Volume List  / Volume 8 (1)

Article

THE USE OF AN ARTIFICIAL NEURAL NETWORK TO PREDICT AUSTRALIA’S EXPORT AIR CARGO DEMAND

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


8 / 1 / 15-30 Pages

Author(s)

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 -


Abstract

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.


Download Article

Number of downloads: 302


References:

Airports Council International. 2004. Chapter 3: Demand forecasting techniques. Available from Internet: http://www.aci-na.org/sites/default/files/chapter_3_-_demand_forecasting_techniques.pdf.

 

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.

 

Ali, M. et al. 2017. Emotion recognition involving physiological and speech signals: a comprehensive review. In book (ed. Kyamakya, K. et al.) Recent Advances in Nonlinear Dynamics and Synchronization: With Selected Applications in Electrical Engineering, Neurocomputing, and Transportation, Springer International Publishing, Switzerland: 287-302.

 

Australian Bureau of Statistics. 2001. Trade since 1900. Available from internet: http://www.abs.gov.au/ausstats/abs@.nsf/Previousproducts/1301.0Feature%20Article532001.

 

BaFail, 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.

 

Boeing Commercial Airplanes. 2016. World air cargo forecast: 2016-2017. Available from Internet: http://www.boeing.com/resources/boeingdotcom/commercial/about-our-market/cargo-market-detail-wacf/download-report/assets/pdfs/wacf.pdf.

 

Bureau of Infrastructure, Transport and Regional Economics. 2016. International airline activity 2015 statistical report. Available from Internet: https://bitre.gov.au/publications/ongoing/files/International_airline_activity_CY2015.pdf df.

 

Bureau of Infrastructure, Transport and Regional Economics. 2017. International airline activity 2016 statistical report. Available from internet: https://bitre.gov.au/publications/ongoing/files/International_airline_activity_CY2016.pdf.

 

Bureau of Transport and Regional Economics. 2004. Air transport statistics: international airlines. Issue number 1/117. Available from Internet: https://bitre.gov.au/publications/ongoing/files/International_airline_activity_CY03_Y.pdf.

 

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.

 

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

 

Cocchi, M.; Li Vigni, M.; Durante, C. 2017. Chemometrics-Bioinformatics. In book (ed. Georgiou, C.A.; Danezis, G.P.) Food Authentication: Management, Analysis and Regulation. John Wiley & Sons, Chichester, UK: 481-518.

 

Cook, G.N.; Billig, B.G. 2017. Airline operations and management: a management textbook. Routledge, United Kingdom. 344 p.

 

Dell ́Olio, L. et al. 2017. Public transportation quality of service: factors, models, and applications. Elsevier. The Netherlands. 242 p.

 

Doganis, R. 2010. Flying off course: airline economics and marketing. Fourth Edition, Routledge, United Kingdom. 336 p.

 

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.

 

Hamal, K. 2011. International air freight movements through Australian airports to 2030, in Australian Transport Research Forum 2011 Proceedings, 28 - 30 September 2011, Adelaide, Australia. Available from internet: http://atrf.info/papers/2011/2011_Hamal.pdf.

 

Hellermann, R. 2006. Capacity options for revenue management: theory and applications in the air cargo industry. Springer-Verlag, Germany. 199 p.

 

Hertwig, P.; Rau, P. 2010. Risk management in the air cargo industry: revenue management, capacity options and financial intermediation. Diplomica-Verlag, Germany.76 p.

 

Howard, R.D.; McLaughlin, G.W.; Knight, W.E. 2012. The handbook of institutional research. Jossey-Bass. United States. 768 p.

 

International Civil Aviation Organization. 2001. Outlook for air transport to the year 2010. Document No. Cir. 281/AT/116. International Civil Aviation Organization, Canada. 49 p.

 

Jazayeri, K.; Jazayeri, M.; Uysal, S. 2016. Comparative analysis of Levenberg–Marquardt and Bayesian regularization backpropagation algorithms in photovoltaic power estimation using artificial neural network. In book (ed. Perner, P) Advances in Data Mining. Applications and Theoretical Aspects: 16th Industrial Conference, ICDM 2016, New York, July 13-17 Proceedings, Springer International Publishing, Switzerland: 80-95.

 

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

 

Kar, B.P.; Nayak, S.K.; Nayak, S.C. 2015. Opposition-based GA learning of artificial neural networks for financial time-series forecasting. In book (ed. Behera, H.S., Mohapatra, D.P.) Computational Intelligence in Data Mining—Volume 2: Proceedings of the International Conference on CIDM, 5-6 December 2015, Springer-India, India: 405-414.

 

Kashyap, S.K.; Lolarak, R.; Kumar, L. 2017. Approximation of standing mine support parameters by artificial neural network. In book (ed. Singh, P.K. et.al.) NexGen Technologies for Mining and Fuel Industries. Volume I and II. Allied Publishers India: 575-582.

 

Khan, M.G.M.; Ahmed, S.; Prasad, B. 2015. Forecasting exchange rate of Kina against AUD using artificial neural network and time series models, International Journal of Business Forecasting and Marketing Intelligence 2(1): 37-54.

 

Khare, M.; Shiva Nagendra, S.M. 2007. Artificial neural networks in vehicular pollution modelling. Springer-Verlag, Germany. 242 p.

 

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.

 

Kupfer, F. et al. 2011. World air cargo and merchandise trade. In book (ed. Macário, R., Van de Voorde, E.) Critical Issues in Air Transport Economics and Business. Routledge, United Kingdom: 98-111.

 

Lahmiri, S.; Gagnon, S. 2015. A sequential probabilistic system for bankruptcy data classification. In book (ed. Jakóbczak, D.J.) Analyzing Risk through Probabilistic Modeling in Operations Research. IGI Global, United States: 138-147.

 

Lefort, C. 2016. Australia air freight crunch hits flights of fancy foods. Available from Internet: https://www.reuters.com/article/us-australia-aircargo/australia-air-freight-crunch-hits-flights-of-fancy-foods-idUSKCN12J0BQ.

 

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

 

Merkus, H.G.; Meesters, G.M.H. 2013. Introduction. In book (ed. Merkus, H.G.; Meesters, G.M.H.) Particulate Products: Tailoring Properties for Optimal Performance. Springer International Publishing, Switzerland: 1-19.

 

Nachev, A. 2008. Fuzzy ARTMAP neural network for classifying the financial health of a firm. In book (ed. Nguyen, N.T., Borzemski, L., Grzech, A., Ali, M.) New Frontiers in Applied Artificial Intelligence: 21st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2008, Wroclaw, Poland, June 2008, Proceedings. Springer-Verlag, Germany: 82-91.

 

Nikravesh, M.; Aminzadeh, F. 2003. Soft computing for intelligent reservoir characterization and modelling. In book (ed. Nikravesh, M., Aminzadeh, F., Zadeh, L.A.) Soft Computing and Intelligent Data Analysis in Oil Exploration, Elsevier Science, United Kingdom: 3-32.

 

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

 

Productivity Commission. 1998. International air services. Report No. 2, Ausinfo, Australia. 352 p.

 

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

 

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.

 

Sathe-Pathak, R.; Patil, S.; Panat, A. 2016. Application of three different artificial neural network architectures for voice conversion. In book (ed. Satapathy, S.C., Mandal, J.K., Udgata, S.K., Bhateja, V.) Information Systems Design and Intelligent Applications, Proceedings of Third International Conference India 2016, Volume 2. Springer India, India: 237-246.

 

Sefa, I.; Altin, N.; Ozdemir, S. 2017. Maximum power point tracking algorithms for partial shaded PV systems. In book (ed. Bizon, N. et al.) Energy Harvesting and Energy Efficiency: Technology, Methods, and Applications, Springer International Publishing, Switzerland: 261-292.

 

Senthilkumar, M. 2010. Use of artificial neural networks (ANNs) in colour measurement. In book (ed. Gulrajani, M.L.) Colour Measurement: Principles, Advances and Industrial Applications, Woodhead Publishing Limited, United Kingdom: 125-146.

 

Sharma, A. et al. 2015. ANN based modeling of performance and emission characteristics of diesel engine fueled with Polanga Biodiesel at different injection pressures, International Energy Journal 15: 57-72.

 

Shields, M. 1998. The changing cargo business. In book (ed. Butler, G.F., Keller, M.R.) Handbook of Airline Marketing, McGraw-Hill, United States: 183-187.

 

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 Company, India. 656 p.

 

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

 

Terzic, E. et al. 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.

 

Totamane, R.; Dasgupta, A.; Rao, S. 2012. Air cargo demand modeling and prediction, IEEE Systems Journal 8(1): 52-62.

 

Watts, M.J.; Worner, S.P. 2008. Using artificial neural networks to determine the relative contribution of abiotic factors influencing the establishment of insect pest species, Ecological Informatics 3(1): 64-74.