# GraphDAC: A Graph-Analytic Approach to Dynamic Airspace Configuration

arXiv: https://arxiv.org/abs/2307.15876 · HTML: https://mlsanity.com/paper/2307.15876/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 20 references that are in the map.
- First posted on arXiv 29 Jul 2023.

## Builds on (most cited first, 2 of 2)

- [Medical image denoising using convolutional denoising autoencoders](https://mlsanity.com/paper/1608.04667/) (2016)
- [Counter-Unmanned Aircraft System(s) (C-UAS): State of the Art, Challenges and Future Trends](https://mlsanity.com/paper/2008.12461/) (2020)

## Built on by (most cited first, 1 of 1)

- [ASPaeroFlow: Decomposition Heuristics for Joint Air Traffic Flow & Capacity Management](https://mlsanity.com/paper/2608.09315/) (2026)

## Abstract (arXiv)

The current National Airspace System (NAS) is reaching capacity due to increased air traffic, and is based on outdated pre-tactical planning.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