Crash prediction modeling for curved segments of rural two-lane two-way highways in Utah.

Details

• Creators:
  Saito, Mitsuru ; Knecht, Casey S. ; Schultz, Grant G. ; Cook, Aaron A.
• Corporate Creators:
  Brigham Young University. Dept. of Civil and Environmental Engineering
• Corporate Contributors:
  United States. Federal Highway Administration
• Subject/TRT Terms:
  • Rural highways
  • Two lane highways
  • Highway curves
  • Accident risk forecasting
  • Regression analysis
  • Calibration
  • Highway safety
• Publication/ Report Number:
  UT-15.12
• Resource Type:
  Tech Report
• Geographical Coverage:
  Utah
• Corporate Publisher:
  Utah. Dept. of Transportation
• Abstract:
  This report contains the results of the development of crash prediction models for curved segments of rural ; two-lane two-way highways in the state of Utah. The modeling effort included the calibration of the predictive ; model found in the Highway Safety Manual (HSM) as well as the development of Utah-specific models using ; negative binomial regression. The data for these models came from 1,495 randomly sampled curved segments in ; Utah, with crash data coming from years 2008-2012. For this research, two sample periods were used: a three-year ; period from 2010 to 2012 and a five-year period from 2008 to 2012. The calibration factor for the HSM predictive ; model was determined to be 1.50 for the three-year period and 1.60 for the five-year period. A negative binomial ; model was used to develop Utah-specific crash prediction models based on both the three-year and five-year ; sample periods. The independent variables used for negative binomial regression included the same set of ; variables used in the HSM predictive model along with other variables such as speed limit and truck traffic that ; were considered to have a significant effect on potential crash occurrence. The significant variables were found to ; be average annual daily traffic, segment length, total truck percentage, and curve radius. The main benefit of the ; Utah-specific crash prediction models is that they provide a reasonable level of accuracy for crash prediction yet ; only require four variables, thus requiring much less effort in data collection compared to using the HSM ; predictive model.
  More +
• Format:
  PDF
• Funding:
  14-8287
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/29694/dot_29694_DS1.pdf
• File Type:
  [PDF - 1.98 MB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha256:8382c20c31a465ce4df8fc9fa494e5d2b1d10d711a91ae4912b399fc77c61662