Application of an ANFIS to Estimate Kansai International Airport’s International Air Passenger Demand

Abstract

This study presents an Adaptive Network Based Inference System (ANFIS) model to forecast international passenger demand at Osaka’s Kansai International Airport. The study covered the period 1994 to 2018. The study used nine determinants of air travel demand and three dummy variables as input variables. The results reveal that the model successfully forecasts Kansai International Airport’s international passenger demand. The coefficient of determination (R2) was high, being around 0.9776%. The overall MAPE of Kansai International Airport’s international air passenger demand model was 7.40%.

Keywords

• Adaptive neuro-fuzzy inference system Airline Passengers
• Airport
• International passengers
• Passenger forecasting

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