Autonomous Aircraft: Challenges and Opportunities

The emerging field of Advanced Air Mobility (AAM) presents myriad opportunities for disrupting traditional modes of transport, including passenger travel to cargo logistics. However, its path to full-scale adoption is fraught with regulatory, market, and logistical challenges. This report presents a nuanced understanding of AAM's complexities and its potential for transformative impact, particularly to reduce the impacts on surface transportation degradation. The research employs data-driven methodologies, machine learning algorithms, and Geographic Information System (GIS) techniques to explore the landscape of AAM. These studies reveal the crucial role of regulatory frameworks and gross domestic product in AAM adoption, the importance of accurate market forecasting, and the value of identifying key commodity and geographical targets for cargo drones. Additionally, this study highlights the potential of AAM in safely transporting dangerous cargo and improving pharmaceutical supply chains. The successful integration of AAM into global transportation systems requires a multi-disciplinary and multi-stakeholder approach. This study highlights the need for future research to build on this work to scale and optimize AAM technologies to meet the varying needs of nations and industries worldwide.

• Record URL:
  https://www.ugpti.org/resources/reports/details.php?id=1142
• Record URL:
  https://rosap.ntl.bts.gov/view/dot/72546
• Summary URL:
  https://rosap.ntl.bts.gov/view/dot/72612
• Supplemental Notes:
  • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
• Corporate Authors:

  North Dakota State University, Fargo

  Fargo, ND  United States  58105

  Mountain-Plains Consortium

  North Dakota State University
  Fargo, ND  United States  58108

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Authors:
  • Bridgelall, Raj
  • Tolliver, Denver
• Publication Date: 2023-11

Language

• English

Media Info

• Media Type: Digital/other
• Edition: Final Report
• Features: Figures; Maps; References; Tables;
• Pagination: 51p

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Delivery service; Drones; Geographic information systems; Machine learning; Market assessment; Regulation; Transportation planning
• Subject Areas: Aviation; Freight Transportation; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01898641
• Record Type: Publication
• Report/Paper Numbers: MPC 23-506
• Contract Numbers: MPC-666
• Files: UTC, NTL, TRIS, ATRI, USDOT
• Created Date: Nov 13 2023 9:00AM