基于复杂网络的空中交通特征与延误传播分析

doi:10.7527/S1000-6893.2017.721473

电子电气工程与控制

本期目录 | 过刊浏览 | 高级检索

| 后一篇

基于复杂网络的空中交通特征与延误传播分析

武喜萍¹, 杨红雨¹, 韩松臣²

1. 四川大学视觉合成图形图像技术国防重点学科实验室, 成都 610065;
2. 四川大学空天科学与工程学院, 成都 610065

收稿日期:2017-05-25 修回日期:2017-06-26 出版日期:2017-11-30 发布日期:2017-06-26
通讯作者: 韩松臣 E-mail:hansongchen@nuaa.edu.cn
基金资助:
国家自然科学基金（71573184）

Analysis of properties and delay propagation of air traffic based on complex network

WU Xiping¹, YANG Hongyu¹, HAN Songchen²

1. National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu 610065, China;
2. School of Aeronautics & Astronautics, Sichuan University, Chengdu 610065, China

Received:2017-05-25 Revised:2017-06-26 Online:2017-11-30 Published:2017-06-26
Supported by:
National Natural Science Foundation of China(71573184)

摘要/Abstract

摘要：

空中交通流量管理主要集中在尾随间隔、空中等待等局部战术空中交通流量调配，对空中交通流量整体运行规律、延误传播和整体解决的研究较少。航空运输网络是一个复杂系统，从复杂网络理论角度对航空运输网络进行研究很有必要。首先，分析了空中交通流量网络的度、度分布、权值、权分布、流量、容量、延误等静态特征；然后，提出了空中交通流量网络整体效能评估方法，使用选择性攻击和随机攻击方法分析了空中交通流量网络的抗毁性，基于负荷容量级联失效模型和病毒传播模型与空中交通延误传播的相似性建立了空中交通延误传播模型，最后，使用实际运行数据验证了空中交通流量网络的特征与延误传播模型的有效性，该研究可以为大范围空中流量管理提供决策支持。

关键词: 空中交通管制, 空中交通流量管理, 延误传播, 复杂网络, 网络特性

Abstract:

Current air traffic flow management focuses mainly on making air traffic flow control measures, such as miles in trail, air holding, but lacks strategic consideration of the global operation law of the air traffic flow, air traffic flow delays formation and propagation, and holistic resolution programs. The air transportation network is a complicated network system, and it is necessary to study air traffic flow using complex network theories. The topological structure of the air traffic flow network is analyzed, such as degree, degree distribution, weight, weight distribution, flow, capacity and delay. The method for evaluating air traffic flow network efficiency is then proposed, and the invulnerability of the air traffic flow network is analyzed using selective attack and random attack. The model for air traffic delay propagation is established based on the similarity between the load capacity cascading failure model and the virus propagation model, and air traffic delay propagation. The research on the air traffic flow network structure and delay propagation are validated using a large number of actual data. The research can provide decision support for large scale air traffic flow management.

Key words: air traffic control, air traffic flow management, delay propagation, complex network, network property

中图分类号:

V355

武喜萍, 杨红雨, 韩松臣. 基于复杂网络的空中交通特征与延误传播分析[J]. 航空学报, 2017, 38(S1): 721473-721473.

WU Xiping, YANG Hongyu, HAN Songchen. Analysis of properties and delay propagation of air traffic based on complex network[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2017, 38(S1): 721473-721473.

参考文献

[1] PIEN K, HAN K, SHANG W, et al. Robustness analysis of the European air traffic network[J]. Transportmetrica A Transport, 2015, 11(9): 772-792.
[2] CHEUNG D P, GUNES M H. A complex network analysis of the united states at transportation[C]//Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. Piscataway, NJ: IEEE Press, 2012: 699-701.
[3] MEHTA V, PATEL F, GLINA Y, et al. Characterization of traffic and structure in the U.S. airport network[C]//2012 Conference on Intelligent Data Understanding, 2012: 124-129.
[4] XU Z, HARRISS R. Exploring the structure of the U.S. intercity passenger air transportation network: A weighted complex network approach[J]. GeoJournal, 2008, 73(2): 87-102.
[5] GUIDA M, MARIA F. Topology of the Italian airport network: A scale-free small-world network with a fractal structure[J]. Chaos, Solitons and Fractals, 2007, 31(3): 527-536.
[6] BAGLER G. Analysis of the airport network of India as a complex weighted network[J]. Physica A: Statistical Mechanics and its Applications, 2008, 387(12): 2972-2980.
[7] ZHANG J, CAO X B, DU W B, et al. Evolution of Chinese airport network[J]. Physica A, 2010, 389(18): 3922-3931.
[8] CAI K Q, ZHANG J, DU W B.et al. Analysis of the Chinese air route network as a complex network[J]. Chinese Physics B, 2012, 21(2): 028903.
[9] PALEARI S, REDONDI R,MALIGHETTI P. A comparative study of airport connectivity in China, Europe and US: Which network provides the best service to passengers?[J].Transportation Research Part E,2010,46(2):198-210.
[10] SCHAUM A, JAQUEZ R B. Estimating the state probability distribution for epidemic spreading in complex networks[J]. Applied Mathematics and Computation, 2016, 291:197-206.
[11] XU D G, XU X Y, YANG C H, et al. Spreading dynamics and synchronization behavior of periodic diseases on complex networks[J]. Physica A, 2017, 466: 544-551.
[12] DADLANIA A, KUMAR M S, MURUGAN S, et al. System dynamics of a refined epidemic model for infection propagation over complex networks[J]. IEEE System Journal, 2016, 10(4): 1316-1325.
[13] XU W, NI W, ZHENG K F, et al. Virus propagation modeling and convergence analysis in large-scale networks[J]. IEEE Transactions on Information Forensics and Security, 2016, 11(10): 2241-2254.
[14] LIU W P, LIU C, YANG Z, et al. Modeling the propagation of mobile malware on complex networks[J]. Communications in Nonlinear Science and Numerical Simulation, 2016, 37: 249-264.
[15] GAN C Q, YANG X F, LIU W P, et al. Propagation of computer virus both across the internet and external computers: A complex-network approach[J].Communications in Nonlinear Science and Numerical Simulation, 2014, 19(8): 2785-2792.
[16] ZHANG C M, HUANG H T. Optimal control strategy for a novel computer virus propagation model on scale-free networks[J]. Physical A, 2016, 451: 251-265.
[17] CHEN D B, GAO H, LU L Y, et al. Identifying influential nodes in large-scale directed networks: The role of clustering[J]. Plos One, 2013, 8(10): 1-10.
[18] ZHANG W, BAI S Y, JIN R. The model of microblog message diffusion based on complex social network[J]. International Journal of Modern Physics B, 2014, 28(20):1450136.
[19] ZHU H M, YIN X C, MA J, et al. Identifying the main paths of information diffusion in online social networks[J]. Physica A, 2016, 452: 320-328.
[20] ZHANG B, HALANG W A. Understanding the cascading failures in Indian power grids with complex networks theory[J]. Physica A, 2013, 392(15): 3273-3280.
[21] CHANG L, WU Z G. Performance and reliability of electrical power grids under cascading failures[J]. Electrical Power and Energy Systems, 2011, 33(8): 1410-1419.
[22] BILIS E I, KROGER W, NAN C. Performance of electric power systems under physical malicious attacks[J]. IEEE Systems Journal, 2013, 7(4): 854-865.
[23] LATORA V,MARCHIORI M. Efficient behavior of smallworld networks[J]. Physical Review Lettes, 2001, 87(19): 198701.

E-mail：hkxb@buaa.edu.cn

关于我们

期刊社服务

专业学科

封面文章

友情链接

主管单位：中国科学技术协会主办单位：中国航空学会北京航空航天大学

基于复杂网络的空中交通特征与延误传播分析

Analysis of properties and delay propagation of air traffic based on complex network

PDF (PC)

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

本文评价

[1]	王春政, 胡明华, 杨磊, 赵征, 单晶. 基于Agent模型的机场网络延误预测[J]. 航空学报, 2021, 42(7): 324604-324604.
[2]	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): 324300-324300.
[3]	刘继新, 江灏, 董欣放, 兰思洁, 王浩哲. 基于空中交通密度的进场航班动态协同排序方法[J]. 航空学报, 2020, 41(7): 323717-323717.
[4]	王莉莉, 王航臣. 突发事件下大规模空中交通流量管理的组合优化模型[J]. 航空学报, 2019, 40(8): 322898-322898.
[5]	黄洋, 汤俊, 老松杨. 基于复杂网络的无人机飞行冲突解脱算法[J]. 航空学报, 2018, 39(12): 322222-322222.
[6]	齐雁楠, 高经东. 空域扇区网络级联失效抗毁性及优化策略[J]. 航空学报, 2018, 39(12): 322579-322579.
[7]	卜建, 刘银鑫, 王艳军. 空中交通管制员的眼动行为与疲劳关系[J]. 航空学报, 2017, 38(S1): 721525-721525.
[8]	徐肖豪, 李善梅. 空中交通拥挤的识别与预测方法研究[J]. 航空学报, 2015, 36(8): 2753-2763.
[9]	马园园, 胡明华, 张洪海, 尹嘉男, 吴凡. 多机场终端区进场航班协同排序方法[J]. 航空学报, 2015, 36(7): 2279-2290.
[10]	尹嘉男, 胡明华, 张洪海, 马园园, 吴凡. 独立离场模式下多跑道时空资源优化调度方法[J]. 航空学报, 2015, 36(5): 1574-1584.
[11]	尹嘉男, 胡明华, 张洪海, 马园园. 多跑道协同运行模式优化方法[J]. 航空学报, 2014, 35(3): 795-806.
[12]	尹嘉男, 胡明华, 彭瑛, 唐勇, 马园园. 相关进近模式下多跑道时空资源优化调度方法[J]. 航空学报, 2014, 35(11): 3064-3073.
[13]	蔡林, 张玲, 杨善水, 王莉. 大型飞机供配电系统可靠性评估与分析[J]. 航空学报, 2011, 32(8): 1488-1496.
[14]	王洪, 刘昌忠, 汪学刚, 吴宏刚. 一种多点定位的目标位置精确解算方法[J]. 航空学报, 2011, 32(7): 1269-1274.
[15]	刘方勤, 胡明华, 张颖. 基于航路耦合容量的协同多航路资源分配[J]. 航空学报, 2011, 32(4): 672-684.