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

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