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. This framework is founded on short time analysis (1-5 minutes) which is not valid up to date. The research team foresees that using a “flock” of interconnected, self-managed drones, can establish a deployable system to perform immediate monitoring/assessment for traffic condition to infer if congestion is approached. The drones will use their own computational and communication capabilities to host an integrated reconnaissance platform that performs traffic monitoring and traffic analysis in real-time fashion. Unlike conventional image processing approaches, a specialized “inverse image” processing technique will be investigated in this project to suit the limited computing abilities for the drones, namely “Model Based Image Processing” (MBIP). In this technique, the targeted features are represented using specific statistical distribution patterns and/or equations. The patterns/equations will be utilized as an inverse filter that will be applied by the reconnaissance device to trigger if the feature has been observed, in this case the feature will correlate to imminent congestion onset. This module requires a very limited computational power and therefore it can be easily integrated in onboard drone circuits. The targeted feature (herein, features correlated to traffic congestion) will be reproduced utilizing a traffic simulation models. The developed framework will be introduced as Traffic Model-Based Image Processing (T-MBIP). The proposed methodology will be tested first in a hardware in the loop simulation environment to examine its fidelity and then, if possible, in a full scale field environment. The project will 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 mitigations actions to be undertaken before congestion happens rather than after traffic flow has already broken down.

Project

  • Status: Active
  • 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: 20190731
  • Actual Completion Date: 0
  • 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