ESTIMATION OF AUSTRALIA’S OUTBOUND AIRLINE PASSENGER DEMAND USING AN ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM

Abstract

: This study has proposed and empirically tested an adaptive neuro-fuzzy inference system (ANFIS) model for predicting Australia’s outbound international airline passenger demand. The model was developed using eleven input parameters of world GDP, world population, world air fare yields, world jet fuel prices, outbound flights from Australia, Australia’s unemployment numbers, Australian’s (AUD/USD) foreign exchange rate, Australia’s outbound tourist expenditure and four dummy variables. The model was constructed using annual data from 1994 to 2019. The hybrid learning algorithm and the subtractive clustering partition method were used to generate the optimum ANFIS models. The performance of the model was measured using five error measures: coefficient of determination (R2-value), root mean square errors (RMSE), mean absolute errors (MAE) and the mean absolute percentage error (MAPE). The results found that the mean absolute percentage error (MAPE) for the overall data set of the model was 3.60%. The R2-value was around 0.9886, demonstrating that the ANFIS is an efficient model for predicting Australia’s outbound airline passenger demand.