GraphDAC: A Graph-Analytic Approach to Dynamic Airspace Configuration
GraphDAC: A Graph-Analytic Approach to Dynamic Airspace Configuration
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DOI：
10.1109/iri58017.2023.00048
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发表时间：
2023-07
期刊：
影响因子：
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通讯作者：
Ke-ke Feng;Dahai Liu;Yongxin Liu;Hong Liu;H. Song
中科院分区：
文献类型：
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作者：
Ke-ke Feng;Dahai Liu;Yongxin Liu;Hong Liu;H. Song
由于空中流量的增加，目前的国家空域系统（NAS）达到了容量，并且基于过时的时期计划。这项研究提出了一种更具动态的空域配置（DAC）方法，可以增加吞吐量并适应波动的交通，这是紧急情况的理想选择。所提出的方法将领空作为约束图形构建，压缩其尺寸，并应用了启用光谱群集的自适应算法来生成协作机场组，并均匀地分发了其中的工作负载。在各种交通条件下，我们的实验表明工作量失衡减少了50％。这项研究最终可能构成用于优化空域配置的推荐系统的基础。可在https://github.com/kefenge2022/graphdac.git上找到代码。
The current National Airspace System (NAS) is reaching capacity due to increased air traffic, and is based on outdated pre-tactical planning.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.