AI-Enabled Vision System for Intersection Analytics

Details

• Creators:
  Adu-Gyamfi, Yaw ; Tanner, Muturi
• Corporate Creators:
  University of Missouri--Columbia. Department of Civil and Environmental Engineering
• Corporate Contributors:
  Missouri. Department of Transportation. Construction and Materials Division ; United States. Department of Transportation. Federal Highway Administration
• Subject/TRT Terms:
  • Computer vision
  • Turning traffic
  • Traffic signal timing
  • Traffic signal intervals
  • Artificial intelligence
  • Machine learning
  • Traffic data
  • Automatic data collection systems
  • Intersections
  • Metrics (Quantitative assessment)
  More +
• Publication/ Report Number:
  cmr 25-012
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report (September 2024-September 2025)
• Corporate Publisher:
  Missouri. Department of Transportation. Construction and Materials Division
• Abstract:
  This project developed and demonstrated an AI-enabled vision system for intersection analytics capable of automatically generating Turning Movement Counts (TMCs) and integrating them with Signal Phase and Timing (SPaT) data to evaluate traffic signal performance. Using existing Missouri Department of Transportation (MoDOT) CCTV (Closed Circuit Television) infrastructure, the system eliminated the need for manually placed detection zones and significantly reduced the labor traditionally required for data collection.
• Format:
  PDF
• Funding:
  TR202506
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/86975/dot_86975_DS1.pdf
• File Type:
  [PDF - 2.22 MB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:da061584ae3da639346b8969cda4230a5e6de0e34192320332bd9ce39e08fc5a261e1574188ac66d74ba7590d6a88d5e09143a448cb9418a6294b68054edfdc0