Volume List  / Volume 7 (4)



DOI: 10.7708/ijtte.2017.7(4).01

7 / 4 / 406 - 423 Pages


Panarat Srisaeng - School of Tourism and Hospitality Management, Suan Dusit University, Huahin Campus, Huahin, Prachup Khiri Khan, Thailand, 77110 -

Glenn Baxter - School of Tourism and Hospitality Management, Suan Dusit University, Huahin Campus, Huahin, Prachup Khiri Khan, Thailand, 77110 -


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.

Download Article

Number of downloads: 1820


Abed, S.Y.; Ba-Fail, A.O.; Jasimuddin, S.M. 2001. An econometric analysis of international air travel demand in Saudi Arabia, Journal of Air Management 7(3): 143-148.


Aderamo, A.J. 2010. Demand for air transport in Nigeria, Journal of Economics 1(1): 23-31.


Akgüngör, A.P.; DoÄŸan, E. 2009. An artificial intelligent approach to traffic accident estimation: model development and application, Transport 24(2): 135–142.


Alekseev, K.P.G.; Seixas, J.M. 2002. Forecasting the air transport demand for passengers with neural modelling, in Proceedings of the VII Brazilian Symposium on Neural Networks (SBRN/02), 11–14 November 2002, Recife, Brazil.


Alekseev, K.P.G.; Seixas, J.M. 2009. A multivariate neural forecasting modeling for air transport – Preprocessed by decomposition: a Brazilian application, Journal of Air Transport Management 15(5): 212–216.


Ba-Fail, A.O. 2004. Applying data mining techniques to forecast number of airline passengers in Saudi Arabia (domestic and international travels), Journal of Air Transportation 9(1): 100-115.


Ba-Fail, A.O.; Abed, S.Y.; Jasimuddin, S.M. 2000. The determinants of domestic air travel demand in the Kingdom of Saudi Arabia, Journal of Air Transportation World Wide 5(2): 72–86.


Bureau of Infrastructure, Transport and Regional Economics. 2017. International airline activity, Statistical Report. Available from internet: https://bitre.gov.au/publications/ongoing/files/International_airline_activity_CY2016.pdf.


Bhadra, D. 2003. Demand for air travel in the United States: bottom-up econometric estimation and implications for forecasts by origin and destination pairs, Journal of Air Transportation 8(2):19–56.


Chen, S.C.; Kuo, S.Y.; Chang, K.W.; Wang, Y.T. 2012. Improving the forecasting accuracy of air passenger and air cargo demand: the application of back-propagation neural networks, Transportation Planning and Technology 35(3): 373-392.


Chew, E.P.; Lee, L.H.; Tan, L.C. 2011. Advances in maritime logistics and supply chain systems. World Scientific Publishing, Singapore. 332 p.


Clark, D.E.; McGibany, J.M.; Myers, A. 2009. The effects of 9/11 on the airline travel industry. In book (ed. Morgan M.J.) The Impact Of 9/11 On Business and Economics: The Business of Terror - The Day That Changed Everything? Palgrave Macmillan, United Kingdom: 76-86.


Claveria, O.; Torra, S. 2014. Forecasting tourism demand to Catalonia: neural networks vs. time series models, Economic Modelling 36: 220-228.


Da Silva, I.N.; Spatti, D.H.; Flauzino, R.A.; Liboni, L.H.B.; dos Reis Alves, S.F. 2017. Artificial neural networks: a practical course. Springer International Publishing, Switzerland. 307 p.


Dulikravich, G.S.; Colaço, M.J. 2015. Hybrid optimization algorithms and hybrid response surfaces. In book (ed. Greiner, D. et al.) Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering Sciences, Springer International Publishing, Switzerland: 19-48.


Efendigil, T.; Önüt, S.; Kahraman, C. 2009. A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models: a comparative analysis, Expert Systems with Applications 36(3): 6697-6707.


Garrido, C.; De Oña, R.; De Oña, J. 2014. Neural networks for analyzing service quality in public transportation, Expert Systems with Applications 41(15): 6830–6838.


Gately, E. 1996. Neural networks for financial forecasting. John Wiley & Sons, Inc, United States. 169 p.


Gonzalez, S. 2000. Neural Networks for Macroeconomic Forecasting: A Complementary Approach to Linear Regression Models, Finance Canada Working Paper 2000-07. Available from internet: http://citeseerx.ist.psu.edu/viewdoc/download?doi=


Grayson, J.; Gardner, S.; Stephens, M. 2015. Building better models with JMP Pro. SAS Institute, Inc, United States. 352 p.


Guo, Z.X.; Wong, W.K. 2013. Fundamentals of artificial intelligence techniques for apparel management applications. In book: (ed. Wong, WK. et al.) Optimizing Decision Making in the Apparel Supply Chain Using Artificial Intelligence (AI): From Production to Retail. Woodhead Publishing, United Kingdom: 13-40.


Hagan, M.T.; Demuth, H.B.; Beale, M.H. 1996. Neural network design, PWS Publishing, Boston. 800 p.


Jiang, D.; Zhang, Y.; Hu, X.; Zeng, Y.; Tan, J.; Shao, D. 2004. Progress in developing an ANN model for air pollution index forecast, Atmospheric Environment 38(40): 7055–7064.


International Air Transport Association. 2008. Air travel demand. IATA Economics Briefing No. 9. Available from Internet: http://www.iata.org/ whatwedo/Documents/economics/air_travel_demand.pdf.


Kale, M.U.; Deshmukh, M.M.; Wadatkar, S.B.; Talokar, A.S. 2016. Constraints in rainfall-runoff using artificial neural network. In book (ed. Panigrahi, B., Goyal, M.R.) Modeling Methods and Practices in Soil and Water Engineering, Apple Academic Press, Inc, Canada: 31-40.


Kalogirou, S.A. 2014. Solar energy engineering: processes and systems. Second Edition, Academic Press, United Kingdom. 840 p.


Kar, P.; Das, A. 2016. Artificial neural networks and learning techniques. In book (ed. Samui, P.) Handbook of Research on Advanced Computational Techniques for Simulation-Based Engineering, IGI Global, United States: 227-251.


Kopsch, F. 2012. A demand model for domestic air travel in Sweden, Journal of Air Transport Management 20: 46–48.


KPMG. 2006. Economic Impact Study of the Melbourne 2006 Commonwealth Games Post-Event Analysis, Office of Commonwealth Games Coordination. Available from internet: https://opus.lib.uts.edu.au/bitstream/10453/19802/1/econ_impact_report.pdf.


Kunt, M.M.; Aghayan, I.; Noii, N. 2011. Prediction for traffic accident severity: comparing the artificial neural network, genetic algorithm, combined genetic algorithm and pattern search methods, Transport 26(4): 353–366.


Luxton, D.D. 2016. An introduction to artificial intelligence in behavioral and mental healthcare. In book (ed. Loxton, D.D.) Artificial Intelligence in Behavioral and Mental Health Care, Academic Press, United Kingdom: 1-26.


Madden, J.R. 2002. The economic consequences of the Sydney Olympics: the CREA/Arthur Andersen study, Current Issues in Tourism 5(1): 7–21.


McKnight, P. 2010. Airline economics. In book (ed. Wald A., Fay C., Gleich R.) Introduction to Aviation Management, LIT Verlag, Germany: 25-53.


Merilaita, S. 2010. Applying artificial neural networks to the study of prey colouration. In book (ed. Tosh, C.R., Ruxton, G.D.) Modelling Perception with Artificial Neural Networks, Cambridge University Press, United Kingdom: 215-235.


Merkus, H.G.; Meesters, G.M.H. 2014. Introduction. In book (ed. Merkus, H.G., Meesters, G.M.H.) Particulate Products: Tailoring Properties for Optimal Performance, Springer International Publishing, Switzerland: 1-19.


Myers, R.H., Montgomery, D.C.; Anderson-Cook, C.M. 2016. Response surface methodology: process and product optimization using design experiments. Forth Edition, John Wiley & Sons, United States. 856 p.


Priddy, K.L.; Keller, P.E. 2005. Artificial neural networks: an introduction. SPIE Press, United States. 180 p.


Rao, R.K. 2011. Advanced modeling and optimization of manufacturing processes: international research and development. Springer Verlag London Ltd, United Kingdom. 380 p.


Remennikov, A.M.; Mendis, P.A. 2016. Prediction of airblast loads in complex environments using artificial neural networks. In book (ed. Syngellakis, S.) Design Against Blast: Load Definition & Structural Response. WIT Press, United Kingdom: 53-62.


Richter, M.M.; Weber, R.O. 2013. Case-based reasoning: a textbook. Springer-Verlag, Germany. 546 p.


Ruiz-Aguilar, J.J.; Turias, I.J.; Jiménez-Come, M.J. 2014. Hybrid approaches based on SARIMA and artificial neural networks for inspection time series forecasting’, Transportation Research Part E: Logistics and Transportation Review 67: 1–13.


Rumelhart, D.E.; Hinton, G.E.; Williams, R.J. 1986. Parallel distributed processing: explorations in the microstructure of cognition: foundations. The MIT Press, United States. 567 p.


Santos, A. A. P.; Junkes, L.N.; Pires Jr, F.C.M. 2014. Forecasting period charter rates of VLCC tankers through neural networks: a comparison of alternative approaches, Maritime Economics & Logistics 16(1): 72–91.


Sen, J.; Sas, A.K. 2014. Artificial neural network model for forecasting the stock price of Indian IT company. In book (ed. Babu, B.V. et al.) Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012, December 28-30. Springer India, India: 1151-1160.


Shahin, M.A. 2013. Artificial intelligence in geotechnical engineering: applications, modeling aspects, and future directions. In Book (ed. Yang, X.S. et al.) Metaheuristics in Water, Geotechnical and Transport Engineering. Elsevier, United Kingdom: 169-204.


Shirgure, P.S.; Rajput, G.S. 2014. Evaporation estimations with neural networks. In book (ed. Goyal, M.R., Harmsen, E.W.) Evapotranspiration: Principles and Applications for Water Management. Apple Academic Press, Canada, 131-164.


Sineglazov, V.; Chumachenko, E.; Gorbatyuk, V. 2013. An algorithm for solving the problem of forecasting, Aviation 17(1): 9–13.


Sivanandam, S.N.; Sumathi, S.; Deepa, S.N. 2006. Introduction to neural networks using Matlab 6.0. Tata McGraw-Hill, India. 656 p.


Sivrikaya, O.; Tunç, E. 2013. Demand forecasting for domestic air transportation in Turkey, The Open Transportation Journal 7: 20–26.


Skias, S.T. 2006. Background of the verification and validation of neural networks. In book (ed. Taylor, B.J.) Methods and Procedures for the Verification and Validation of Artificial Neural Networks. Springer Science+Business Media, United States: 1-12.


Srisaeng, P.; Baxter, G.; Wild, G. 2015a. Using an artificial neural network approach to forecast Australia’s domestic passenger air travel demand, World Review of Intermodal Transportation Research 5(3): 281-313.


Srisaeng, P.; Baxter, G.S.; Wild, G. 2015b. Forecasting demand for low cost carriers in Australia using an artificial neural network approach, Aviation 19(2): 90-103.


Terzic, E.; Terzic, J.; Nagarajah, R.; Alamgir, M. 2012. A neural network approach to fluid quantity measurement in dynamic environments. Springer-Verlag, United Kingdom. 140 p.


Tiryaki, S.; Aydın, A. 2014. An artificial neural network model for predicting compression strength of heat treated woods and comparison with a multiple linear regression model, Construction and Building Materials 62: 102–108.


Washington, S.P.; Karlaftis, M.G.; Mannering, F. 2011. Statistical and econometric methods for transportation data analysis. Second Edition, Chapman & Hall/CRC Press, United States. 544 p.


Wiart, J. 2016. Radio-frequency human exposure assessment: from deterministic to stochastic methods. John Wiley & Sons, United States. 196 p.


Yang, J.L.; Ma, J.; Howard, S.K. 2016. A structure optimization algorithm of neural networks for pattern learning from educational data. In book (ed. Shanmuganathan, S., Sandhya, S) Artificial Neural Network Modelling. Springer International Publishing, Switzerland: 67-82.


Yeung, D.S. et. al. 2010. Sensitivity analysis for neural networks. Springer, Germany. 86 p.