Traffic & Safety Statewide Model and GIS Modeling

Several steps have been taken over the past two years to advance the Utah Department of Transportation (UDOT) safety initiative. Previous research projects began the development of a hierarchical Bayesian model to analyze crashes on Utah roadways. Development of this statistical model continued in this project to analyze all state roadways by functional classification and consider additional variables including annual average daily traffic (AADT), vehicle miles traveled (VMT), speed limit, percent trucks, number of lanes, road geometry, road surface type, land use, and crash type. The model analyzes roadway segments and determines a posterior predictive distribution, or a distribution of the number of crashes that would be expected for that segment based on the number of crashes reported on other segments with the same characteristics (e.g., functional classification). The actual number of crashes for each segment is compared to the predictive distribution by calculating a percentile. A high percentile indicates more crashes than would be expected and a low percentile indicates less. The actual numbers of crashes are also compared to the mean of the predictive distribution to illustrate how many crashes above or below the estimate have occurred on that segment. In addition to the statistical model a Geographic Information System (GIS) framework was developed to facilitate the analysis. The GIS framework has the capability to format the raw data obtained from UDOT such that it can be read into the statistical model. The GIS framework also displays the numerical data output by the statistical model spatially, allowing for an easy and intuitive analysis by UDOT staff.

• Record URL:
  • https://drive.google.com/file/d/1c7eYO1sJ4wB9NbqUnv1E_poxNAoOHYuI/view?usp=sharing
• 
• Record URL:
  • https://rosap.ntl.bts.gov/view/dot/24693
• Corporate Authors:

  Brigham Young University

  Department of Civil and Environmental Engineering
  Provo, UT  United States  84602

  Utah Department of Transportation

  Research Division
  Salt Lake City, UT  United States  84114-8410
• Authors:
  • Schultz, Grant G
  • Johnson, E Scott
  • Black, Clancy W
  • Francom, Devin
  • Saito, Mitsuru
• Publication Date: 2012-7

Language

• English

Media Info

• Media Type: Web
• Edition: Final Report
• Features: Appendices; Figures; Photos; References; Tables;
• Pagination: 162p

Subject/Index Terms

• TRT Terms: Crash data; Crashes; Geographic information systems; Mathematical models; Mathematical prediction; Traffic crashes; Traffic safety
• Identifier Terms: Utah Department of Transportation
• Uncontrolled Terms: Bayesian models; Road segments
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors; I81: Accident Statistics;

Filing Info

• Accession Number: 01383586
• Record Type: Publication
• Report/Paper Numbers: UT-12.06
• Contract Numbers: 12-8471
• Files: NTL, TRIS, STATEDOT
• Created Date: Aug 22 2012 3:37PM