Recurrence in the evolution of air transport networks

Abstract Changes in air transport networks over time may be induced by competition among carriers, changes in regulations on airline industry, and socioeconomic events such as terrorist attacks and epidemic outbreaks. Such network changes may reflect corporate strategies of each carrier. In the pres...

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Main Authors: Kashin Sugishita, Naoki Masuda
Format: Article
Language:English
Published: Nature Publishing Group 2021-03-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-021-84337-z
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spelling doaj-a3d7a1a688f449c2a50eba23ce34b95a2021-03-11T12:22:35ZengNature Publishing GroupScientific Reports2045-23222021-03-0111111510.1038/s41598-021-84337-zRecurrence in the evolution of air transport networksKashin Sugishita0Naoki Masuda1Department of Mathematics, State University of New York at BuffaloDepartment of Mathematics, State University of New York at BuffaloAbstract Changes in air transport networks over time may be induced by competition among carriers, changes in regulations on airline industry, and socioeconomic events such as terrorist attacks and epidemic outbreaks. Such network changes may reflect corporate strategies of each carrier. In the present study, we propose a framework for analyzing evolution patterns in temporal networks in discrete time from the viewpoint of recurrence. Recurrence implies that the network structure returns to one relatively close to that in the past. We applied the proposed methods to four major carriers in the US from 1987 to 2019. We found that the carriers were different in terms of the autocorrelation, strength of periodicity, and changes in these quantities across decades. We also found that the network structure of the individual carriers abruptly changes from time to time. Such a network change reflects changes in their operation at their hub airports rather than famous socioeconomic events that look closely related to airline industry. The proposed methods are expected to be useful for revealing, for example, evolution of airline alliances and responses to natural disasters or infectious diseases, as well as characterizing evolution of social, biological, and other networks over time.https://doi.org/10.1038/s41598-021-84337-z
collection DOAJ
language English
format Article
sources DOAJ
author Kashin Sugishita
Naoki Masuda
spellingShingle Kashin Sugishita
Naoki Masuda
Recurrence in the evolution of air transport networks
Scientific Reports
author_facet Kashin Sugishita
Naoki Masuda
author_sort Kashin Sugishita
title Recurrence in the evolution of air transport networks
title_short Recurrence in the evolution of air transport networks
title_full Recurrence in the evolution of air transport networks
title_fullStr Recurrence in the evolution of air transport networks
title_full_unstemmed Recurrence in the evolution of air transport networks
title_sort recurrence in the evolution of air transport networks
publisher Nature Publishing Group
series Scientific Reports
issn 2045-2322
publishDate 2021-03-01
description Abstract Changes in air transport networks over time may be induced by competition among carriers, changes in regulations on airline industry, and socioeconomic events such as terrorist attacks and epidemic outbreaks. Such network changes may reflect corporate strategies of each carrier. In the present study, we propose a framework for analyzing evolution patterns in temporal networks in discrete time from the viewpoint of recurrence. Recurrence implies that the network structure returns to one relatively close to that in the past. We applied the proposed methods to four major carriers in the US from 1987 to 2019. We found that the carriers were different in terms of the autocorrelation, strength of periodicity, and changes in these quantities across decades. We also found that the network structure of the individual carriers abruptly changes from time to time. Such a network change reflects changes in their operation at their hub airports rather than famous socioeconomic events that look closely related to airline industry. The proposed methods are expected to be useful for revealing, for example, evolution of airline alliances and responses to natural disasters or infectious diseases, as well as characterizing evolution of social, biological, and other networks over time.
url https://doi.org/10.1038/s41598-021-84337-z
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