Leveraging Crowd-Sourced Data and Artificial Intelligence for Timely Detection of Roadway Anomalies

Roadway debris like carcasses and tire fragments, can pose a hazard to traffic. It can be struck by vehicles and result in a crash or secondary crash. Advance roadway debris detection is critical for traffic operations and safety. Transportation Management Centers (TMCs) can detect debris in urban areas, but a recent study found that Waze, reported three times as many road hazards to similar TMC events on freeways. Waze detected 72% of road hazards first and nearly sixteen (16) minutes earlier. Harnessing crowd-sourced data from navigational apps or connected vehicle dashcams has become a reality with Artificial Intelligence (AI)-enabled processing. This action opens opportunities for enhanced situational awareness and expedite the appropriate responses from the Texas Department of Transportation (TxDOT). The research team will leverage crowd-sourced third-party data and AI-enabled processing to capitalize on this opportunity.

• Record URL:
  • https://library.ctr.utexas.edu/Presto/project=0-7266

Language

• English

Project

• Status: Active
• Funding: $424,925.00
• Contract Numbers:

  0-7266

• Sponsor Organizations:

  Texas Department of Transportation

  125 E. 11th Street
  Austin, TX  United States  78701-2483
• Managing Organizations:

  Texas Department of Transportation

  125 E. 11th Street
  Austin, TX  United States  78701-2483
• Project Managers:

  Odell, Wade

• Performing Organizations:

  Texas A&M Transportation Institute

  Texas A&M University System
  3135 TAMU
  College Station, TX  United States  77843-3135

  University of North Texas, Denton

  North Texas Discovery Park
  3940 North Elm St. Suite F-115
  Denton, TX  United States  76207
• Principal Investigators:

  Le, Minh

• Start Date: 20250901
• Expected Completion Date: 20270831
• Actual Completion Date: 0

Subject/Index Terms

• TRT Terms: Artificial intelligence; Connected vehicles; Crowdsourcing; Foreign object debris; Object detection; Traffic safety
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors;

Filing Info

• Accession Number: 01967588
• Record Type: Research project
• Source Agency: Texas Department of Transportation
• Contract Numbers: 0-7266
• Files: RIP, STATEDOT
• Created Date: Sep 29 2025 4:31PM