| Citation: | WU Minggong, YE Zelong, WEN Xiangxi, et al. Air traffic complexity recognition method based on complex networks[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(5): 839-850. doi: 10.13700/j.bh.1001-5965.2019.0354(in Chinese)
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Identifying the complexity of air traffic is an important task in air traffic management. Most current algorithms are usually tested using some macro-indexes, such as aircraft density, aircraft clusters, stranded degree, and so on. In this paper, the air traffic situation is described from the perspective of complex networks: aircraft in airspace are regarded as nodes and edges form within Airborne Collision Avoidance System (ACAS) communication ranges. The dynamic air traffic situation is studied by selecting topological characteristic indexes such as loop numbers, node strength, average clustering coefficient, betweenness centrality and network efficiency. On this basis, Independent Component Analysis (ICA) is used to recognize air traffic complexity online and treat the smooth traffic as a training data set. The congestion is recognized according to the changes of SPE-statistic,
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