Evaluating Different Methods for Estimating Queue Length on Access Ramps

Understanding the queue length and queuing time of ramp is important for transportation agencies to manage and operate the ramps with optimum performance. Since these data are collected with conventional sensors system such as coil, which are prone to error specially during the congestion. The increased deployment of cameras and recent advancements in artificial intelligence such as deep learning and computer vision gives an opportunity to employ traffic surveillance camera videos for ramp management. In this study, the authors employed the four location surveillance cameras videos to develop and evaluate the framework developed using the object detection and tracking algorithms. This framework uses the existing camera videos as input to the framework and determine the queueing parameters of highway on ramps such as queue length and queuing time which provides an important information to freeway management team to optimize signal timing. Additionally, this study provides a detailed implementation plan for computer vision and optimum location of the camera installation and hardware requirements.

• Record URL:
  https://www.ugpti.org/resources/reports/details.php?id=1145
• Record URL:
  https://rosap.ntl.bts.gov/view/dot/72889
• Summary URL:
  https://rosap.ntl.bts.gov/view/dot/73021
• Supplemental Notes:
  • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
• Corporate Authors:

  University of Utah, Salt Lake City

  Department of Civil and Environmental Engineering
  Salt Lake City, UT  United States 

  Utah Department of Transportation

  Salt Lake City, UT  United States 

  Mountain-Plains Consortium

  North Dakota State University
  Fargo, ND  United States  58108

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Authors:
  • Tiwari, Sushant
  • Rashidi, Abbas
  • Markovic, Nikola
• Publication Date: 2023-11

Language

• English

Media Info

• Media Type: Digital/other
• Edition: Final Report
• Features: Figures; Photos; References; Tables;
• Pagination: 48p

Subject/Index Terms

• TRT Terms: Cameras; Computer vision; Data collection; Detection and identification systems; Interchange ramps; Traffic queuing; Traffic surveillance
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01903517
• Record Type: Publication
• Report/Paper Numbers: MPC 23-507
• Contract Numbers: MPC-699
• Files: UTC, NTL, TRIS, USDOT, STATEDOT
• Created Date: Dec 27 2023 12:18PM