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Exploring dynamic evolution and fluctuation characteristics of air traffic flow volume time series: A single waypoint case

机译:探索空气流量流量时间序列的动态演化与波动特性:单路径

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Understanding the dynamic evolution and fluctuation characteristics of air traffic flow volume time series is the basis for designing effective air traffic flow management measures and controlling strategy. The research on optimization and control of air traffic flow management is fruitful. However, there is little research on dynamic evolution and fluctuation characteristics of air traffic flow volume time series. With the incorporation of complex networks theory into the time series analysis, we get complex networks description of air traffic flow volume time series in about 24 h length, correlate the visibility lines and air traffic flow volume fluctuations, extract the fluctuation patterns, differentiate the fluctuation characteristics to explore the fluctuation patterns distribution. We find that there are significant fluctuation patterns and the transition loops between these fluctuation patterns in the time series. The distribution of fluctuation patterns is not even. The minimal difference is 0.0588, and the maximal difference is 0.7199. The work in our paper maybe helpful for scholars and engineers in understanding the intrinsic nature of air traffic and in development of intelligent assistant decision making systems for air traffic management. (C) 2018 Elsevier B.V. All rights reserved.
机译:了解空中流量流量时间序列的动态演化和波动特性是设计有效空中流量管理措施和控制策略的基础。富有成效的空中流量管理优化与控制研究。然而,空中流量流量时间序列的动态演化和波动特性几乎没有研究。随着复杂网络理论的融入时间序列分析,我们得到复杂的网络的空中流量体积时间序列在大约24小时的时间内,关联能见度线和空气流量流量波动,提取波动模式,区分波动探索波动模式分布的特点。我们发现,在时间序列中存在显着的波动模式和这些波动模式之间的过渡环。波动模式的分布甚至不是。最小差异为0.0588,最大差异为0.7199。本文中的工作可能有助于学者和工程师在了解空中交通的内在性质以及在智能助理决策制定系统中进行空中交通管理。 (c)2018年elestvier b.v.保留所有权利。

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