Corridor-Wide Surveillance Using Unmanned Aircraft Systems

Details

• Creators:
  Zhang, Yu ; Kourtellis, Achilleas ; Post, Joseph ; Tang, Hualong ; Yelchuri, Keerthana ; Porter, Brian
• Corporate Creators:
  University of South Florida. National Institute for Congestion Reduction
• Corporate Contributors:
  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:
  Applications Cameras Data Collection Design Drones Experimental Design Experiments Incident Management Literature Reviews Machine Learning Object Detection Object Detection Algorithm Traffic Traffic Congestion Traffic Data Transportation Unmanned Aircraft Systems

  More +
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report [September 2021 – August 2022]
• Corporate Publisher:
  University of South Florida. National Institute for Congestion Reduction
• Abstract:
  In the second phase of the project, the research team continued traffic data collection with unmanned aerial systems (UAS) and dual sensors (RGB and thermal cameras) for roadways under normal operational condition and during incident clearance. The research team conducted a thorough literature review of incident detection methods. Based on the review outcomes, the research team extracted traffic features from learning the video data and trained several learning models for identifying non-congestion conditions caused by incidents. This method was tested on a two-minute video with data captured by a drone at a location at which traffic was passing through an incident site. The results show that some machine learning models (support vector machine, K nearest neighbor, and random forest) performed very well in F1 scoring.
• Format:
  PDF
• Funding:
  69A3551947136, 79075-14, 79075-15
• Collection(s):
  University Transportation Centers
• Main Document Checksum:
  urn:sha-512:abc9b752d07362a9e76c2ffb634ee077420719f49b03ada75142985927e8915f9cdc9dc1e93b5844c83ea79094065bbb847e6973332fe1bd320177abd74711bb
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/89333/dot_89333_DS1.pdf
• File Type:
  [PDF - 2.25 MB ]