基于多层时序网络模型的中国机场网络特征分析

doi:10.16265/j.cnki.issn1003-3033.2024.10.0082

中国安全科学学报 ›› 2024, Vol. 34 ›› Issue (10): 71-79.doi: 10.16265/j.cnki.issn1003-3033.2024.10.0082

基于多层时序网络模型的中国机场网络特征分析

郭九霞¹(), 李虹屹², 魏嵩³, 叶伟⁴, 王超⁵

¹ 中国民用航空飞行学院空中交通管理学院,四川广汉 618307
² 海南机场设施股份有限公司机场建设规划部,海南海口, 570204
³ 民航局运行监控中心飞行计划处,北京 100710
⁴ 中国民用航空飞行学院空管中心,四川广汉 618307
⁵ 中国民航大学空中交通管理学院,天津 300300

收稿日期:2024-04-19 修回日期:2024-07-20 出版日期:2024-12-05
作者简介:
郭九霞 (1981—),女,山西安泽人,博士,教授,主要从事复杂系统韧性、空中交通管理等方面的研究。E-mail:didiyes@163.com。
魏嵩，高级工程师；
叶伟，高级工程师；
王超，教授
基金资助:
四川省社科基金资助(SCJJ23ND186); 民航教育人才类项目(MHJY2023010); 天津市自然科学基金资助(21JCZDJC00780); 中央高校基本科研业务费专项资金(PHD2023-041)

Analysis of Chinese airport network characteristics based on multi-layer temporal network model

GUO Jiuxia¹(), LI Hongyi², WEI Song³, YE Wei⁴, WANG Chao⁵

¹ School of Air Traffic Management, Civil Aviation Flight University of China, Guanghan Sichuan 618307, China
² Airport Construction Planning Department, Hainan Airport Facilities Co., Ltd., Haikou Hainan 570204, China
³ Flight Planning Department, Civil Aviation Administration Operation Monitoring Center, Beijing 100710, China
⁴ Air Traffic Control Center, Civil Aviation Flight University of China, Guanghan Sichuan 618307, China
⁵ School of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China

Received:2024-04-19 Revised:2024-07-20 Published:2024-12-05

摘要/Abstract

摘要：

为克服静态网络模型在分析动态系统时出现网络结构特性反映不全面和数据表达失真的问题,引入时间属性,提出一种基于时序复杂网络模型的中国机场网络(CAN)结构分析方法。通过分析节点时序度、时序度中心性、时序介数中心性和时序接近中心性等网络特征指标,探究中国机场时序网络的结构特征,筛选和排序网络中的重要机场节点,并分析异质节点产生的原因。结果表明: CAN结构呈现出无标度网络特性;CAN的平均时序距离值差距较大,最大值是最小值的6.06倍;乌鲁木齐地窝堡国际机场(ZWWW)、呼和浩特白塔国际机场(ZBHH)的时序介数中心性相对其他指标较高,其在网络中起到重要的中心作用,而深圳宝安国际机场(ZGSZ)相对其他指标具有较高的时序度中心性。

关键词: 时序网络, 中国机场网络(CAN), 多层时序网络模型(MTNM), 网络特征指标, 幂律分布

Abstract:

To address the problem of incomplete reflection of network structure characteristics and data distortion in static network models used in dynamic systems, an airport network structure analysis method based on a temporal complex network model was proposed by introducing time attributes. Network characteristic indicators such as nodal temporal degree, temporal centrality, temporal betweenness centrality, and temporal closeness centrality were analyzed. Then, the structural properties of CAN were investigated. Moreover, crucial airport nodes within the temporal network were identified and ranked, and the reasons for the heterogeneous node generation were analyzed. The results indicated that the temporal network structure of Chinese airports presented scale-free network characteristics. The average temporal distance values of CAN vary significantly with the maximum being 6.06 times the minimum. ZWWW (Urumqi Diwopu International Airport) and ZBHH (Hohhot Baita International Airport) represented relatively higher temporal betweenness centrality than other indicators, playing an important central role in the network. However, ZGSZ (Shenzhen Bao'an International Airport) had a higher temporal degree of centrality than other indicators.

Key words: temporal network, Chinese airport network (CAN), multi-layer temporal network model (MTNM), network characteristics indicator, power distribution

中图分类号:

X949

郭九霞, 李虹屹, 魏嵩, 叶伟, 王超. 基于多层时序网络模型的中国机场网络特征分析[J]. 中国安全科学学报, 2024, 34(10): 71-79.

GUO Jiuxia, LI Hongyi, WEI Song, YE Wei, WANG Chao. Analysis of Chinese airport network characteristics based on multi-layer temporal network model[J]. China Safety Science Journal, 2024, 34(10): 71-79.

图/表 10

图1

图2

图3

表1

各网络层时序入度、出度幂律分布函数

网络层	时序出度幂律分布函数	时序入度幂律分布函数
第1层	$P (k O i 1) = 0.177 7 O 1 k - 0.910 8 O 1 + 0.003 2 O 1$	$P (k I i 1) = 0 . 181 I 1 k - 0 . 928 I 1 + 0.000 29 I 1$
第2层	$P (k O i 2) = 0 . 167 O 2 k - 0 . 978 O 2 + 0.003 66 O 2$	$P (k I i 2) = 0 . 162 I 2 k - 0 . 949 I 2 + 0.003 83 I 2$
第3层	$P (k O i 3) = 0 . 177 O 3 k - 0 . 921 O 3 + 0.003 66 O 3$	$P (k I i 3) = 0 . 104 I 3 k - 0 . 76 I 3 + 0.003 2 I 3$
第4层	$P (k O i 4) = 0 . 15 O 4 k - 0 . 835 O 4 + 0.003 19 O 4$	$P (k I i 4) = 0 . 136 I 6 k - 0 . 887 I 4 + 0.003 55 I 4$
第5层	$P (k O i 5) = 0 . 115 O 5 k - 0 . 807 O 5 + 0.002 9 O 5$	$P (k I i 5) = 0 . 115 I 5 k - 0 . 797 I 5 + 0.003 36 I 5$
第6层	$P (k O i 6) = 0 . 105 O 6 k - 0 . 817 O 6 + 0.003 1 O 6$	$P (k I i 6) = 0 . 124 I 6 k - 0.893 2 I 6 + 0.003 65 I 6$
第7层	$P (k O i 7) = 0 . 12 O 7 k - 0 . 867 O 7 + 0.003 31 O 7$	$P (k I i 7) = 0 . 12 I 7 k - 0.859 2 I 7 + 0.003 62 I 7$

表1

图4

图5

图6

表2

图7

图8

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机场	时序出度中心性	时序出度中心性	时序度中心性
ZGGG	2.480 3	2.476 0	4.956 3
ZSPD (上海浦东)	2.091 7	2.096 1	4.187 8
ZBAA (北京首都)	2.034 9	2.039 3	4.074 2
ZGSZ	1.851 5	1.847 2	3.698 7
ZBAD (北京大兴)	1.821 0	1.838 4	3.659 4
ZPPP	1.777 3	1.777 3	3.554 6
ZUCK (重庆江北)	1.607 0	1.602 6	3.209 6
ZLXY (西安咸阳)	1.545 9	1.541 5	3.087 3
ZUTF (成都天府)	1.541 5	1.541 5	3.083 0
ZSSS (上海虹桥)	1.519 7	1.506 6	3.026 2

基于多层时序网络模型的中国机场网络特征分析

Analysis of Chinese airport network characteristics based on multi-layer temporal network model

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