Fly-By Image Processing for Real Time Congestion Mitigation

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. The proposed congestion mitigation approach is based on the premise that a short time analysis (1-5 minutes) will be sufficient to manage the congestion. We 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.

Project

  • Status: Completed
  • Funding: $122581
  • Contract Numbers:

    69A3551747104

  • Sponsor Organizations:

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Managing Organizations:

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

    University of Florida
    365 Weil Hall
    Gainesville, FL  United States  32611
  • Project Managers:

    Tucker-Thomas, Dawn

  • Performing Organizations:

    University of Alabama, Birmingham

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

    North Carolina State University

    Department of Civil, Construction and Environmental Engineering
    2501 Stinson Drive
    Raleigh, NC  United States  27695
  • Principal Investigators:

    Uddin, Nasim

  • Start Date: 20180801
  • Expected Completion Date: 20201130
  • Actual Completion Date: 20210513
  • USDOT Program: University Transportation Centers Program

Subject/Index Terms

Filing Info

  • Accession Number: 01680031
  • Record Type: Research project
  • Source Agency: Southeastern Transportation Research, Innovation, Development and Education Center (STRIDE)
  • Contract Numbers: 69A3551747104
  • Files: UTC, RIP
  • Created Date: Sep 4 2018 10:30AM