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

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  • Liu, Hongzhi
  • Zhang, Xingchen
  • Zhang, Xie

Abstract

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.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:503:y:2018:i:c:p:560-571
    DOI: 10.1016/j.physa.2018.02.030
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    Cited by:

    1. Chandra, Aitichya & Verma, Ashish & Sooraj, K.P. & Padhi, Radhakant, 2023. "Modelling and assessment of the arrival and departure process at the terminal area: A case study of Chennai international airport," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 615(C).
    2. 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).
    3. Chen, Yunxiang & Zhao, Yifei & Wu, Yexin, 2024. "Recent progress in air traffic flow management: A review," Journal of Air Transport Management, Elsevier, vol. 116(C).
    4. Olivares, Felipe & Sun, Xiaoqian & Wandelt, Sebastian & Zanin, Massimiliano, 2023. "Measuring landing independence and interactions using statistical physics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 170(C).
    5. 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).
    6. Zhang, Mengyao & Huang, Tao & Guo, Zhaoxia & He, Zhenggang, 2022. "Complex-network-based traffic network analysis and dynamics: A comprehensive review," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).

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