Volume List  / Volume 8 (1)



DOI: 10.7708/ijtte.2018.8(1).02

8 / 1 / 15-30 Pages


Glenn Baxter - School of Tourism and Hospitality Management, Suan Dusit University, Huahin Campus, Huahin, Prachaup Khiri Khan, Thailand -

Panarat Srisaeng - School of Tourism and Hospitality Management, Suan Dusit University, Huahin Campus, Huahin, Prachaup Khiri Khan, Thailand -


In this paper an Artificial Neural Network (ANN) is proposed for predicting Australia’s annual export air cargo demand. The modelling in the study was based on annual data from 1993 to 2016. The ANN model was developed using the input parameters of world real merchandise exports, world population growth, world jet fuel prices, world air cargo yields (proxy for air cargo costs), outbound flights from Australia, and Australian/United States dollar exchange rate and two dummy variables, which controlled for the strong cyclical fluctuations in air cargo demand which occurred in 2003 and 2015. 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 neuron in the hidden layer 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 0.97844. The results suggest that the ANN model is an efficient tool for predicting Australia’s annual export air cargo demand.

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