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Global air transport complex network : multi-scale analysis
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Guo, Weisi, Toader, B., Feier, R., Mosquera, Guillem, Ying, F., Oh, S., Williams, M. and Krupp, A. (2019) Global air transport complex network : multi-scale analysis. SN Applied Sciences, 1 . 680. doi:10.1007/s42452-019-0702-2 ISSN 2523-3963.
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Official URL: https://doi.org/10.1007/s42452-019-0702-2
Abstract
Almost half of the world's population is carried by airlines each year, and understanding this mode of transport is important from economic and scientific perspectives. In recent years, the increasing availability of data has led to complex network and agent interaction models which attempt to gain better understanding of the air transport network and develop forecasts. In this case study paper, we review existing research on two key approaches, namely: (i) a top-down multi-scale network science approach, and (ii) a bottom-up entropy-maximization interaction network approach. Using simple socioeconomic indicators, we were able to construct a very accurate interaction model that can predict traffic volume, and the model can forward estimate the impact of population growth or fuel cost. Using network science approaches, we were able to identify community structures and relate them to economic outputs. We also saw how hubs evolved over time to become more influential. Looking into the future, using random graph theory, it seems that reduced flight cost will lead to increased hub influence. The disseminated knowledge in this case study paper will provide both academics and industry practitioners with steps forward to co-explore the interesting research landscape.
| Item Type: | Journal Article | ||||||||
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| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering Faculty of Science, Engineering and Medicine > Science > Mathematics |
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| Journal or Publication Title: | SN Applied Sciences | ||||||||
| Publisher: | Springer | ||||||||
| ISSN: | 2523-3963 | ||||||||
| Official Date: | July 2019 | ||||||||
| Dates: |
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| Volume: | 1 | ||||||||
| Article Number: | 680 | ||||||||
| DOI: | 10.1007/s42452-019-0702-2 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Reuse Statement (publisher, data, author rights): | This is a post-peer-review, pre-copyedit version of an article published in SN Applied Sciences. The final authenticated version is available online at: https://doi.org/10.1007/s42452-019-0702-2 | ||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||
| Description: | Engineering: Data Science, Big Data and Applied Deep Learning: From Science to Applications |
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| Date of first compliant deposit: | 3 June 2019 | ||||||||
| Date of first compliant Open Access: | 8 June 2020 | ||||||||
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