The structural evolution of the Chinese aviation network during and after the pandemic: A machine learning-based approach

IF 6.3 2区 工程技术 Q1 ECONOMICS
Ziyu Cui , Hanjiang Dong , Kun Wang , Jiehong Qiu , Xiaowen Fu
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引用次数: 0

Abstract

To identify the evolution pattern of the Chinese aviation network before, during, and after the pandemic, we develop a machine learning-based framework to analyze the network's development dynamics. By integrating link prediction algorithms into this framework, we quantify the contributions of 11 topological features driving structural changes. Utilizing aviation passenger flow data from China from 2014 to 2024, we identify important topological features that reveal the impact of the COVID-19 pandemic on the air network evolution. The empirical findings yield the following insights: (1) Targeted investments in core hub airports should be prioritized, given their critical role in maintaining network connectivity and facilitating rapid recovery during disruptions. (2) Airlines should strategically optimize shared connectivity and resource allocation to maintain critical routes and network resilience during times of resource constraints caused by the pandemic. (3) To control possible cascading effects caused by disruptions on international routes, secondary hubs and regional routes can be promoted. This would stabilize domestic connectivity and enhance the resilience of the aviation network. (4) Post-pandemic, the diversity-driven topological features become more prominent, suggesting airlines' plan of enhancing network robustness. Policymakers should promote the development of secondary hubs and new routes, thereby improving the aviation network's resilience and reducing excessive concentration to core hubs. These findings provide practical insights for balancing centralization, regional development, and network diversification, contributing to a resilient and adaptive aviation network capable of withstanding future disruptions.
大流行期间和之后中国航空网络的结构演变:基于机器学习的方法
为了确定大流行之前、期间和之后中国航空网络的演变模式,我们开发了一个基于机器学习的框架来分析网络的发展动态。通过将链接预测算法集成到该框架中,我们量化了驱动结构变化的11个拓扑特征的贡献。利用2014 - 2024年中国航空客流数据,我们确定了揭示COVID-19大流行对航空网络演变影响的重要拓扑特征。(1)考虑到核心枢纽机场在保持网络连通性和促进中断期间的快速恢复方面的关键作用,应优先考虑对核心枢纽机场的有针对性投资。(2)航空公司应战略性地优化共享连接和资源分配,在疫情造成的资源紧张时期保持关键航线和网络弹性。(3)为控制国际航线中断可能带来的连锁效应,可促进二级枢纽和区域航线的发展。这将稳定国内连通性,增强航空网络的弹性。(4)大流行后,多样性驱动的拓扑特征更加突出,表明航空公司增强网络鲁棒性的计划。政策制定者应促进二级枢纽和新航线的发展,从而提高航空网络的弹性,减少对核心枢纽的过度集中。这些发现为平衡集中化、区域发展和网络多样化提供了实际见解,有助于建立一个能够承受未来干扰的弹性和适应性航空网络。
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来源期刊
Transport Policy
Transport Policy Multiple-
CiteScore
12.10
自引率
10.30%
发文量
282
期刊介绍: Transport Policy is an international journal aimed at bridging the gap between theory and practice in transport. Its subject areas reflect the concerns of policymakers in government, industry, voluntary organisations and the public at large, providing independent, original and rigorous analysis to understand how policy decisions have been taken, monitor their effects, and suggest how they may be improved. The journal treats the transport sector comprehensively, and in the context of other sectors including energy, housing, industry and planning. All modes are covered: land, sea and air; road and rail; public and private; motorised and non-motorised; passenger and freight.
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