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

doi:10.16265/j.cnki.issn1003-3033.2024.10.0082

Abstract

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

CLC Number:

X949

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.

Figures/Tables 10

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Table 1

Power-law distribution function of temporal indegree and outdegree of each network layer

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	$P (k O i 7) = 0 . 12 O 7 k - 0 . 867 O 7 + 0.003 31 O 7$	$P (k I i 7) = 0 . 12 I 7 k - 0.859 2 I 7 + 0.003 62 I 7$

Table 1

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Table 2

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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