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不断增长的空中交通需求与有限的空域容量之间的不平衡,导致空中交通出现了难以解决的延误问题.由于空中交通与复杂的空中运输系统相互关联,延误问题会在这些系统中被放大并扩散,导致出现被称为延误传播的突发性行为.理解延误传播动力学是现代空中交通管理的重要内容.在本研究中,我们提出了一个有关延误传播动力学的复杂网络观点.我们利用时空网络对空中交通情况进行建模,建模的节点为机场.为了建立节点之间的动态性边缘,我们提出了一种延误传播方法,并将其应用于假设的空中交通时间表.在构建时空网络的基础上,我们提出了三个度量指标,即强度、严重程度和速度来衡量延误传播的动力学.为了验证所提出的方法是否有效,我们以东南亚地区(SAR)和美国的国内航班为例进行了研究.研究结果表明,美国空中交通中受延误传播影响的航班数量和延误传播的总数量分别是SAR的5倍和10倍.研究进一步表明,美国空中交通的延误传播速度比SAR的快8倍.延误传播动力学模型显示,在SAR大约有6个枢纽机场存在显著的延误传播,而美国大约有16个.本工作为研究空中交通延误的发展过程提供了一个有力的工具.“,”Intractable delays occur in air traffic due to the imbalance between ever-increasing air traffic demand and limited airspace capacity. As air traffic is associated with complex air transport systems, delays can be magnified and propagated throughout these systems, resulting in the emergent behavior known as delay propagation. An understanding of delay propagation dynamics is pertinent to modern air traffic manage-ment. In this work, we present a complex network perspective of delay propagation dynamics. Specifically, we model air traffic scenarios using spatial–temporal networks with airports as the nodes. To establish the dynamic edges between the nodes, we develop a delay propagation method and apply it to a given set of air traffic schedules. Based on the constructed spatial-temporal networks, we suggest three metrics—magnitude, severity, and speed—to gauge delay propagation dynamics. To validate the effectiveness of the proposed method, we carry out case studies on domestic flights in the Southeastern Asia region (SAR) and the United States. Experiments demonstrate that the propagation magnitude in terms of the number of flights affected by delay propagation and the amount of propagated delays for the US traffic are respectively five and ten times those of the SAR. Experiments further reveal that the propagation speed for US traffic is eight times faster than that of the SAR. The delay propagation dynamics reveal that about six hub airports in the SAR have significant propagated delays, while the sit-uation in the United States is considerably worse, with a corresponding number of around 16. This work provides a potent tool for tracing the evolution of air traffic delays.