STRIDE Project: Fly-By Image Processing for Real Time Congestion Mitigation (Project H2) [supporting dataset]

Traffic monitoring is the centerpiece of congestion mitigation and traffic management. Whilst surveillance technologies have matured enough to provide informative depiction for the traffic, the current state-of-the-art systems cannot support immediate congestion problems. Proactive congestion mitigation requires a) real-time surveillance for traffic parameters, b) prediction for imminent congestion onset, in order to c) inform responsible parties to take immediate actions to prevent congestion. This framework is founded on short time analysis (1-5 minutes) which is not valid up to date. The authors foresee that using a “flock” of interconnected, self-managed drones, can establish a deployable system to perform immediate monitoring/assessment for traffic conditions to infer if congestion is approached. To detect vehicles, a faster technique of Convolutional Neural Network (CNN) called YOLOv3 is used. In this technique, a single neural network is used to the full image which divides the image into regions and predicts bounding boxes and probabilities for each region. Then these bounding boxes are weighted by the predicted probabilities. This technique requires huge computational power and therefore, GPUs are used to process the videos recorded by drones’ cameras. By calibrating the camera using real values compared to their apparent values in images, the detected vehicles can be tracked. The targeted feature (herein, features correlated to traffic congestion) were reproduced utilizing a traffic simulation model. The proposed methodology was tested by collecting and investigating video images from drones. The project, if continued further, has the potential to advance the state of proactive traffic and congestion management by embedding a distributed, simulation-based traffic state prediction system within the integrated drone surveillance software to enable congestion mitigation actions to be undertaken before congestion happens rather than after traffic flow has already broken down.

• Dataset URL:
  https://doi.org/10.5281/zenodo.4688028
• Record URL:
  https://stride.ce.ufl.edu/wp-content/uploads/2021/05/STRIDE-Project-H2-Final-Report-.pdf
• Record URL:
  https://rosap.ntl.bts.gov/view/dot/57434
• Dataset URL:
  https://rosap.ntl.bts.gov/view/dot/58445
• Supplemental Notes:
  • The dataset supports report: Fly-By Image Processing for Real-Time Congestion Mitigation, available at the URL above. This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
• Corporate Authors:

  University of Alabama, Birmingham

  Department of Civil, Construction and Environmental Engineering
  1075 13th Street South
  Birmingham, AL  United States  35294

  North Carolina State University, Raleigh

  Raleigh, NC  United States  27695

  Southeastern Transportation Research, Innovation, Development and Education Center (STRIDE)

  University of Florida
  365 Weil Hall
  Gainesville, FL  United States  32611

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Authors:
  • Uddin, Nasim
  • 0000-0001-6921-5577
  • Latef, Abdel Aziz Abdel
  • 0000-0002-2005-794X
• Publication Date: 2021-4-14

Language

• English

Media Info

• Media Type: Dataset
• Dataset: Version: 1 Integrity Hash:
• Dataset publisher:

  Zenodo

  ,    

Subject/Index Terms

• TRT Terms: Drones; Image processing; Neural networks; Traffic data; Traffic surveillance
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01779341
• Record Type: Publication
• Contract Numbers: 69A355174710
• Files: UTC, NTL, TRIS, ATRI, USDOT
• Created Date: Aug 24 2021 10:34AM