Machine Learning for Dynamic Airspace Configuration towards Optimized Mobility in 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.


    • English


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


    • 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

    • Performing Organizations:

      Embry-Riddle Aeronautical University

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

      Song, Houbing

      Liu, Dahai

    • Start Date: 20201001
    • Expected Completion Date: 20221231
    • Actual Completion Date: 0
    • USDOT Program: University Transportation Centers Program

    Subject/Index Terms

    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