The influence of the connection pattern of airline network on flight delay distribution in China and its changes in recent years

doi:10.3969/j.issn.1004-9479.2023.04.2021513

Abstract

Abstract:

Point-to-point airlines through hub airports constitutes the trunk network system, which plays a key role in the whole air traffic organization. Based on the panel data of 2012 and 2018, this paper constructs a series of indicators set, aiming to explain the impact of airline network on the spatio-temporal distribution of flight delays and forecast its change trend with a seven-year comparison. The results are as follows: ①Airline network is the external basis of spatio-temporal distribution of flight delays, and the cumulative effect and the peak effect of delays are caused by the two components of airlines and airports, respectively. The high delay rate of hub airports cannot be completely attributed to the poor airport construction and air traffic control, which is closely related to the airline network, including the strength and structure of network connection. ②The degree of airline connection in China is more than the degree of aviation demand, and its oversaturation state is obvious and mainly exists between the composite hub airports. The intra-day delay frequency changes from a low value and increases continuously until the end. The overlap of peak hours forms delay contagion and spread, and produces the cumulative effect of flight delay. ③The high node degree and intermediary center of hub airports lead to the low degree of agglomeration and average path length index of the overall route network. The central advantages and bridging functions of hub airports lead to an oligarchic structure of network connection, resulting in the top effect of flight delay. ④The pattern of China's airline network is relatively fixed in the past seven years, and it is still facing great challenges to reduce flight delays. Optimizing air traffic functions and implementing flow regulation are an important direction of airspace reform.

Key words: airline network, connection pattern, flight delays, China, hub airports

摘要：

城市对航线贯通枢纽机场构成干线网络系统，在空中交通组织中发挥关键作用，使用2012年和2018年城市对航线数据和相关航班延误数据构建系列指示指标，旨在阐释航线网络联系模式对航班延误分布的影响，进而以研究期间的比较分析来展望其变化趋势。研究结论如下：①航线网络是影响航班延误的外部性基础因素，我国枢纽机场高延误率不能完全归结为机场规模和空管水平，其与航线网络密切相关，分别由网络联系强度和网络联系结构两个方面引发了延误累积效应和延误顶端效应。②我国航线网络联系强度大多超出航空需求度，日内延误频率由低值向高值变化且持续向后端挤压，高峰时段叠置形成延误传染扩散，产生航班延误累积效应。③枢纽机场节点度和中介中心度过高，整体航线网络集聚度和平均路径长度指数偏低，枢纽机场中心优势和桥接作用使网络联系呈现寡头结构，产生航班延误顶端效应。④研究期间我国航线网络形态较为固定，缓解航班延误仍面临巨大挑战，优化空中交通功能实施流量调控是重要改革方向。

关键词: 航线网络, 联系模式, 航班延误, 中国, 枢纽机场

Yinuo ZHANG, Yaqing DONG, Zi LU, Xinru DU, Jin YANG, Yuhan WANG. The influence of the connection pattern of airline network on flight delay distribution in China and its changes in recent years[J]. World Regional Studies, 2023, 32(4): 96-108.

张一诺, 董雅晴, 路紫, 杜欣儒, 杨锦, 王雨晗. 中国航线网络联系模式对航班延误分布的影响及其近年变化[J]. 世界地理研究, 2023, 32(4): 96-108.

Figures/Tables 6

Fig.1 Flight delay rate of 12 hub airports from 2012 to 2018

Fig.2 Spatial pattern of point-point network and distribution of flight delays in air corridors in 2012 and 2018

Fig.3 Rank of point-to-point airline saturation in 2012 and 2018

Fig.4 Daily planned flights and delayed flights in air corridors

Tab.1 Single factor detection results and independent test of four indicators in 2012 and 2018

机场延误率	2012年				2018年
机场延误率	$K$	$C$	$B$	$L$	$K$	$C$	$B$	$L$
PEK	0.426^**	0.757^***	0.603^*	0.774^*	0.841^***	0.754^**	0.314^***	0.517^***
PVG	0.256^*	0.458^**	0.168^**	0.448^***	0.922^***	0.067^***	0.156^**	0.158^**
SHA	0.102	0.241^**	0.096^**	0.133	0.245	0.118^*	0.562^***	0.361^***
CAN	0.242	0.111	0.126^*	0.215^***	0.120	0.322^***	0.142^*	0.211^**
CKG	0.793^***	0.760^*	0.883^***	0.868^**	0.854^***	0.889^*	0.853^*	0.557^*
CTU	0.374^*	0.227^**	0.443^*	0.647^**	0.502^**	0.541^**	0.587^***	0.153
WUH	0.796^***	0.258^*	0.418^***	0.148^***	0.872^***	0.367^**	0.256	0.358^**
CGO	0.062	0.071^***	0.196	0.133	0.045	0.101^**	0.062^***	0.061^*
SHE	0.042	0.411^*	0.126	0.115	0.510^**	0.409^**	0.112^*	0.246^**
XIY	0.805^***	0.734^*	0.612^**	0.716^*	0.874^***	0.651^*	0.157^**	0.462^**
KMG	0.426^**	0.707^**	0.572^*	0.749^*	0.847^***	0.657^**	0.627^***	0.568^***
URC	0.476^**	0.747^***	0.103^*	0.263^**	0.342	0.453^**	0.146	0.563^**
探测力系数	0.720^***	0.391	0.670^*	0.280	0.690^**	0.340	0.550^*	0.260
平均影响	0.790^**	0.570^*	0.416^**	0.369^**	0.840^***	0.470^*	0.652^***	0.378^***
整网影响	0.772^***	0.554^**	0.165^*	0.495^**	0.778^***	0.617^**	0.587^**	0.177^*

Tab.1 Single factor detection results and independent test of four indicators in 2012 and 2018

机场延误率	2012年				2018年
机场延误率	$K$	$C$	$B$	$L$	$K$	$C$	$B$	$L$
PEK	0.426^**	0.757^***	0.603^*	0.774^*	0.841^***	0.754^**	0.314^***	0.517^***
PVG	0.256^*	0.458^**	0.168^**	0.448^***	0.922^***	0.067^***	0.156^**	0.158^**
SHA	0.102	0.241^**	0.096^**	0.133	0.245	0.118^*	0.562^***	0.361^***
CAN	0.242	0.111	0.126^*	0.215^***	0.120	0.322^***	0.142^*	0.211^**
CKG	0.793^***	0.760^*	0.883^***	0.868^**	0.854^***	0.889^*	0.853^*	0.557^*
CTU	0.374^*	0.227^**	0.443^*	0.647^**	0.502^**	0.541^**	0.587^***	0.153
WUH	0.796^***	0.258^*	0.418^***	0.148^***	0.872^***	0.367^**	0.256	0.358^**
CGO	0.062	0.071^***	0.196	0.133	0.045	0.101^**	0.062^***	0.061^*
SHE	0.042	0.411^*	0.126	0.115	0.510^**	0.409^**	0.112^*	0.246^**
XIY	0.805^***	0.734^*	0.612^**	0.716^*	0.874^***	0.651^*	0.157^**	0.462^**
KMG	0.426^**	0.707^**	0.572^*	0.749^*	0.847^***	0.657^**	0.627^***	0.568^***
URC	0.476^**	0.747^***	0.103^*	0.263^**	0.342	0.453^**	0.146	0.563^**
探测力系数	0.720^***	0.391	0.670^*	0.280	0.690^**	0.340	0.550^*	0.260
平均影响	0.790^**	0.570^*	0.416^**	0.369^**	0.840^***	0.470^*	0.652^***	0.378^***
整网影响	0.772^***	0.554^**	0.165^*	0.495^**	0.778^***	0.617^**	0.587^**	0.177^*

Fig.5 Superposition of the index of the connection structure between point-point network in 2012 and 2018

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