A complex network analysis approach for identifying air traffic congestion based on independent component analysis

Xurui Jiang, Xiangxi Wen, Minggong Wu, Min Song and Congliang Tu

Physica A: Statistical Mechanics and its Applications, 2019, vol. 523, issue C, 364-381

Abstract: Identifying air traffic congestion in air transportation domain is a challenging task. Most current algorithms are usually tested using some macro-indexes, such as aircraft density, aircraft clusters, stranded degree, and so on. In this work, we describe the air traffic situation 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 topological property indicators in complex networks, such as loop numbers, node strength, average clustering coefficient, between-ness centrality and weighted network efficiency, are adopted for dynamic air traffic situation. On this basis, several assessment methods can identify air traffic congestion. Nevertheless, since these methods need to set thresholds subjectively, we propose the use of independent component analysis (ICA) for online monitoring air traffic congestion. In this way, smooth situations are treated as datasets for training, and congested situations can be identify according to the change of SPE-statistic, I2-statistic and Ie2-statistic. The simulation results show that proposed method has the ability to identify air traffic congestion well.

Keywords: Air traffic complexity; Congestion identification; Complex networks; Independent component analysis; Air traffic management (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:523:y:2019:i:c:p:364-381

DOI: 10.1016/j.physa.2019.01.129

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