Evaluating Different Methods for Estimating Queue Length on Access Ramps

Details

• Creators:
  Tiwari, Sushant ; Rashidi, Abbas ; Markovic, Nikola
• Corporate Creators:
  University of Utah. Department of Civil and Environmental Engineering
• Corporate Contributors:
  Mountain-Plains Consortium ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  • Computer vision
  • Data collection
  • Ramp metering
  • Ramps
  • Traffic queuing
  • Machine learning
  • Artificial intelligence
  • Video cameras
• Publication/ Report Number:
  MPC-699 ; MPC 23-507
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States ; Utah
• Edition:
  Final Report
• Corporate Publisher:
  Mountain-Plains Consortium
• Abstract:
  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, we 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.
• Format:
  PDF
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/72889/dot_72889_DS1.pdf
• File Type:
  [PDF - 4.06 MB]
• Collection(s):
  Artificial Intelligence Deployment Center of Excellence (AI COE) ; University Transportation Centers
• Main Document Checksum:
  urn:sha-512:8bd8a8577347affc7328dc7a28c505d380a3b9b67566f58e257bd48ab48642857de5fb3b5fafcd4598adaebd5ffed6d3840f2c7bcc30332b1702f86bd1d5273f