Forecasting Demand for Low Cost Carriers in Australia Using an Artificial Neural Network Approach

Abstract

This study focuses on predicting Australia's low cost carrier passenger demand and revenue passenger kilometres performed (RPKs) using traditional econometric and artificial neural network (ANN) modelling methods. For model development, Australia's real GDP, real GDP per capita, air fares, Australia's population and unemployment, tourism (bed spaces) and 4 dummy variables, utilizing quarterly data obtained between 2002 and 2012, were selected as model parameters. The neural network 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 11 inputs, 9 neurons in the hidden layers and 1 neuron in the output layer. When comparing the predictive accuracy of the two techniques, the ANNs provided the best prediction and showed that the performance of the ANN model was better than that of the traditional multiple linear regression (MLR) approach. The highest R-value for the enplaned passengers ANN was around 0.996 and for the RPKs ANN was round 0.998, respectively.

The Use of an Artificial Neural Network to Predict Australia’s Export Air Cargo Demand

“…Over-fitting can lead to predictions that are beyond the range of the training data (Richter and Weber, 2013). To avoid model over-fitting, the collected data was randomly divided into a 70:15:15 ratio (Garrido et al, 2014;Kunt et al, 2011;Srisaeng et al, 2015). A cross validation process was carried out during the training phase to avoid over-fitting of the proposed model (Efendigil et al, 2009).…”

“…The stopping criterion was the mean square error (MSE) of the estimated demand with respect to the samples belonging to the validation set. The validation set was not used in adapting the weight vectors of the neural estimator, and was therefore able to detect over-fitting in the training phase (Alekseev and Seixas, 2009;Srisaeng et al, 2015).…”

“…For estimating the generalization capacity of the ANN forecasting model, a testing set was also used (Srisaeng et al, 2015). Thus, after the training process was completed, a testing process was applied to ensure the model accuracy was sufficiently reliable.…”

“…The artificial neural network model is characterized by a network of three layers: input, output and hidden layers (Cocchi et al, 2017;Khan et al, 2015;Srisaeng et al, 2015). An ANN consists of a large number of simple processing elements called neurons.…”

“…The ANNs output layer provides the model's predicted values (Kar et al, 2015;Lahmiri and Gagnon, 2015). The number of neurons in the input layer is equal to the number of input variables or independent variables, and the number of output neurons is equal to the number of output variable(s) or dependent variable(s) (Srisaeng et al, 2015;Tiryaki and Aydın, 2014).…”