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:
  Accident Risk Forecasting Calibration Highway Curves Highway Safety Regression Analysis Rural Highways Two Lane Highways
• 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.
  
  
• Format:
  PDF
• Funding:
  14-8287
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha256:8382c20c31a465ce4df8fc9fa494e5d2b1d10d711a91ae4912b399fc77c61662
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/29694/dot_29694_DS1.pdf
• File Type:
  [PDF - 1.98 MB ]