Abstract

In this paper, we develop a route-traffic-based method for detecting community structures in airline networks. Our model is both an application and an extension of the Clauset-Newman-Moore (CNM) modularity maximization algorithm, in that we apply the CNM algorithm to large airline networks, and take both route distance and passenger volumes into account. Therefore, the relationships between airports are defined not only based on the topological structure of the network but also by a traffic-driven indicator. To illustrate our model, two case studies are presented: American Airlines and Southwest Airlines. Results show that the model is effective in exploring the characteristics of the network connections, including the detection of the most influential nodes and communities on the formation of different network structures. This information is important from an airline operation pattern perspective to identify the vulnerability of networks.

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The different versions of the original document can be found in:

http://downloads.hindawi.com/journals/jat/2019/3032015.xml,
http://dx.doi.org/10.1155/2019/3032015 under the license http://creativecommons.org/licenses/by/4.0
https://doaj.org/toc/0197-6729,
https://doaj.org/toc/2042-3195 under the license http://creativecommons.org/licenses/by/4.0/
http://hdl.handle.net/1854/LU-8659540,
http://dx.doi.org/10.1155/2019/3032015,
https://biblio.ugent.be/publication/8659540/file/8659541
http://downloads.hindawi.com/journals/jat/2019/3032015.pdf,
https://biblio.ugent.be/publication/8659540,
https://biblio.ugent.be/publication/8659540/file/8659541.pdf,
https://academic.microsoft.com/#/detail/2997422508
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Published on 01/01/2019

Volume 2019, 2019
DOI: 10.1155/2019/3032015
Licence: Other

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