交通运输系统工程与信息 ›› 2014, Vol. 14 ›› Issue (1): 71-80.

• 系统工程理论与方法 • 上一篇    下一篇

中国航空网络演化过程的复杂性研究

王姣娥*1, 莫辉辉2   

  1. 1. 中国科学院 地理科学与资源研究所, 中国科学院区域可持续发展分析与模拟重点实验室,北京 100101;  2. 中国交通运输协会,北京 100053
  • 收稿日期:2013-07-12 修回日期:2013-08-05 出版日期:2014-02-25 发布日期:2014-07-07
  • 作者简介:王姣娥(1981-),女,湖南涟源人, 副研究员,博士.
  • 基金资助:

    国家自然科学青年基金(41001082); 中国科学院地理科学与资源研究所秉维优秀青年人才基金项目(2011RC201).

Complex Evolution Process of China’s Air Transport Network

WANG Jiao-e1, MO Hui-hui2   

  1. 1. Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China; 2. China Communications and Transportation Association, Beijing 100053, China
  • Received:2013-07-12 Revised:2013-08-05 Online:2014-02-25 Published:2014-07-07

摘要:

基于图论和复杂网络理论的分析方法,本文对1952~2008年中国航空网络演化过程进行了定量剖析.伴随着节点(城市)和航线的增减,中国航空网络表现出异速增长特征.平均路径长度由1952年的5.74逐渐下降到2008年的2.24,网络效率逐步趋于稳定;集聚系数由1952年的0增长到2008年的0.69,呈现向小世界网络演化的趋势,簇度相关性显示度值在平均值(14)以上的上层系统已初步形成层级结构;1952-1962年的网络度分布具有度特征值和长尾分布特征,此后逐渐向具有“无标度”特征的网络演进.中国航空网络的度度相关系数呈现倒U型变化趋势,其原因在于演化受到距离、技术、经济等综合因素的影响.本文长尺度的历史数据一方面验证了当前网络复杂性相关理论研究和实证分析中的不足,同时可为网络演化理论发展提供重要的实证基础.

关键词: 航空运输, 演化过程, 复杂网络, 航空网络, 小世界, 度度相关性

Abstract:

Graph index and complex network methods were used to evaluate the evolution process of China’s air transport network (ATNC) during 1952-2008. The allometric growth was explored in the development history of ATNC, with the fluctuating growth of nodes (cities) and edges (airlines or city-pairs). The average path length in ATNC was reduced from 5.74 in 1952 to 2.24 in 2008, which showed spatiotemporal convergence and increasing efficiency. In contrast, the clustering coefficient rose from 0 to 0.69. Both the average path length and the clustering coefficient indicated a developing trajectory of small-world network. A hierarchical structure was shaped in the upper airport system with degree over 14. Degree distribution showed the long-tail characteristics from 1952 and 1962, and then turned to a scale-free network. The degree-degree correlation shows as an inverse-U pattern, which is affected by complicated factors such as distance, technology, and economic elements. In summary, the paper gives an analysis on the evolution process of ATNC, which supplement the shortage of the complex network theory model and its application in air transport network, and provides an experimental base for establishing theoretical evolution models.

Key words: air transportation, evolution, complex network, air transport network, small-world, degree-degree correlation

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