GraphDAC: A Graph-Analytic Approach to Dynamic Airspace Configuration
Ke Feng Dahai Liu Yongxin Liu Hong Liu Houbing Song
The current National Airspace System (NAS) is reaching capacity due to
increased air traffic, and is based on outdated pre-tactical planning. This
study proposes a more dynamic airspace configuration (DAC) approach that could
increase throughput and accommodate fluctuating traffic, ideal for emergencies.
The proposed approach constructs the airspace as a constraints-embedded graph,
compresses its dimensions, and applies a spectral clustering-enabled adaptive
algorithm to generate collaborative airport groups and evenly distribute
workloads among them. Under various traffic conditions, our experiments
demonstrate a 50\% reduction in workload imbalances. This research could
ultimately form the basis for a recommendation system for optimized airspace
configuration. Code available at https://github.com/KeFenge2022/GraphDAC.git