MODELLING AUSTRALIA'S OUTBOUND PASSENGER AIR TRAVEL DEMAND USING AN ARTIFICIAL NEURAL NETWORK APPROACH.

This paper focuses on predicting Australia's outbound international airline passenger demand using an artificial neural network (ANN) modelling method. The modelling in the study was based on annual data for the period 1993 to 2016. The model was developed using the input parameters of world GDP, world population growth, world jet fuel prices, world air fares (proxy for air travel cost), Australia's tourism attractiveness, outbound flights, Australia's unemployment levels, the Australian and United States foreign exchange rate and three dummy variables (Sydney Olympics, 9/11 and the 2006 Commonwealth Games). The artificial neural network (ANN) used multi-layer perceptron (MLP) architecture that compromised a multi-layer feed-forward network and the sigmoid and linear functions were used as activation functions with the feed forward-back propagation algorithm. The ANN was applied during training, testing and validation and had 8 inputs, 1 neurons in the hidden layers and 1 neuron in the output layer. The data was randomly divided into three data sets; training, testing and model validation. The best-fit model was selected according to four goodness-of-fit measures: mean absolute error (MAE), mean square error (MSE), root mean square errors (RMSE), AND mean absolute percentage errors (MAPE). The highest R-value obtained from the ANN model is 0.99733, demonstrating that the ANN provided a high predictive capability. [ABSTRACT FROM AUTHOR]