GraphDAC: A Graph-Analytic Approach to Dynamic Airspace Configuration

Ke Feng, Dahai Liu, Yongxin Liu, Hong Liu, Houbing Song
7/29/2023
math.OCcs.LGcs.MA

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Abstract

The current National Airspace System (NAS) is reaching capacity due to increased air traffic, and is based on outdated pre-tactical planning.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

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Cite this paper

@article{feng2023graphdac,
  title  = {GraphDAC: A Graph-Analytic Approach to Dynamic Airspace Configuration},
  author = {Ke Feng and Dahai Liu and Yongxin Liu and Hong Liu and Houbing Song},
  year   = {2023},
  eprint = {2307.15876},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2307.15876v1}
}

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