Using Machine Learning with Photolog Images to Identify Opportunities to Improve Highway Safety

Roadway features captured in Kentucky Transportation Cabinet (KYTC) Photolog imagery are relevant to safety, operations, design, construction, maintenance, and system management for a large portion of Kentucky’s state-maintained roadway network. However, identifying conditions of interest hinges largely on manual review. Given the immense volume of imagery and competing demands placed on staff, reliance on manual review limits the Cabinet’s ability to use Photolog imagery in a scalable, consistent, and timely manner. As a result, roadway conditions that affect safety, operations, and asset management may go unidentified or are located only after a delay. Not having efficient methods to screen Photolog imagery systematically prevents KYTC from fully leveraging this resource to make data-informed decisions about transportation system management.

Language

• English

Project

• Status: Active
• Funding: $375,000.00
• Contract Numbers:

  KYSPR 27-707

• Sponsor Organizations:

  Kentucky Transportation Cabinet

  200 Mero Street
  Frankfort, KY  United States  40622
• Performing Organizations:

  University of Kentucky, Lexington

  Kentucky Transportation Center College of Engineering, 176 Raymond Building
  Lexington, KY  United States  40506-0281
• Principal Investigators:

  Ashurst, Kean

  Kirk, Adam

  Graves, Clark

  Ross, Paul

• Start Date: 20260701
• Expected Completion Date: 20290630
• Actual Completion Date: 0

Subject/Index Terms

• TRT Terms: Image analysis; Machine learning; Photologging
• Identifier Terms: Kentucky Transportation Cabinet
• Geographic Terms: Kentucky
• Subject Areas: Data and Information Technology; Highways; Maintenance and Preservation;

Filing Info

• Accession Number: 02000586
• Record Type: Research project
• Source Agency: University of Kentucky, Lexington
• Contract Numbers: KYSPR 27-707
• Files: RIP, STATEDOT
• Created Date: Aug 26 2026 5:04PM