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

Authors

Ke Feng, Embry-Riddle Aeronautical University

Document Type

Report

Publication Date

5-2023

Keywords

Emergency, Evacuation, Dynamic Airspace Configuration

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.

Comments

As of 8/30/2024 this code is available on GitHub and Kaggle. See links below.

https://github.com/KeFenge2022/GraphDAC/tree/main

https://www.kaggle.com/datasets/usdot/flight-delays

Recommended Citation

Feng, Ke, "Application of Artificial Intelligence in the Optimization of Mobility in Dynamic Airspace Configurations During Emergency Situations" (2023). Center for Advanced Transportation Mobility. 28.
https://digital.library.ncat.edu/catm/28