Schultz, Grant G.
• Corporate Creators:
  Brigham Young University. Department of Civil and Environmental Engineering
• Corporate Contributors:
  Utah. Division of Traffic & Safety ; Utah. Dept. of Transportation. Research Division
• Subject/TRT Terms:
  • Highway safety
  • Medians
  • Manuals
  • Binomial distributions
  • Bayes' theorem
  • Empirical methods
  • Crashes
  • Evaluation
  • Roads--Utah--Safety measures
  • Median strips--Evaluation
  More +
• Publication/ Report Number:
  UT-10.12b
• Resource Type:
  Tech Report
• Geographical Coverage:
  Utah
• Edition:
  Sept. 2009-Feb. 2011.
• Corporate Publisher:
  Utah. Dept. of Transportation. Research Division
• Abstract:
  This report documents the calibration of the Highway Safety Manual (HSM) safety performance function (SPF) ; for rural two-lane two-way roadway segments in Utah and the development of new models using negative ; binomial and hierarchical Bayesian modeling techniques. Crash data from 2005-2007 on 157 selected study ; segments in Utah provided a 3-year observed crash frequency to obtain a calibration factor for the HSM SPF ; and develop new models. The calibration factor for the HSM SPF for rural two-lane two-way roads in Utah is ; 1.16, indicating that the HSM underpredicts the number of crashes on these roads by 16 percent. ; Negative binomial regression was used to develop four new models, and one additional model was ; developed using hierarchical (or full) Bayesian techniques. The empirical Bayes (EB) method can be applied ; with each negative binomial model because the models include an overdispersion parameter used with the EB ; method. The hierarchical Bayesian technique accounts for high levels of uncertainty. Because the hierarchical ; Bayesian model produces a density function of a predicted crash frequency, a comparison of this density ; function with an observed crash frequency can help identify segments with significant safety concerns. ; Each model has its own strengths and weaknesses, which include its data requirements and predicting ; capability. This report recommends that UDOT use the negative binomial model with transformed average ; annual daily traffic (AADT) at a 95 percent confidence level (Equation 5-11) for predicting crashes. This model ; produces accurate results and requires less data than other models. The hierarchical Bayesian process should be ; used for identifying segments with extreme crash frequencies that may benefit from safety improvements.
  More +
• Format:
  PDF
• Funding:
  10-9103
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/20470/dot_20470_DS1.pdf
• File Type:
  [PDF - 1.68 MB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:854b64a7d2ec0f70860fbaf5ad8dc8b9bd86f84c45fed810d28e0a1a7aa47fb47679d4ce9e1609927d662463c8eb43fc3ca4be1b325a5d33474eaece97333478