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

Details

• Creators:
  Feng, Ke ; Liu, Yongxin ; Meier-Dornberg, Marc ; Mwangi, Wairimu ; Liu, Dahai ; Song, Houbing
• Corporate Creators:
  North Carolina A&T State University. Transportation Institute. Center For Advanced Transportation Mobility ; Embry-Riddle Aeronautical University ; University of Maryland, Baltimore County
• 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:
  • Air traffic control
  • Airspace utilization
  • Disasters and emergency operations
  • Machine learning
  • Artificial intelligence
  • Emergencies
  • Evacuation
  • Dynamic Airspace Configuration
• Publication/ Report Number:
  CATM-2023-R3-ERAU
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report October 2020 – May 2023
• Corporate Publisher:
  North Carolina A&T Sate University. Transportation Institute. Center For Advanced Transportation Mobility
• Abstract:
  In emergency situations, the timely evacuation of people is crucial, and air traffic plays a significant role. Dynamic airspace configuration (DAC) is a promising approach to maximizing air traffic throughput while accommodating dynamic traffic changes, making it suitable for emergency evacuation. This study aims to use artificial intelligence to develop practical DAC for emergency evacuation. The proposed modeling method constructs the airspace as a graph and applies a spectral clustering algorithm to group airports and balance the workload among sectors. Sharing air traffic control resources within the cluster can lead to more efficient air traffic control. Two sets of experiments conducted under different air traffic conditions show that our approach reduces the workload unbalance level among sectors by 50%. This study has the potential to be further developed into a recommendation system to assist with airspace configuration during emergency evacuations.
• Format:
  PDF
• Funding:
  69A3551747125
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/74663/dot_74663_DS1.pdf
• File Type:
  [PDF - 2.41 MB]
• Collection(s):
  University Transportation Centers
• Main Document Checksum:
  urn:sha-512:c29a9ec2af70871945f8969568ac89e9d74057148094338be265d603feab37e071cf3f18a2e258a50021a6df61bd9bb3352820e2a8c48d323d941a14bbf86109