基于有限穿越可视图的进场航班流量波动特性研究

doi:10.16097/j.cnki.1009-6744.2022.06.025

交通运输系统工程与信息 ›› 2022, Vol. 22 ›› Issue (6): 244-257.DOI: 10.16097/j.cnki.1009-6744.2022.06.025

基于有限穿越可视图的进场航班流量波动特性研究

张勰^*1，肖恩媛¹，刘宏志²，赵嶷飞¹，王梦琦¹

1. 中国民航大学，空中交通管理学院，天津 300300；2. 中国民航科学技术研究院，民航发展规划研究院，北京 100028

收稿日期:2022-04-22 修回日期:2022-10-20 接受日期:2022-11-07 出版日期:2022-12-25 发布日期:2022-12-23
作者简介:张勰(1981- )，男，陕西咸阳人，副研究员。
基金资助:
国家自然科学基金委员会与中国民用航空局联合资助项目(U1633112)

Fluctuation Characteristics of Arrival Flight Flow Based on Limited Penetrable Visibility Graph

ZHANG Xie^*1, XIAO En-yuan¹, LIU Hong-zhi², ZHAO Yi-fei¹, WANG Meng-qi¹

1. College of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China; 2. Institute of Civil Aviation Development and Planning, China Academy of Civil Aviation Science and Technology, Beijing 100028, China

Received:2022-04-22 Revised:2022-10-20 Accepted:2022-11-07 Online:2022-12-25 Published:2022-12-23
Supported by:
The Jointly Program of National Natural Science Foundation of China and Civil Aviation Administration of China

摘要/Abstract

摘要： 研究空中交通流量的波动特性是设计高效流量管理措施和控制策略的基础，掌握空中交通流量波动特性有利于空域资源配置与流量运行需求之间的均衡匹配。在3种时间粒度上，针对进场航班流量时间序列，一方面从复杂网络整体维度出发，采用有限穿越可视图对时间序列进行建网，利用k-core算法探究航班流量波动特性；另一方面从复杂网络局部维度出发，引入序模体方法，构造有限穿越可视图序模体，利用多元序模体类型转换规律来刻画流量的动态转移模式，进而掌握航班流量波动动态演化规律，为波动模式的预测提供了有效工具。研究结果表明：在有限穿越可视图方法映射得到的网络中，节点所属核阶数可以有效刻画流量波动强度，且与波动强度成正相关关系，即节点所属核阶数越大，波动强度越大，天津机场进场航班流量数据的强波动时段为16:50-17:30；序模体越长，波动特性刻画能力越强，但鉴于受到空中交通混沌特性影响，序模体过长对于流量预测意义不大，推荐使用5节点序模体；波动模式状态转移图在有效刻画流量波动动态演化的同时，也可以计算波动模式的转移概率，3种时间粒度下转移概率分别为12.315%、 13.131%和10.638%，为波动模式的预测提供了有效工具。

关键词: 航空运输, 有限穿越可视图, 序模体, k 阶核, 复杂网络, 航班流量时间序列

Abstract: Studying the fluctuation characteristics of air traffic flow is the basis for designing efficient management and control strategies. Understanding the fluctuation characteristics of air traffic flow is conducive to the balance between airspace resource allocation and demand. In three time granularities, this paper uses the limited penetrable visibility graph method to build the complex network for the time series and explores the fluctuation characteristics of the flight flow with the k-core kernel algorithm from the overall perspective of the complex network, based on the time series of incoming flight traffic. The motif method is used to construct the sequence motif of the limited penetrable visibility graph, and the type conversion law of multivariate sequence motif is used to describe the dynamic transfer mode of traffic flow, so as to grasp the regular pattern of the dynamic evolution of flight traffic fluctuation. The method provides an effective tool for the prediction of fluctuation mode. It is found that: (1) In the network mapped by the limited penetrable visibility graph method, the k-core order of the node can effectively describe the fluctuation intensity of traffic flow, and has a positive correlative relationship with the fluctuation intensity. It means that the greater the k-core order of the node, the greater the fluctuation intensity, and the strong fluctuation period of arrival flight flow data of Tianjin airport is 16:50-17:30; (2) Although the longer the motif is, the more dynamic the motif can be, and the longer motif has no significance for the prediction of traffic flow under the influence of the chaotic characteristics of air traffic flow. For the research on the dynamic evolution of air traffic flow fluctuation, a 5-node motif is recommended. (3) The state transition diagram of fluctuation patterns can not only effectively describe the dynamic evolution of flow fluctuation, and it can also calculate the transition probability of fluctuation patterns. The transition probabilities under the three time granularities are 12.315%, 13.131%, and 10.638%, respectively. The state transition diagram provides an effective tool for the prediction of fluctuation patterns.

Key words: air transportation, limited penetrable visibility graph, motif, k-core, complex network, flight flow time series

中图分类号:

张勰, 肖恩媛, 刘宏志, 赵嶷飞, 王梦琦. 基于有限穿越可视图的进场航班流量波动特性研究[J]. 交通运输系统工程与信息, 2022, 22(6): 244-257.

ZHANG Xie, XIAO En-yuan, LIU Hong-zhi, ZHAO Yi-fei, WANG Meng-qi. Fluctuation Characteristics of Arrival Flight Flow Based on Limited Penetrable Visibility Graph[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(6): 244-257.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: http://www.tseit.org.cn/CN/10.16097/j.cnki.1009-6744.2022.06.025

http://www.tseit.org.cn/CN/Y2022/V22/I6/244

参考文献

[1] MURCA M C R, HANSMAN R J, LI L, et al. Flight trajectory data analytics for characterization of air traffic flows: A comparative analysis of terminal area operations between New York, Hong Kong and Sao Paulo[J]. Transportation Research Part C: Emerging Technologies, 2018, 97(12): 324-347.

[2] SIDIROPOULOS S, HAN K, MAJUMDAR A, et al. Robust identification of air traffic flow patterns in Metroplex terminal areas under demand uncertainty[J]. Transportation Research Part C: Emerging Technologies, 2017, 75: 212-227.

[3] 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. [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.]

[4] 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. [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.]

[5] 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. [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(07): 325-338.]

[6] 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. [WANG C, ZHU M, ZHAO Y D. Air traffic flow prediction model based on improved addingweighted one-rank local-rejion method[J]. Journal of Southwest Jiaotong University, 2018, 53(1): 206-213.]

[7] WEINREICH I, RICKERT H, LUKASCHEWITSCH M. Wavelet- based time series prediction for air traffic data [C]//Truchetet F. Wavelet Applications in Industrial Processing: Proceedings of SPIE Vol. 5266. Bellingham, SPIE, 2004: 238-248.

[8] 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. [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.]

[9] 张弦, 王宏力. 基于支持向量经验模态分解的故障率时间序列预测[J]. 航空学报, 2011, 32(3): 480- 487. [ZHANG X, WANG H L. Failure rate time series prediction based on support vector empirical mode decomposition[J]. Acta Aeronautica et Astronautica Sinica, 2011, 32(3): 480-487.]

[10] 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.

[11] 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, 2020:124485.

[12] 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.]

[13] 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.]

[14] 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. [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.]

[15] LIU J, WANG H T, JIANG R F, 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 and Its Applications, 2016, 460: 174-187.

[16] 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. IEEE, 2016.

[17] REN W, JIN N. Sequential limited penetrable visibilitygraph motifs[J]. Nonlinear Dynamics, 2020, 99(3): 2399- 2408.

[18] IACOVACCI J, LACASA L. Sequential visibility- graph motifs[J]. PHYSICAL REVIEW E, 2016, 93(4): 042309.

[19] 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.

基于有限穿越可视图的进场航班流量波动特性研究

Fluctuation Characteristics of Arrival Flight Flow Based on Limited Penetrable Visibility Graph

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	王红勇, 张加豪, 许平. 基于复杂性的多扇区移交策略优化[J]. 交通运输系统工程与信息, 2023, 23(1): 284-294.
[2]	冯芬玲, 蔡明旭, 贾俊杰. 基于多层复杂网络的中欧班列运输网络关键节点识别研究[J]. 交通运输系统工程与信息, 2022, 22(6): 191-200.
[3]	王亭, 张永, 周明妮, 鲁文博, 李世豪. 融合网络拓扑结构特征与客流量的城市轨道交通关键节点识别研究[J]. 交通运输系统工程与信息, 2022, 22(6): 201-211.
[4]	温瑞英, 刘文瀚, 王红勇. 编队飞行中基于危险区域的后机最优位置研究[J]. 交通运输系统工程与信息, 2022, 22(5): 300-308.
[5]	唐小卫, 陈祯, 张生润, 丁叶. 繁忙机场机坪空间构型对航班离港滑行时间的影响[J]. 交通运输系统工程与信息, 2022, 22(5): 309-317.
[6]	韩博, 邓志强, 于敬磊, 石依琳, 于剑. 碳达峰目标下中国民航CO₂与NO_x减排协同效益分析[J]. 交通运输系统工程与信息, 2022, 22(4): 53-62.
[7]	张仕杰, 唐涛, 刘金涛, 李辰岭. 基于复杂网络的列车辅助驾驶危险致因传播模型[J]. 交通运输系统工程与信息, 2022, 22(4): 129-136.
[8]	蒋丽, 杨露, 梁昌勇, 董骏峰. 基于无人机的高层住宅最后“一百米”配送优化[J]. 交通运输系统工程与信息, 2022, 22(4): 236-245.
[9]	钟罡, 励瑾, 张晓玮, 张洪海. 物流无人机对地风险评估方法研究[J]. 交通运输系统工程与信息, 2022, 22(4): 246-254.
[10]	马剑, 廖伟屹, 陈娟, 王巧, 唐铁桥, 原志路. 基于乘客超越行为建模的航空登机效率分析[J]. 交通运输系统工程与信息, 2022, 22(4): 255-267.
[11]	张洪海, 冯棣坤, 张晓玮, 刘皞, 钟罡, 张连东. 城市物流无人机起降点布局规划研究[J]. 交通运输系统工程与信息, 2022, 22(3): 207-214.
[12]	张培文, 杜福民, 汪瑜. 基于链路预测的中小机场连接机制研究[J]. 交通运输系统工程与信息, 2022, 22(2): 298-304.
[13]	王红勇, 郭宇鹏. 基于航空器自主运行的空中交通复杂性建模[J]. 交通运输系统工程与信息, 2022, 22(2): 305-312.
[14]	张洪海, 张连东, 刘皞, 钟罡. 城市低空物流无人机航迹规划模型研究[J]. 交通运输系统工程与信息, 2022, 22(1): 256-264.
[15]	范博松, 邵春福. 城市轨道交通系统动态风险模态分析建模[J]. 交通运输系统工程与信息, 2021, 21(6): 203-209.