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The Journal of Supercomputing

Erschienen in:

22.09.2020

Forecasting air passenger traffic flow based on the two-phase learning model

verfasst von: Xinfang Wu, Yong Xiang, Gang Mao, Mingqian Du, Xiuqing Yang, Xinzhi Zhou

Erschienen in: The Journal of Supercomputing | Ausgabe 5/2021

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Abstract

The future airports will head toward a highly intelligent direction, like the unmanned check-in services, while the scale and resources allocation of the ground service are tightly related to the air passenger flow. Therefore, forecasting passenger flow accurately will affect the development of future airports and the optimization of service of civil airlines significantly. As a kind of time series, air passenger flow is influenced by multiple factors, particularly, the stochastic part of seasonality, trend and volatility. These will ultimately affect the accuracy of the prediction. Therefore, this paper introduces a prediction model based on a two-phase learning framework. In phase one, various predictors cope with different features of time series in parallel and the prediction results are integrated in phase two. Furthermore, this paper has compared principal error indicators with actual data and results show that the two-phase learning model performs better than current fusion models and owns stable performance.

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Titel: Forecasting air passenger traffic flow based on the two-phase learning model
verfasst von: Xinfang Wu
Yong Xiang
Gang Mao
Mingqian Du
Xiuqing Yang
Xinzhi Zhou
Publikationsdatum: 22.09.2020
Verlag: Springer US
Erschienen in: The Journal of Supercomputing / Ausgabe 5/2021
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-020-03428-2

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