An open-access modeled passenger flow matrix for the global air network in 2010
An open-access modeled passenger flow matrix for the global air network in 2010
The expanding global air network provides rapid and wide-reaching connections accelerating both domestic and international travel. To understand human movement patterns on the network and their socioeconomic, environmental and epidemiological implications, information on passenger flow is required. However, comprehensive data on global passenger flow remain difficult and expensive to obtain, prompting researchers to rely on scheduled flight seat capacity data or simple models of flow. This study describes the construction of an open-access modeled passenger flow matrix for all airports with a host city-population of more than 100,000 and within two transfers of air travel from various publicly available air travel datasets. Data on network characteristics, city population, and local area GDP amongst others are utilized as covariates in a spatial interaction framework to predict the air transportation flows between airports. Training datasets based on information from various transportation organizations in the United States, Canada and the European Union were assembled. A log-linear model controlling the random effects on origin, destination and the airport hierarchy was then built to predict passenger flows on the network, and compared to the results produced using previously published models. Validation analyses showed that the model presented here produced improved predictive power and accuracy compared to previously published models, yielding the highest successful prediction rate at the global scale. Based on this model, passenger flows between 1,491 airports on 644,406 unique routes were estimated in the prediction dataset. The airport node characteristics and estimated passenger flows are freely available as part of the Vector-Borne Disease Airline Importation Risk (VBD-Air) project (see link below).
e64317
Preis, Tobias
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Huang, Zhuojie
07e288b7-51b3-414a-82b7-28d83b114be6
Wu, Xiao
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Garcia, Andres J.
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Fik, Timothy J.
06248534-2752-4bd8-b1a1-55ed3e353a87
Tatem, Andrew J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
15 May 2013
Preis, Tobias
e9ed823c-bd74-4b7a-a809-85acb7513b00
Huang, Zhuojie
07e288b7-51b3-414a-82b7-28d83b114be6
Wu, Xiao
0fe3c946-45ac-49b8-90c7-335435642bb3
Garcia, Andres J.
66af41c0-7fd4-4f11-b5bc-5333b4c04824
Fik, Timothy J.
06248534-2752-4bd8-b1a1-55ed3e353a87
Tatem, Andrew J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Preis, Tobias, Huang, Zhuojie, Wu, Xiao, Garcia, Andres J., Fik, Timothy J. and Tatem, Andrew J.
(2013)
An open-access modeled passenger flow matrix for the global air network in 2010.
PLoS ONE, 8 (5), .
(doi:10.1371/journal.pone.0064317).
Abstract
The expanding global air network provides rapid and wide-reaching connections accelerating both domestic and international travel. To understand human movement patterns on the network and their socioeconomic, environmental and epidemiological implications, information on passenger flow is required. However, comprehensive data on global passenger flow remain difficult and expensive to obtain, prompting researchers to rely on scheduled flight seat capacity data or simple models of flow. This study describes the construction of an open-access modeled passenger flow matrix for all airports with a host city-population of more than 100,000 and within two transfers of air travel from various publicly available air travel datasets. Data on network characteristics, city population, and local area GDP amongst others are utilized as covariates in a spatial interaction framework to predict the air transportation flows between airports. Training datasets based on information from various transportation organizations in the United States, Canada and the European Union were assembled. A log-linear model controlling the random effects on origin, destination and the airport hierarchy was then built to predict passenger flows on the network, and compared to the results produced using previously published models. Validation analyses showed that the model presented here produced improved predictive power and accuracy compared to previously published models, yielding the highest successful prediction rate at the global scale. Based on this model, passenger flows between 1,491 airports on 644,406 unique routes were estimated in the prediction dataset. The airport node characteristics and estimated passenger flows are freely available as part of the Vector-Borne Disease Airline Importation Risk (VBD-Air) project (see link below).
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Published date: 15 May 2013
Organisations:
Global Env Change & Earth Observation, WorldPop, Geography & Environment, PHEW – S (Spatial analysis and modelling), Population, Health & Wellbeing (PHeW)
Identifiers
Local EPrints ID: 352822
URI: http://eprints.soton.ac.uk/id/eprint/352822
ISSN: 1932-6203
PURE UUID: 7219dedd-88a8-49f7-bd2a-5aa5d847abe1
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Date deposited: 21 May 2013 12:15
Last modified: 15 Mar 2024 03:43
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Contributors
Author:
Tobias Preis
Author:
Zhuojie Huang
Author:
Xiao Wu
Author:
Andres J. Garcia
Author:
Timothy J. Fik
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