Please wait a minute...
文章检索
复杂系统与复杂性科学  2020, Vol. 17 Issue (2): 39-46    DOI: 10.13306/j.1672-3813.2020.02.005
  本期目录 | 过刊浏览 | 高级检索 |
中国航线网络结构的多层性分析
徐开俊, 吴佳益, 杨泳, 梁磊
中国民用航空飞行学院飞行技术学院,四川 广汉 618307
Multilayered Analysis of Chinese Airline Network Structure
XU Kaijun1, WU Jiayi2, YANG Yong3, LIANG Lei4
Department of Flight Technology, Civil Aviation Flight University of China, Guanghan 618307, China
全文: PDF(2687 KB)  
输出: BibTeX | EndNote (RIS)      
摘要 为了更深入研究中国航线网络的拓扑特性中的细节问题,运用复杂网络理论,将每个航空公司定义为网络中的一个层并建立多层网络模型,仿真逐层合并过程中网络特征参数的演变,探讨中国航空多层网络(CAMN)的拓扑新特性。结果表明,CAMN总度值呈现幂律分布,总度值高的五大机场其度值在各航空公司间分布均匀;中国航空多层网络在聚合过程中都呈现无标度网络特性,而“小世界网络”特性仅在较多数量层的网络聚合时明显,且成规模的航空公司合作使网络的运输效率更高;中国航空聚合网络的“小世界”现象主要是由大中型航空公司对应层引起,大中型航空公司网络的运输效率比廉价航空公司网络高,但同质性更低。
服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
徐开俊
吴佳益
杨泳
梁磊
徐开俊
吴佳益
杨泳
梁磊
关键词 中国航线网络复杂网络多层网络拓扑特性    
Abstract:To further study the details of the topological characteristics of Chinese airline network, this work explored the new topological features of China Aviation Multi-layer Network (CAMN) based on complex network theory. We defined each airline as a layer in network and established a multi-layer network by simulating the evolution of network characteristic parameters in the layer-by-layer process. The results indicate that overlapping degree value of CAMN obeys a power-law distribution and five airports with highest overlapping value are uniformly distributed among airlines. Meanwhile, CAMN presents the characteristics of scale-free networks in aggregation, but characteristics of the small world network can only be obviously observed in aggregation of multi-layer networks. Further, the cooperation of large-scale airlines makes network more efficient. The phenomenon of small world in Chinese aviation aggregation network is mainly caused by the corresponding layer of large and medium-sized airlines. The transportation efficiency of large and medium-sized airline networks is higher than that of low-cost airlines, but the homogeneity is poorer.
Key wordsChinese airline network    complex network    multi-layer network    Topological features
     出版日期: 2020-06-24
:  U8  
  N94  
基金资助:国家自然科学基金民航联合基金(U1533127);中国民用航空飞行学院创新团队支持计划(JG201915);中国民航飞行学院研究生创新项目(X20182)
通讯作者: 吴佳益(1991),男,四川内江人,硕士研究生,主要研究方向为航空复杂网络。   
作者简介: 徐开俊(1981),男,四川成都人,博士后,教授,主要研究方向为情感认知及航空网络。
引用本文:   
徐开俊, 吴佳益, 杨泳, 梁磊. 中国航线网络结构的多层性分析[J]. 复杂系统与复杂性科学, 2020, 17(2): 39-46.
XU Kaijun, WU Jiayi, YANG Yong, LIANG Lei. Multilayered Analysis of Chinese Airline Network Structure[J]. Complex Systems and Complexity Science, 2020, 17(2): 39-46.
链接本文:  
https://fzkx.qdu.edu.cn/CN/10.13306/j.1672-3813.2020.02.005      或      https://fzkx.qdu.edu.cn/CN/Y2020/V17/I2/39
[1]R. Guimerá, Amaral L A N. Modeling the world-wide airport network[J]. European Physical Journal B, 2004, 38(2):381385.
[2]Gautreau A, Barrat A, Barthelemy M. Microdynamics in stationary complex networks[J]. Proceedings of the National Academy of Sciences, 2009, 106(22):88478852.
[3]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.
[4]刘宏鲲, 周涛. 中国城市航空网络的实证研究与分析[J]. 物理学报, 2007, 56(1):106112.
Liu Hongkun, Zhou Tao. Empirical study of Chinese city airline network[J]. Acta Physica Sinica, 2007, 56(1):106112.
[5]Bagler G. Analysis of the airport network of India as a complex weighted network[J]. Physica A Statistical Mechanics & Its Applications, 2008, 387(12):29722980.
[6]Boccaletti S, Bianconi G, Criado R, et al. The structure and dynamics of multilayer networks[J]. Physics Reports, 2014,544(1):1122.
[7]Gómez-Gardees J, Reinares I, Arenas A, et al. Evolution of cooperation in multiplex networks[J]. Scientific Reports, 2012, 6(620):16.
[8]Du W B, Zhou X L, Lordan O, et al. Analysis of the Chinese airline network as multi-layer networks[J]. Transportation Research Part E: Logistics and Transportation Review, 2016, 89:108116.
[9]Lordan O, Sallan J M. Analyzing the multilevel structure of the European airport network[J]. Chinese Journal of Aeronautics, 2017, 30(2):554560.
[10] Cardillo A, Gómez-Gardenes J, Zanin M, et al. Emergence of network features from multiplexity[J]. Scientific Reports, 2013, 3:01344.
[11] Klophaus R, Lordan O. Codesharing network vulnerability of global airline alliances[J]. Transportation Research Part A: Policy and Practice, 2018, 111:110.
[12] Dai L, Derudder B, Liu X. The evolving structure of the Southeast Asian air transport network through the lens of complex networks, 1979–2012[J]. Journal of Transport Geography, 2018, 68:6777.
[13] 孙圣波, 朱保平, 杨晓光. 基于三角模体的社团发现算法[J]. 南京理工大学学报(自然科学版), 2017, 41(1):3540.
Sun Shengbo, Zhu Baoping, Yang Xiaoguang. Community discovery algorithm based on triangular motifs[J]. Journal of Nanjing University of Science and Technology, 2017, 41(1):3540.
[14] 李明高, 杜鹏, 朱宇婷. 城市轨道交通换乘节点与网络运行效率关系研究[J]. 交通运输系统工程与信息, 2015, 15(2):4853.
LI Minggao, DU Peng, ZHU Yuting. Effect of urban rail transit transfer nodes on network performance[J]. Journal of Transportation Systems Engineering and Information Technology, 2015, 15(2):4853.
[15] Stauffer D. Introduction to Percolation Theory[M]. London:Taylor & Francis, 1985.
[16] Guimerà, R, Díaz-Guilera, A, Vega-Redondo F, et al. Optimal network topologies for local search with congestion[J]. Physical Review Letters, 2002, 89(24):248701.
[17] Motter A E, Lai Y C. Dissipative chaotic scattering[J]. Physical Review E Statistical Nonlinear & Soft Matter Physics, 2002, 65(1/2):015205.
[18] Deng Y. Generalized evidence theory[J]. Applied Intelligence, 2015, 43(3):530543.
[19] Jiang W, Yang Y, Luo Y,et al.Determining basic probability assignment based on the improved similarity measures of generalized fuzzy numbers[J]. International Journal of Computers Communications & Control,2015(3):333347.
[20] Battiston F, Nicosia V, Latora V. Structural measures for multiplex networks[J]. Physical Review E, 2014, 89(3):032804.
[21] 中国民用航空局.2017年民航行业发展统计公报[DB/OL]. [20190210]. http://www.caac.gov.cn/XXGK/XXGK/TJSJ.
Civil Aviation Administration of China. 2017 civil aviation industry development statistics bulletin[DB/OL]. [20190210]. http://www.caac.gov.cn/XXGK/XXGK/TJSJ.
[22] Wu J Y, Yang Y, Xu K J.Comparative analysis of Chinese airway network based on complex network[C]. International Conference on Advanced Cloud and Big Data (CBD).2018.
[23] Boccaletti S, Bianconi G, Criado R, et al. The structure and dynamics of multilayer networks[J]. Physics Reports, 2014, 544(1):1122.
[24] Min B, Yi S D, Lee K M, et al. Network robustness of multiplex networks with interlayer degree correlations[J]. Phys Rev E Stat Nonlin Soft Matter Phys, 2014, 89(4):042811.
[25] Zanin M, Lillo F. Modelling the air transport with complex networks: a short review[J]. The European Physical Journal Special Topics, 2013, 215(1):521.
[26] Guimerá, Amaral L A N. Modeling the world-wide airport network[J]. European Physical Journal B, 2004, 38(2):381385.
[1] 聂廷远, 王艳伟, 聂晶晶, 刘鹏飞. 基于注意力机制和复杂网络的FPGA可布性预测[J]. 复杂系统与复杂性科学, 2026, 23(1): 53-59.
[2] 户佐安, 杨江浩, 邓锦程. 考虑多元变量的世界航空网络综合鲁棒性研究[J]. 复杂系统与复杂性科学, 2026, 23(1): 60-69.
[3] 孙小慧, 刘毅, 米玉梅, 吕凯. 韧性视角下城市地铁与常规公交网络关键站点及线路识别[J]. 复杂系统与复杂性科学, 2026, 23(1): 26-36.
[4] 牟奇锋, 李晓倩. 基于邻接矩阵的复杂网络演化融合迭代方法[J]. 复杂系统与复杂性科学, 2026, 23(1): 79-86.
[5] 孙文静, 余路粉, 潘文林, 蓝春江. 基于节点影响因子和贡献因子的复杂网络重要节点识别[J]. 复杂系统与复杂性科学, 2026, 23(1): 87-95.
[6] 卢新彪, 刘泽诚, 陈贵允, 杨铁流, 高兴. 基于图卷积网络的复杂网络能控性提升方法[J]. 复杂系统与复杂性科学, 2025, 22(4): 24-28.
[7] 周青, 李依函, 陈文冲. “互联网+”企业创新生态系统网络演化分析[J]. 复杂系统与复杂性科学, 2025, 22(4): 1-7.
[8] 章浩淳, 寇博潇, 张泰杰, 唐智慧. 基于Granger Causality的滑坡机理网络客观权值确定方法[J]. 复杂系统与复杂性科学, 2025, 22(4): 63-70.
[9] 韩世翔, 闫光辉, 裴华艳. 复杂网络上双向免疫对传染病传播的影响[J]. 复杂系统与复杂性科学, 2025, 22(4): 55-62.
[10] 张琦, 汪小帆. 复杂网络观点动力学分析与干预若干研究进展[J]. 复杂系统与复杂性科学, 2025, 22(2): 31-44.
[11] 张明磊, 宋玉蓉, 曲鸿博. 基于图注意力机制的复杂网络关键节点识别[J]. 复杂系统与复杂性科学, 2025, 22(2): 113-119.
[12] 陶昭, 侯忠生. 复杂网络的无模型自适应牵制控制[J]. 复杂系统与复杂性科学, 2025, 22(2): 120-127.
[13] 李伟莎, 王淑良, 宋博. 基于强化学习风电并网策略下的韧性分析[J]. 复杂系统与复杂性科学, 2025, 22(2): 128-134.
[14] 张耀波, 张胜, 王雨萱, 熊聪源. 基于K-shell的复杂网络簇生长维数研究[J]. 复杂系统与复杂性科学, 2025, 22(1): 11-17.
[15] 詹秀秀, 叶涛, 刘闯, 刘雪梅. 农产品贸易网络中国家影响力分析与研究[J]. 复杂系统与复杂性科学, 2025, 22(1): 26-32.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed