Application of Artificial Intelligence in the Optimization of Mobility in Dynamic Airspace Configurations During Emergency Situations

Air traffic control (ATC) system is extremely complex so it is impossible to address every component under emergency operation. In this project, the research team narrows the research scope down to airspace configuration. Current national airspace configuration follows a static layout which cannot adapt to the dynamic air traffic conditions or incoming emergency events. Therefore, the team proposes to boost the current ATC system by developing a novel machine learning (ML)-based dynamic airspace configuration (DAC) framework. Different from the traditional statistical and graphical based DAC approaches, the proposed ML-based framework aims to discover the difference of DAC on areas with different air traffic pattern, so that a mapping between ATC control and the air traffic evaluation metrics can be found. The proposed framework will provide: a DAC model that is able to self-adjust the airspace configuration based on the air traffic demands of different time periods of the day or emergency events, thus providing increased airspace capacity, safety and efficiency of ATC operations under unexpected situations with rapid demand changes.

Language

• English

Project

• Status: Completed
• Funding: $220580
• Contract Numbers:

  69A3551747125

• Sponsor Organizations:

  Center for Advanced Transportation Mobility

  North Carolina Agricultural and Technical State University
  Greensboro, NC  United States  27411

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Project Managers:

  Song, Houbing

  Liu, Dahai

  Liu, Yongxin

• Performing Organizations:

  Embry-Riddle Aeronautical University

  600 S. Clyde Morris Boulevard
  Daytona Beach, Fl  United States  32114
• Principal Investigators:

  Song, Houbing

  Liu, Dahai

  Liu, Yongxin

• Start Date: 20201001
• Expected Completion Date: 20221231
• Actual Completion Date: 20230531
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Air traffic control; Airspace utilization; Disasters and emergency operations; Machine learning
• Subject Areas: Aviation; Operations and Traffic Management; Planning and Forecasting; Security and Emergencies;

Filing Info

• Accession Number: 01784515
• Record Type: Research project
• Source Agency: Center for Advanced Transportation Mobility
• Contract Numbers: 69A3551747125
• Files: UTC, RIP
• Created Date: Oct 11 2021 11:24PM