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Exploring the impact of flow values on multiscale complexity quantification of airport flight flow fluctuations

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  • Liu, Hongzhi
  • Zhang, Xie
  • Hu, Huaqing
  • Zhang, Xingchen

Abstract

Being able to characterize the dynamic characteristics of the air traffic system and embody the dynamic evolution thereof, the complexity of air traffic flows has attracted the broad attention of scholars and researchers. To fully consider amplitude information, we adopt the improved multiscale weighted permutation entropy (IMWPE) and propose an improved multivariate multiscale weighted permutation entropy (IMMWPE) to explore the complexity of flight flows of the top ten busy airports in China from a univariate and a multivariate perspective, respectively. In addition, we propose an improved multiscale weight contingency to investigate the inhomogeneity between fluctuation pattern distributions in departure and arrival flight flows. At the advantages of the multiple coarse-graining procedures, we obtain more reliable and stable entropy and contingency results, considering amplitude information. Comparisons between the weighted and unweighted results show that the weighted methods are as capable as the unweighted in exactly characterizing the dynamic characteristics of flight flow fluctuations, i.e., temporal scale dependency, spike distributions, differences between univariate and multivariate perspectives. Results also show that the weight has a stronger impact on the complexity of univariate time series than the multivariate. From the univariate perspective, the complexity of departure and arrival flight flows of ZLXY and ZGGG is more sensitive to the amplitude information. From the multivariate perspective, ZUCK could be a typical representative of the complexity of the total flight flows of the ten airports.

Suggested Citation

  • Liu, Hongzhi & Zhang, Xie & Hu, Huaqing & Zhang, Xingchen, 2022. "Exploring the impact of flow values on multiscale complexity quantification of airport flight flow fluctuations," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
    • Handle: RePEc:eee:chsofr:v:165:y:2022:i:p1:s0960077922009742
    DOI: 10.1016/j.chaos.2022.112795
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    References listed on IDEAS

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    1. Jiang, Xurui & Wen, Xiangxi & Wu, Minggong & Song, Min & Tu, Congliang, 2019. "A complex network analysis approach for identifying air traffic congestion based on independent component analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 364-381.
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    3. Liu, Hongzhi & Zhang, Xingchen & Zhang, Xie, 2020. "Multiscale multifractal analysis on air traffic flow time series: A single airport departure flight case," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    4. Zhang, Xie & Liu, Hongzhi & Zhao, Yifei & Zhang, Xingchen, 2019. "Multifractal detrended fluctuation analysis on air traffic flow time series: A single airport case," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 531(C).
    5. Liu, Hongzhi & Zhang, Xingchen & Zhang, Xie, 2018. "Exploring dynamic evolution and fluctuation characteristics of air traffic flow volume time series: A single waypoint case," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 560-571.
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