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Modeling monthly flows of global air travel passengers: an open-access data resource

Modeling monthly flows of global air travel passengers: an open-access data resource
Modeling monthly flows of global air travel passengers: an open-access data resource
The global flow of air travel passengers varies over time and space, but analyses of these dynamics and their integration into applications in the fields of economics, epidemiology and migration, for example, have been constrained by a lack of data, given that air passenger flow data are often difficult and expensive to obtain. Here, these dynamics are modeled at a monthly scale to provide an open-access spatio-temporally resolved data source for research purposes (www.vbd-air.com/data). By refining an annual-scale model of Huang et al. (2013), we developed a set of Poisson regression models to predict monthly passenger volumes between directly connected airports during 2010. The models not only performed well in the United States with an overall accuracy of 93%, but also showed a reasonable confidence in estimating air passenger volumes in other regions of the world. Using the model outcomes, this research studied the spatio-temporal dynamics in the world airline network (WAN) that previous analyses were unable to capture. Findings on the monthly variation of WAN offer new knowledge for dynamic planning and strategy design to address global issues, such as disease pandemics and climate change.
worldwide airline network (WAN), air passenger flow, monthly dynamics, spatio-temporal modeling
0966-6923
52-60
Mao, Liang
7b34bb41-e91f-4bd6-a16b-290a59c3905d
Wu, Xiao
0fe3c946-45ac-49b8-90c7-335435642bb3
Huang, Zhuojie
07e288b7-51b3-414a-82b7-28d83b114be6
Tatem, Andrew J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Mao, Liang
7b34bb41-e91f-4bd6-a16b-290a59c3905d
Wu, Xiao
0fe3c946-45ac-49b8-90c7-335435642bb3
Huang, Zhuojie
07e288b7-51b3-414a-82b7-28d83b114be6
Tatem, Andrew J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e

Mao, Liang, Wu, Xiao, Huang, Zhuojie and Tatem, Andrew J. (2015) Modeling monthly flows of global air travel passengers: an open-access data resource. Journal of Transport Geography, 48, 52-60. (doi:10.1016/j.jtrangeo.2015.08.017).

Record type: Article

Abstract

The global flow of air travel passengers varies over time and space, but analyses of these dynamics and their integration into applications in the fields of economics, epidemiology and migration, for example, have been constrained by a lack of data, given that air passenger flow data are often difficult and expensive to obtain. Here, these dynamics are modeled at a monthly scale to provide an open-access spatio-temporally resolved data source for research purposes (www.vbd-air.com/data). By refining an annual-scale model of Huang et al. (2013), we developed a set of Poisson regression models to predict monthly passenger volumes between directly connected airports during 2010. The models not only performed well in the United States with an overall accuracy of 93%, but also showed a reasonable confidence in estimating air passenger volumes in other regions of the world. Using the model outcomes, this research studied the spatio-temporal dynamics in the world airline network (WAN) that previous analyses were unable to capture. Findings on the monthly variation of WAN offer new knowledge for dynamic planning and strategy design to address global issues, such as disease pandemics and climate change.

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More information

Accepted/In Press date: 15 August 2015
e-pub ahead of print date: 1 September 2015
Published date: October 2015
Keywords: worldwide airline network (WAN), air passenger flow, monthly dynamics, spatio-temporal modeling
Organisations: Global Env Change & Earth Observation, WorldPop, Geography & Environment, Population, Health & Wellbeing (PHeW)

Identifiers

Local EPrints ID: 381131
URI: https://eprints.soton.ac.uk/id/eprint/381131
ISSN: 0966-6923
PURE UUID: b4943eaa-c203-4402-81d5-7b5e425b831b
ORCID for Andrew J. Tatem: ORCID iD orcid.org/0000-0002-7270-941X

Catalogue record

Date deposited: 23 Sep 2015 16:35
Last modified: 06 Jun 2018 12:28

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Contributors

Author: Liang Mao
Author: Xiao Wu
Author: Zhuojie Huang
Author: Andrew J. Tatem ORCID iD

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