Volume List  / Volume 11 (3)



DOI: 10.7708/ijtte.2021.11(3).10

11 / 3 / 475-487 Pages


Panarat Srisaeng - School of Tourism and Hospitality Management, Suan Dusit University, Hua Hin Campus 77110, Prachaup Khiri Khan, Thailand -

Glenn Baxter - School of Tourism and Hospitality Management, Suan Dusit University, Hua Hin Campus 77110, Prachaup Khiri Khan, Thailand -


This study has proposed and empirically tested an adaptive neuro-fuzzy inference system (ANFIS) model for predicting Australia’s outbound international airline passenger demand. The model was developed using eleven input parameters of world GDP, world population, world air fare yields, world jet fuel prices, outbound flights from Australia, Australia’s unemployment numbers, Australian’s (AUD/USD) foreign exchange rate, Australia’s outbound tourist expenditure and four dummy variables. The model was constructed using annual data from 1994 to 2019. The hybrid learning algorithm and the subtractive clustering partition method were used to generate the optimum ANFIS models. The performance of the model was measured using five error measures: coefficient of determination (R2-value), root mean square errors (RMSE), mean absolute errors (MAE) and the mean absolute percentage error (MAPE). The results found that the mean absolute percentage error (MAPE) for the overall data set of the model was 3.60%. The R2-value was around 0.9886, demonstrating that the ANFIS is an efficient model for predicting Australia’s outbound airline passenger demand.

Download Article

Number of downloads: 579


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 Transport Management 7(3): 143–148.


Abraham, A. 2005. Adaption of fuzzy inference system using neural learning. In book (eds. Nedjah, N.; de Macedo Mourelle, L.) Fuzzy Systems Engineering: Theory and Practice, Springer-Verlag, Germany, 53-85.


Al-Mayyahi, A.; Wang, W.; Birch, P. 2014. Adaptive neuro-fuzzy technique for autonomous ground vehicle navigation, Robotics 3(4): 349-370.


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 Worldwide 5(2): 72–86.


Baseri, H. 2011. Design of adaptive neuro-fuzzy inference system for estimation of grinding performance, Materials and Manufacturing Processes 26(5): 757–763.


BITRE. 2020. International Airline Activity 2019. Available from Internet: https://www.bitre.gov.au/sites/defaults/files/documents/international_airline_activity_cy2019.pdf.


Chen, M.S.; Ying, L.C.; Pan, M.C. 2010. Forecasting tourist arrivals by using the adaptive network-based fuzzy inference system, Expert Systems with Applications 37(2): 1185-1191.


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, USA, 76-86.


Commonwealth Games. 2020. Gold Coast 2018. Available from Internet: https://thecgf.com/games/gold-coast-2018.


Cook, G.N.; Billig, B.G. 2017. Airline operations and management: A management textbook. Routledge, UK. 362 p.


Dargay, J.; Hanley, M. 2001. The determinants of the demand for international air travel to and from the UK. In Proceedings of the 9th world conference on transport research, Edinburgh, Scotland, 59.1-59.14.


Doganis, R. 2019. Flying off course: Airline economics and marketing. Fifth Edition. Routledge, UK. 358 p.


Duval, D.T. 2019. The impact of government policy and regulation. In book (eds. Graham, A.; Dobruszkes, F.) Air Transport – A Tourism Perspective, Elsevier, The Netherlands, 57-66.


Garrow, L.A. 2010. Discrete choice modelling and air travel demand: Theory and applications. Routledge, UK. 306 p.


Hensher, D.A.; Brewer, A.M. 2002. Going for gold at the Sydney Olympics: how did transport perform? Transport Reviews 22(4): 381-399.


Jaffe, S. D. 2016. Airspace closure and civil aviation: A strategic resource for airline managers. Routledge, UK. 302 p.


Janić, M. 2007. The sustainability of air transportation: A quantitative analysis and assessment. Ashgate Publishing, UK. 376 p.


Janić, M.; Stough, R.R. 2005. Congestion charging at airports: Dealing with an inherent complexity. In book (eds. Reggiani, A.; Schintler, L.A.) Methods and Models in Transport and Telecommunications: Cross Atlantic Perspectives. Springer-Verlag, Germany, 239-267.


Jiang, F.; Dong, L.; Dai, Q.; Nobes, D.C. 2018. Using wavelet packet denoising and ANFIS networks based on COSFLA optimization for electrical resistivity imaging inversion, Fuzzy Sets and Systems 337: 93-112.


Kaleel, A.H.; Mallick, Z. 2012. Some studies on noises and its effects on industrial/cognitive task performance and modelling. In book (ed. Fazle Azeem, M.) Fuzzy Inference System: Theory and Applications, InTech, Croatia, 171-214.


Klophaus, R. 2009. Kerosene’s price impact on air travel demand: A cause-and-effect chain. In book (eds. Conrady, R.; Buck, M.) Trends and Issues in Global Tourism 2009. Springer-Verlag, Germany, 79-94.


Kovač, P.; Savković, B.; Rodić, D.; Aleksić, A.; Gostimirović, M.; Sekulić, M.; Kulunžić, N. 2020. Modelling and optimization of surface roughness parameters of stainless steel by artificial intelligence methods. In book (eds. Durakbasa, N. M.; Güneş Gençyılma, M.). In Proceedings of the International Symposium for Production Research 2019, Springer Nature Switzerland AG, Switzerland, 3-12.


KPMG. 2006. Economic impact study of the Melbourne 2006 Commonwealth Games post-event analysis. Office of Commonwealth Games Coordination, Melbourne. Available from Internet: https://opus.lib.uts.edu.au/bitstream/10453/19802/1/econ_impact_report.pdf.


Kumar, P.A.; Vaidehi, V. 2017. A transfer learning framework for traffic video using neuro-fuzzy approach, Sādhanā 42(9): 1431–1442.


Lerkkasemsan, N. 2017. Fuzzy logic-based predictive model for biomass pyrolysis, Applied Energy 185(Part 2): 1019-1030.


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


Mehta, R.; Jain, S. 2009. Optimal operation of a multi-purpose reservoir using neuro-fuzzy technique, Water Research Management 23(3): 509-529.


Mitsa, T. 2010. Temporal data mining. Chapman & Hall/CRC Press, USA. 395 p.


Narang, S.K.; Kumar, S.; Verma, V. 2017. Knowledge discovery from massive data streams. In book (eds. Singh, A.; Dey, N.; Ashour, A.S.; Santhi, V.) Web Semantics for Textual and Visual Information Retrieval. IGI Global, USA, 109-143.


Prideaux, B. 2003. The need to use disaster planning frameworks to respond to major tourism disasters: analysis of Australia's response to major tourism disasters in 2001. In book (eds. Michael Hall, C.; Timothy, D. J.; Duval, D. T.) Safety and Security in Tourism: Relationships, Management, and Marketing. The Haworth Hospitality Press, USA 281-298.


Profillidis, V.A.; Botzoris, G.N. 2019. Modeling of transport demand: Analyzing, calculating, and forecasting transport demand. Elsevier, The Netherlands. 472 p.


Radnoti. G. 2002. Profit strategies for air transportation. McGraw-Hill Professional, USA. 516 p.


Savkovic, B.; Kovac, P.; Dudic, B.; Rodic, D.; Taric, M.; Gregus, M. 2019. Application of an adaptive “neuro-fuzzy” inference system in modelling cutting temperature during hard turning, Applied Sciences 9(18): 3739.


Shukla, A.; Tiwari, R.; Kala, R. 2010. Real life applications of soft computing. CRC Press, Boca Raton. 686 p.


Srisaeng, P.; Baxter, G.S.; Wild, G. 2015a. An adaptive neuro-fuzzy inference system for forecasting Australia's domestic low cost carrier passenger demand, Aviation 19(3): 150-163.


Srisaeng, P.; Baxter, G.; Wild, G. 2015b. An adaptive neuro-fuzzy inference system for modelling Australia's regional airline passenger demand, International Journal of Sustainable Aviation 1(4): 348–374.


Stabler, M.J.; Papatheoudorou, A.; Sinclair, M.T. 2010. The economics of tourism. Second Edition, Routledge, UK. 495 p.


Suparta, W.; Alhasa, K.M. 2016. Modeling of tropospheric delays using ANFIS. Springer International Publishing, Switzerland. 109 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.


Übeyli, E.D.; Cvetkovic, D.; Holland, G.; Cosic, I. 2010. Adaptive neuro-fuzzy inference system employing wavelet coefficients for detection of alterations in sleep EEG activity during hypopnoea episodes, Digital Signal Processing 20(3): 678–691.


Vahabi, A.; Seyyedi, S.; Alborzi, M. 2016. A sales forecasting model in the automotive industry using adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm, International Journal of Advanced Computer Science and Applications 7(11): 24-30.


Wei, L.Y.; Chen, T.L.; Ho, T.H. 2011. A hybrid model based on adaptive-network-based fuzzy inference system to forecast Taiwan stock market, Expert Systems with Applications 38(11): 13625-13631.


Wensveen, J. G. 2015. Air transportation: A management perspective. Eighth Edition, Routledge, UK. 604 p.


Xiao, Y.; Liu, J.J.; Hu, Y.; Wang, Y.; Lai, K.K.; Wang, S. 2014. A neuro-fuzzy combination model based on singular spectrum analysis for air transport demand forecasting, Journal of Air Transport Management 39: 1–11.


Yetilmezsoy, K.; Fingas, M.; Fieldhouse, B. 2011. An adaptive neuro-fuzzy approach for modeling of water-in-oil emulsion formation, Colloids and Surfaces A: Physicochemical and Engineering Aspects 389(1–3): 50–62.


Zaki, A.M.; Mahgoub, O.A.; El-Shafi, A.M.; Soliman, A.M. 2012. Control of efficient intelligent robotic gripper using fuzzy inference system. In book (ed. Fazle Azeem, M) Fuzzy Inference System: Theory and Applications. InTech Open, Croatia, 85-112.


Zendehboudi, A.; Li, X.; Wang, B. 2017. Utilization of ANN and ANFIS models to predict variable speed scroll compressor with vapor injection, International Journal of Refrigeration 74: 475-487.