Volume 40 Issue 6
Dec.  2022
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ZHANG Xie, XIAO Enyuan, LIU Hongzhi, ZHAO Yifei, WANG Mengqi. An Evaluation method for the Suitability of Three Visibility Graphs in Analyzing the Fluctuation Characteristics of Arrival Flight Flows[J]. Journal of Transport Information and Safety, 2022, 40(6): 92-105. doi: 10.3963/j.jssn.1674-4861.2022.06.010
Citation: ZHANG Xie, XIAO Enyuan, LIU Hongzhi, ZHAO Yifei, WANG Mengqi. An Evaluation method for the Suitability of Three Visibility Graphs in Analyzing the Fluctuation Characteristics of Arrival Flight Flows[J]. Journal of Transport Information and Safety, 2022, 40(6): 92-105. doi: 10.3963/j.jssn.1674-4861.2022.06.010

An Evaluation method for the Suitability of Three Visibility Graphs in Analyzing the Fluctuation Characteristics of Arrival Flight Flows

doi: 10.3963/j.jssn.1674-4861.2022.06.010
  • Received Date: 2022-04-23
    Available Online: 2023-03-27
  • Understanding the fluctuation characteristics of air traffic flows plays a leading, essential, and key role in many aspects of their control and management, such as airspace configuration optimization, efficiency promotion, and safety assurance. This paper aims to evaluate the suitability of the visibility graph(VG), horizontal visibility graph(HVG), and limited penetrable visibility graph(LPVG) in analyzing the fluctuation characteristics of air traffic flows. A complex network based on the multi-scale time series data extracted from the same arrival flow is developed and the suitability of three visibility graphs is evaluated from the global and local structure perspectives. From the global perspective, a concept of details loss rate is proposed by considering the characteristics of the network structure-dependent matrix. Then a k-core cluster is used to analyze the suitability of quantifying the strength of flight flow fluctuations. From the local perspective, a transfer probability of fluctuation patterns is calculated using the sequential motifs method, and the suitability of the sequential motif with different lengths in characterizing fluctuation characteristics of flight flows is evaluated. The results show that: ①the loss rate of detail can be limited within 0.5 when the proportion of N value of the LPVG in network nodes ranges from 0.48% to 1.442%;②VG and LPVG(N=1~3) can effectively describe the intensity of fluctuation of flight flow time series data and the suitability value is 2.665, 4.810, 6.973, and 9.883, respectively; ③a long sequential motif would reduce the number of sequential motifs and result in the similarity of transition probability among different types of the sequential motifs, while a short sequential motif is useless for prediction due the chaotic characteristics of traffic flow. Thus, it is recommended to use the sequential motif with the length of 4, 5, 6, and 7 for VG and LPVG(N=1~3). In conclusion, the k-core cluster and the motifs method provide an in-depth analysis of the transfer characteristics among the fluctuation modes and the evolution of time dimension in air traffic, which offers support for delay prediction and plays a leading role in the actual operation management of flights.

     

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  • [1]
    邢雪, 于德新, 田秀娟, 等. 结合可视图的多状态交通流时间序列特性分析[J]. 物理学报, 2017, 66(23): 57-65. https://www.cnki.com.cn/Article/CJFDTOTAL-WLXB201723007.htm

    XING X, YU D X, TIAN X J, et al. Analysis of multi-state traffic flow time series properties using visibility graph[J]. Acta Physica Sinica, 2017, 66(23): 57-65. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-WLXB201723007.htm
    [2]
    LIU H Z, ZHANG X C, ZHANG X. Exploring dynamic evolution and fluctuation characteristics of air traffic flow volume time series: A single waypoint case[J]. Physica A: Statistical Mechanics and its Applications, 2018, 503.
    [3]
    周婷婷, 金宁德, 高忠科, 等. 基于有限穿越可视图的时间序列网络模型[J]. 物理学报, 2012, 61(3): 86-96. https://www.cnki.com.cn/Article/CJFDTOTAL-WLXB201203015.htm

    ZHOU T T, JIN N D, GAO Z K, et al. Limited penetrable visibility graph for establishing complex network from time series[J]. Acta Physica Sinica, 2012, 61(3): 86-96. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-WLXB201203015.htm
    [4]
    WANG J, YANG C, WANG R, et al. Functional brain networks in Alzheimer's disease: EEG analysis based on limited penetrable visibility graph and phase space method[J]. Physica A Statistical Mechanics & Its Applications, 2016, 460: 174-187.
    [5]
    MING Z, MA W, MENG Q, et al. Noise resistance ability analysis of the visibility graph and the limited penetrable visibility graph[C]. Intelligent Control & Automation, Guilin. China: IEEE, 2016.
    [6]
    REN W, JIN N. Sequential limited penetrable visibility-graph motifs[J]. Nonlinear Dynamics, 2020, 99(3): 2399-2408. doi: 10.1007/s11071-019-05439-y
    [7]
    LIU H Z, ZHANG X C, ZHANG X. Exploring dynamic evolution and fluctuation characteristics of air traffic flow volume time series: A single waypoint case[J]. Physica A: Statistical Mechanics and its Applications, 2018, 30(2): 1-21.
    [8]
    LIU H Z, ZHANG X C, ZHANG X. Multiscale complexity analysis on airport air traffic flow volume time series[J]. Physica A: Statistical Mechanics and its Applications, 2020(6): 1-17.
    [9]
    刘宏志. 空中交通流量波动动态演化及其非线性分析[D]. 北京: 北京交通大学, 2020.

    LIU H Z. Dynamic evolution and nonlinearity analysis of air traffic flow[D]. Beijing: Beijing Jiaotong University, 2020. (in Chinese)
    [10]
    王红勇, 邓涛涛, SONG Ziqi, 等. 基于复杂网络模型的航路网络特性分析[J]. Transactions of Nanjing University of Aeronautics and Astronautics, 2020, 37(2): 242-262. https://www.cnki.com.cn/Article/CJFDTOTAL-NJHY202002007.htm

    WANG H Y, DENG T T, SONG Z Q, WANG F, ZHAO Y F. Airway Network Characteristics Based on Complex Network Model[J]. Transactions of Nanjing University of Aeronautics and Astronautics, 2020, 37(2): 242-262. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-NJHY202002007.htm
    [11]
    隋东, 邢娅萍, 涂诗晨. 恶劣天气条件下航路网络修复优化[J]. 航空学报, 2021, 42(2): 323-334. https://www.cnki.com.cn/Article/CJFDTOTAL-HKXB202102028.htm

    SUI D, XING Y P, TU S C. Repair optimization strategy for air route networks under severe weather conditions[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(2): 323-334. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HKXB202102028.htm
    [12]
    张洪海, 胡勇, 杨磊, 等. 多机场终端区微观交通流建模与仿真分析[J]. 西南交通大学学报, 2015, 50(2): 368-374. https://www.cnki.com.cn/Article/CJFDTOTAL-XNJT201502025.htm

    ZHANG H H, HU Y, YANG L, et al. Modeling and simulation analysis of microscopic traffic flow in multi-airport terminal airspace[J]. Journal of Southwest Jiaotong University, 2015, 50(2): 368-374. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XNJT201502025.htm
    [13]
    张洪海, 范围, 廖志华, 等. 平行跑道运行模式对终端区交通流特性的影响研究[J]. 交通运输系统工程与信息, 2017, 17(3): 198-204. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT201703030.htm

    ZHANG H H, FAN W, LIAO Z H, et al. Impacts of parallel runway operation modes on air traffic flow characteristics in terminal areas[J]. Journal of Transportation Systems Engineering and Information Technology, 2017, 17(3): 198-204. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT201703030.htm
    [14]
    张洪海, 杨磊, 别翌荟, 等. 终端区进场交通流广义跟驰行为与复杂相变研究[J]. 航空学报, 2014, 36(3): 949-961. https://www.cnki.com.cn/Article/CJFDTOTAL-HKXB201503029.htm

    ZHANG H H, YANG L, BIE Y H, et al. Analysis on generalized following behavior and complex phase-transition law of approaching traffic flow in terminal airspace[J]. Acta Aeronautica et Astronautica Sinica, 2014, 36(3): 949-961. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HKXB201503029.htm
    [15]
    张洪海, 许炎, 张哲铭, 等. 终端区空中交通流参数模型与仿真[J]. 交通运输系统工程与信息, 2014, 14(6): 58-64. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT201406009.htm

    ZHANG H H, XU Y, ZHANG Z M, et al. Air traffic flow parameter model and simulation for airport terminal area[J]. Journal of Transportation Systems Engineering and Information Technology, 2014, 14(6): 58-64. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT201406009.htm
    [16]
    XU Y, ZHANG H, LIAO Z, et al. A dynamic air traffic model for analyzing relationship patterns of traffic flow parameters in terminal airspace[J]. Aerospace Science And Technology, 2016, 55(8): 10-23.
    [17]
    张洪海, 汤一文, 许炎. TBO模式下终端区进场交通流优化模型与仿真分析[J]. 航空学报, 2020, 41(7): 325-338. https://www.cnki.com.cn/Article/CJFDTOTAL-HKXB202007028.htm

    ZHANG H H, TANG Y W, XU Y. Optimizing arrival traffic flow in airport terminal airspace under trajectory based operations[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(7): 325-338. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HKXB202007028.htm
    [18]
    王超, 朱明, 赵元棣. 基于改进加权一阶局域法的空中交通流量预测模型[J]. 西南交通大学学报, 2018, 53(1): 206-213. https://www.cnki.com.cn/Article/CJFDTOTAL-XNJT201801025.htm

    WANG C, ZHU M, ZHAO Y D. Air traffic flow prediction model based on improved adding-weighted one-rank local-rejion method[J]. Journal of Southwest Jiaotong University, 2018, 53(1): 206-213. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XNJT201801025.htm
    [19]
    WEINREICH I, RICKERT H, LUKASCHEWITSCH M. Wavelet-based time series prediction for air traffic data[C]. Wavelet Applications in Industrial Processing, Bellingham: SPIE, 2004.
    [20]
    张弦, 王宏力. 嵌入维数自适应最小二乘支持向量机状态时间序列预测方法[J]. 航空学报, 2010, 31(12): 2309-2314. https://www.cnki.com.cn/Article/CJFDTOTAL-HKXB201012004.htm

    ZHANG X, WANG H L. Condition time series prediction using least squares support vector machine with adaptive embedding dimension[J]. Acta Aeronautica et Astronautica Sinica, 2010, 31(12): 2309-2314. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HKXB201012004.htm
    [21]
    王兴隆, 刘洋, 潘维煌. 空中交通系统自组织临界特性辨识及应用[J]. 交通信息与安全, 2020, 38(2): 96-101. doi: 10.3963/j.jssn.1674-4861.2020.02.012

    ZWANG X L, LIU Y, PAN W H, Identification and application of self-organized criticality of air traffic system. [J]. Journal of Transport Information and Safety, 2020, 38(2): 96-101. doi: 10.3963/j.jssn.1674-4861.2020.02.012
    [22]
    中国民用航空局空中交通管理局. 中国民航空管流量管理运行规则(试行)[R]. 北京: 中国民用航空局空中交通管理局, 2020.

    Civil Aviation Administration of China. Operation rules for air traffic control flow management of civil aviation of China(for Trial Implementation)[R]. Beijing: Air traffic Management Bureau of CAAC, 2020. (in Chinese)
    [23]
    LACASA L, LUQUE B, BALLESTEROS F, et al. From time series to complex networks: The visibility graph[J]. Proceedings of the National Academy of Sciences of the United States of America, 2008, 105(13): 4972-4975.
    [24]
    LUQUE B, LACASA L, BALLESTEROS F, et al. Horizontal visibility graphs: Exact results for random time series[J]. Physical Review E: Statistical, Nonlinear & Soft Matter Physics, 2009, 80(4 Pt 2): 046103.
    [25]
    NOOY W, MRVAR A, BATAGELJ V. Exploratory social network analysis with pajek[M]. Cambridge: Cambridge University Press, 2012.
    [26]
    SHEN-ORR S S, MILO R, MANGAN S, et al. Network motifs in the transcriptional regulation network of Escherichia coli[J]. Nature Genetics, 2002, 31(1): 64-68.
    [27]
    IACOVACCI J, LACASA L. Sequential visibility-graph motifs[J]. Physical Review E, 2016, 93(4): 042309.
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