地理学报

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中国航空网络空间结构的复杂性

王姣娥1,2, 莫辉辉1,3, 金凤君1   

  1. 1. 中国科学院地理科学与资源研究所, 北京 100101;
    2. 美国路易斯安那州立大学, 巴吞鲁日 70803;
    3. 中国交通运输协会, 北京 100053
  • 收稿日期:2009-02-10 修回日期:2009-06-18 出版日期:2009-08-20 发布日期:2009-09-21
  • 作者简介:王姣娥 (1981-), 女, 博士后, 湖南涟源人, 主要从事交通运输、城市交通、区域发展等研究。 E-mail: jiaoewang@163.com
  • 基金资助:

    国家自然科学重点基金项目(40635026);; 中国博士后科学基金项目(20080440065)

Spatial Structural Characteristics of Chinese Aviation Network Based on Complex Network Theory

WANG Jiao'e1, 2,  MO Huihui1, 3,  JIN Fengjun1   

  1. 1. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;
    2. Louisiana State University, Baton Rouge, LA 70803, USA;
    3. China Communications and Transportation Association, Beijing 100053, China
  • Received:2009-02-10 Revised:2009-06-18 Online:2009-08-20 Published:2009-09-21
  • Supported by:

    Key Project of National Natural Science Foundation of China, No.40635026; Project of China Postdoctoral Science Foundation, No.20080440065

摘要:

航空运输是现代交通运输的重要组成部分,以机场和航线构建的网络是其提供运输服务的空间载体。基于复杂网络理论,借助度分布、平均路径长度、簇系数、度度相关性、簇度相关性等指标对以城市为节点的中国航空网络空间结构进行分析,发现其度累计概率分布表现为具有置信度较高的指数分布,且具有较小的平均路径长度(2.23)和较大的簇系数(0.69),整体结构呈现"小世界网络"的特点。由于中国航空网络规模较小,且新建机场倾向于直接与最高级枢纽机场建立航线联系,不利于区域枢纽机场的形成。因而除顶层结构(北京—上海—广州)外,中国航空网络的其它层级结构并不十分明显。从节点的度、簇系数、可达性等指标及其相关性分析,中国航空网络空间结构特征差异明显,表现出较强的集聚性,且可达性与城市体系上层结构在空间分布上较为吻合。随着中国航空运输需求的快速增加,未来航空网络在市场经济的推动下,将进一步表现出向具有"无标度"特征的"小世界网络"演变的趋势,航空网络的空间结构将日趋复杂化。

关键词: 航空网络, 空间结构, 复杂性, 小世界网络, 无标度网络, 中国

Abstract:

Today, air transport plays an important role in transportation systems, especially for medium and long distance. Based on complex network theory, Chinese aviation network is abstracted as a set of edges (linkages) connecting a set of nodes (cities), and 144 cities and 1018 air routes were chosen for this research operated from winter 2007 to spring 2008. Then, this paper analyzes the spatial structure of Chinese aviation network using indices of degree distribution, the average path length, the clustering coefficient, degree-degree correlation, and clustering-degree correlation. Degree is the number of edges that a node shares with others, and thus symbolizes the importance of the node in the network. Degree distribution is used to reflect the distribution function of degree and donates the statistical characteristics of a network. Average path length is defined as the average number of edges along the shortest paths for all possible node-pairs in the network and it is a measurememt of the efficiency of transportation network. The clustering coefficient of a node is the ratio of actual edges to maximal edges between nodes which are directly connected with the node. The clustering coefficient of the whole network is the average of all individuals. As for an airport network, the average path length and the clustering coefficient are the two most important parameters reflecting network properties and configurations. The results show that Chinese aviation network has a relatively small average path length of 2.23 and a relatively large cluster coefficient of 0.69. More importantly, the accumulative degree distribution follows an exponential expression, with significance of 0.977. Therefore, Chinese aviation network shows the characteristic of a "Small World" network. Due to most new airports' preferences for direct connections with the three top-level national hubs: Beijing, Shanghai, and Guangzhou, the network hierarchy has no distinct difference with the exception of the top one. Also, the spatial distribution of Chinese aviation network is imbalanced according to the indices of degree, clustering coefficient and accessibility index. Besides, the correlation coefficients of these indices mentioned above are analyzed. The results show that there are negative degree correlation, nonlinear clustering-degree correlation, positive accessibility-degree correlation, and slightly positive clustering-accessibility correlation. In conclusion, with the rapid development of air transport demand driven by the market economy, Chinese aviation network will further evolve to a combined model of "Scale Free" and "Small World" networks, and its spatial structure will be more complex.

Key words: aviation network, spatial structure, complexity, small world network, scale free network, China