GraphDAC: A Graph-Analytic Approach to Dynamic Airspace Configuration
This addresses air traffic management challenges for aviation authorities by enabling more efficient and adaptable airspace use, though it is an incremental improvement over existing methods.
The study tackled the capacity limitations of the National Airspace System by proposing a dynamic airspace configuration approach using graph analytics, which reduced workload imbalances by 50% under various traffic conditions.
The current National Airspace System (NAS) is reaching capacity due to increased air traffic, and is based on outdated pre-tactical planning.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