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

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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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Acknowledgements

The authors would like to thank the Bureau of Transportation Statistics of the United States Department of Transportation for providing air passenger traffic flow data, and pay special thanks to Sulistyowati et al. as their work provides useful information to this paper.

Funding

This work is supported by the National Natural Science Foundation of China (Grant No. U1933123).

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Authors and Affiliations

  1. College of Electronics and Information Engineering, Sichuan University, Chengdu, Sichuan, China

    Xinfang Wu & Xinzhi Zhou

  2. The Second Research Institute of CAAC, Chengdu, Sichuan, China

    Yong Xiang, Gang Mao, Mingqian Du & Xiuqing Yang

  3. Civil Aviation Logistics Technology Company Limited, Chengdu, Sichuan, China

    Yong Xiang, Gang Mao, Mingqian Du & Xiuqing Yang

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  1. Xinfang Wu
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Correspondence to Xinzhi Zhou.

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Wu, X., Xiang, Y., Mao, G. et al. Forecasting air passenger traffic flow based on the two-phase learning model. J Supercomput 77, 4221–4243 (2021). https://doi.org/10.1007/s11227-020-03428-2

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