Assessment of Parcel Delivery Systems Using Unmanned Aerial Vehicles

Unmanned aerial vehicles (drones) can be used in traffic and road monitoring applications. The authors investigate methods for routing a drone that is capable of simultaneously detecting road conditions and estimating traffic densities. First, the authors analyze the impact of incidents on model parameters and the difficulty in detecting capacity drops under congested conditions using speed-density observations. Then, the authors propose a framework for adaptively navigating a drone to minimize the uncertainty on parameter and traffic state estimates. This framework (1) assimilates drone observations and local sensor data, (2) quantifies the uncertainty on road network conditions and traffic states, and (3) navigates the drone towards targeted observations that minimize this uncertainty. Compared to estimation without a drone, the authors show that they can obtain significant improvement in traffic estimation and incident detection by adaptively navigating a mobile sensor that is capable of detecting capacity drops. In the future, the proposed drone navigation framework will be used for evaluating the benefit of parcel delivery systems that rely on drone detection and delivery capabilities.

• Record URL:
  https://cammse.uncc.edu/sites/cammse.uncc.edu/files/media/CAMMSE-UNCC-2018-UTC-Project-Report-08-Boyles-Final.pdf
• Summary URL:
  https://cammse.uncc.edu/sites/cammse.uncc.edu/files/media/CAMMSE-UNCC-2018-UTC-Project-Information-08-Boyles.pdf
• Record URL:
  https://rosap.ntl.bts.gov/view/dot/63073
• Supplemental Notes:
  • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
• Corporate Authors:

  Center for Advanced Multimodal Mobility Solutions and Education

  University of North Carolina, Charlotte
  Charlotte, NC  United States  28223

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Authors:
  • Boyles, Stephen D
  • 0000-0002-5414-5438
  • Yahia, Cesar
  • 0000-0001-9062-8149
• Publication Date: 2019-9

Language

• English

Media Info

• Media Type: Digital/other
• Edition: Interim Report
• Features: Figures; References;
• Pagination: 38p

Subject/Index Terms

• TRT Terms: Data collection; Delivery service; Drones; Evaluation and assessment; Highway capacity; Incident detection; Sensors; Traffic estimation; Uncertainty
• Subject Areas: Aviation; Data and Information Technology; Vehicles and Equipment;

Filing Info

• Accession Number: 01724973
• Record Type: Publication
• Contract Numbers: 2018 Project 08
• Files: UTC, NTL, TRIS, ATRI, USDOT
• Created Date: Dec 12 2019 9:23AM