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|Title:||A spatial-temporal network perspective for the propagation dynamics of air traffic delays||Authors:||Cai, Qing
Duong, Vu N.
|Keywords:||Engineering::Aeronautical engineering||Issue Date:||2021||Source:||Cai, Q., Alam, S. & Duong, V. N. (2021). A spatial-temporal network perspective for the propagation dynamics of air traffic delays. Engineering. https://dx.doi.org/10.1016/j.eng.2020.05.027||Journal:||Engineering||Abstract:||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 management. 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 10 times those of the SAR. Experiments further reveal that the propagation speed for U.S. 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 situation 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.||URI:||https://hdl.handle.net/10356/147384||ISSN:||NA||DOI:||10.1016/j.eng.2020.05.027||Rights:||© 2021 The Authors. Published by Elsevier Ltd on behalf of Chinese Academy of Engineering and Higher Education Press Limited Company. All rights reserved.||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||ATMRI Journal Articles|
Updated on May 6, 2021
Updated on May 6, 2021
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