GraphDAC: A Graph-Analytic Approach to Dynamic Airspace Configuration

TL;DR

This paper introduces GraphDAC, a dynamic, graph-based method for reconfiguring airspace to improve traffic handling and workload distribution, demonstrating significant workload imbalance reduction in simulations.

Contribution

It presents a novel spectral clustering-based algorithm for dynamic airspace configuration, enhancing capacity and emergency responsiveness over traditional static methods.

Findings

50% reduction in workload imbalances under various traffic conditions

Constructs a constraints-embedded graph for adaptive airspace partitioning

Provides a basis for an optimized airspace configuration recommendation system

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The current National Airspace System (NAS) is reaching capacity due to increased air traffic, and is based on outdated pre-tactical planning.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