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探究机场网络延误传播因果关系及获取因果时延效应对于剖析机场网络延误传播机理，提出机场网络延误应对措施具有重要意义。为科学、准确地挖掘机场网络延误传播因果关系，本文运用卷积神经网络的方法，提出了一种基于注意力机制的深度学习框架。该框架主要分为两个部分：基于注意力机制的深度可分离扩张因果卷积神经网络的因果关系探究和基于置换重要性方法的因果关系验证，并提出延误传播因果时延指数的概念，用以表征机场间延误传播所需要的时间步长（本文中的1个时间步长等于1h），进而构建了机场网络延误传播因果时延效应网络图。为进一步验证所提方法的有效性，选取我国2019至2020年夏秋和冬春航季的国内离港航班运行数据进行实例分析，实验结果表明，我国机场网络中存在广泛的延误传播因果关系，且夏秋航季中的因果关系多于冬春航季。在延误传播中，机场的重要程度与其规模并不完全一致，大多数延误传播因果关系中的“因”机场主要为我国中小型机场，应重点关注该类机场的延误治理能力提升。此外，针对延误传播因果时延效应，我国夏秋（冬春）航季机场对之间延误传播的平均时延指数约为4.5(5.6)个时间步长，这表明夏秋（冬春）航季中“因”机场的延误将在4.5(5.6)小时内传播至“果”机场，“果”机场可根据时延指数及时调整机场运行管理措施以应对“因”机场带来的延误，防止大面积航班延误的发生。综上，本研究可为航司、机场、管制等部门在减少延误、提高民航运行安全与效率方面提供相应的决策支持。
Abstract：Exploring the causal relationship of the delay propagation in airport networks and obtaining the causal delay effect is significant in the analysis of the mechanism of the delay propagation in airport networks. This study proposes countermeasures for airport network delays. In order to scientifically and accurately explore the causal relationship of the delay propagation of airport networks,this study proposes a deep learning framework based on an attention mechanism, using convolutional neural networks. This framework is divided into two main components: the causal relationship exploration of depthwise separable attention-based dilated causal convolutional networks and the causality verification based on the permutation importance method. The concept of the delay propagation causal delay index is proposed to characterize the time steps required for delay propagation between airports. In this study, one time step corresponds to 1 h. A causal delay effect network diagram of airport network delay propagation is then constructed. To further verify the effectiveness of the proposed method, operation data from domestic outbound flights in China during the summer, autumn,winter, and spring seasons of 2019 to 2020 are analyzed. The experimental results show that there is a wide range of delay propagation causality in China's airport network and that the causal relationship in the summer and autumn seasons is more than that in the winter and spring seasons. Moreover,the importance of airports in delay propagation is not consistent with its scale, as most of the“causal”airports in the causal relationship of delay propagation are small and medium-sized airports in China. Hence, attention should be paid to the improvement of the delay management capacities of such airports. In addition, owing to the causal delay effect of delay propagation, the average delay index of delay propagation between airports in China's summer and autumn(winter and spring) seasons is approximately 4.5(5.6) time steps, which indicates that the delay of the“causal”airports in the summer and autumn(winter and spring) seasons will be transmitted to the“influenced”airports in 4.5(5.6) time steps and that the“influenced”airports can adjust their airport operation management measures in time, according to the delay index, to deal with the delay caused by the“causal”airports to thereby prevent the occurrence of large-scale flight delays. In summary, the results of this study can provide decision-making support for airlines, airports, air control, and other departments to reduce delays and improve the safety and efficiency of civil aviation operations.
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基本信息:
DOI：10.19961/j.cnki.1672-4747.2024.05.008
中图分类号:V35;TP18
引用信息:
[1]李千千,田勇,万莉莉,等.基于深度可分离注意力网络的机场网络因果时延关系探究[J].交通运输工程与信息学报,2024,22(03):80-92.DOI:10.19961/j.cnki.1672-4747.2024.05.008.
基金信息:
江苏省青年基金项目(BK20230892);; 江苏省双创博士人才项目(JSSCBS20220212);; 2024年江苏省研究生科研与实践创新计划项目（KYCX24＿0600）;; 2024年南京航空航天大学博士学位论文创新与创优基金项目（BCXJ24-16）
2024-04-10
2024-04-10
2024-04-10