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

Bin Chen () and Jin Wu
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Bin Chen: Civil Aviation College, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Jin Wu: Civil Aviation College, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China

Sustainability, 2022, vol. 14, issue 24, 1-13

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.

Keywords: predicting model; air-transport-demand forecasting; seasonal autoregressive integrated moving average (sARIMA) model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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