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Predicting Model for Air Transport Demand under Uncertainties Based on Particle Filter

Author

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  • Bin Chen

    (Civil Aviation College, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
    China Civil Aviation Engineering Consulting Co., Ltd., Beijing 100621, China)

  • Jin Wu

    (Civil Aviation College, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)

Abstract

The outbreak of the COVID-19 has brought about huge economic loss and civil aviation industries all over the world have suffered severe damage. An effective method is urgently needed to accurately predict air-transport demand under the influences of such accidental factors. This paper proposes a novel predicting framework for the air-transport demand considering the uncertainties caused by accidental factors including regional wars, climatic anomalies, and virus outbreaks. By employing a seasonal autoregressive integrated moving average (sARIMA) model as the basic model, a particle filter (PF)-based sARIMA-pf model is proposed. The applicability of adapting the high-order sARIMA model as the state transition model in a PF framework is shown and proven to be effective. The proposed method has the advantage of coping with short-term prediction with known uncertainties. By conducting case studies on the prediction of air passenger traffic volume in China, the sARIMA-pf model showed better performance than the sARIMA model and improved the accuracy by 49.29% and 44.96% under the conventional and pandemic scenarios, respectively, when using the root mean square error (RMSE) as the indicator.

Suggested Citation

  • Bin Chen & Jin Wu, 2022. "Predicting Model for Air Transport Demand under Uncertainties Based on Particle Filter," Sustainability, MDPI, vol. 14(24), pages 1-13, December.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:24:p:16694-:d:1002062
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    References listed on IDEAS

    as
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    3. Jin, Feng & Li, Yongwu & Sun, Shaolong & Li, Hongtao, 2020. "Forecasting air passenger demand with a new hybrid ensemble approach," Journal of Air Transport Management, Elsevier, vol. 83(C).
    4. Meng, Huixing & Geng, Mengyao & Xing, Jinduo & Zio, Enrico, 2022. "A hybrid method for prognostics of lithium-ion batteries capacity considering regeneration phenomena," Energy, Elsevier, vol. 261(PB).
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