GraphDAC: A Graph-Analytic Approach to Dynamic Airspace Configuration

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-07-29 DOI:10.1109/IRI58017.2023.00048

Ke-ke Feng, Dahai Liu, Yongxin Liu, Hong Liu, H. Song

引用次数: 0

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The current National Airspace System (NAS) is reaching capacity due to increased air traffic, and is based on outdated pre-tactical planning.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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动态空域配置的图解析方法

由于空中交通的增加，目前的国家空域系统(NAS)正在达到容量，并且是基于过时的战术前规划。本研究提出了一种更动态的空域配置(DAC)方法，可以提高吞吐量并适应波动的流量，是紧急情况的理想选择。该方法将空域构建为约束嵌入图，压缩其维度，并应用支持谱聚类的自适应算法生成协作机场组，并在其中均匀分配工作负载。在各种交通条件下，我们的实验表明工作负载不平衡减少了50%。该研究最终可以为优化空域配置的推荐系统奠定基础。代码可从https://github.com/KeFenge2022/GraphDAC.git获得。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)

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