Modelling and Forecasting Cruise Tourism Demand to İzmir by Different Artificial Neural Network Architectures

Murat Cuhadar, Iclal Cogurcu, Ceyda Kukrer

Abstract


Abstract

Cruise ports emerged as an important sector for the economy of Turkey bordered on three sides by water. Forecasting cruise tourism demand ensures better planning, efficient preparation at the destination and it is the basis for elaboration of future plans. In the recent years, new techniques such as; artificial neural networks were employed for developing of the predictive models to estimate tourism demand. In this study, it is aimed to determine the forecasting method that provides the best performance when compared the forecast accuracy of Multi-layer Perceptron (MLP), Radial Basis Function (RBF) and Generalized Regression neural network (GRNN) to estimate the monthly inbound cruise tourism demand to İzmir via the method giving best results. We used the total number of foreign cruise tourist arrivals as a measure of inbound cruise tourism demand and monthly cruise tourist arrivals to İzmir Cruise Port in the period of January 2005 ‐December 2013 were utilized to appropriate model. Experimental results showed that radial basis function (RBF) neural network outperforms multi-layer perceptron (MLP) and the generalised regression neural networks (GRNN) in terms of forecasting accuracy. By the means of the obtained RBF neural network model, it has been forecasted the monthly inbound cruise tourism demand to İzmir for the year 2014.


Keywords


Cruise Tourism, Demand Forecasting, Artificial Neural Networks

Full Text:

PDF

References


REFERENCES

Boyd, L. (1999). Brief History of the Passenger Ship Industry. (2014, January, 13). Retrieved from the Duke University, Digital Collections Website, http://library.duke.edu/digitalcollections/adaccess/guide/transportation/passenger-ships.

BREA (Business Research & Economic Advisors) (2012), Contribution of Cruise Tourism to the Economics of Europe 2012; Turkey and Black Sea Regional Report, UK.

Cartwright, R. & Baird, C. (1999), The Development and Growth of the Cruise Industry, Butterworth-Heinemann.

Cevik, A. & Guzelbey, I. H., (2008) Neural Network Modeling of Strength Enhancement or CFRP Confined Concrete Cylinders, Building and Environment, (43), 751-763.

Cho, V. (2003). A comparison of three different approaches to tourist arrival forecasting. Tourism Management, 24(3), 323–330.

Cho,V. (2009). A study on the temporal dynamics of tourism demand in the Asia Pacific region, International Journal of Tourism Research, 11 (5) 465–485.

Churchwell, S.J. (2009). The Letters of Julia Newel from the 1867 Cruise of the Quaker City as Reported to the Janesville Gazette, Mark Twain Journal, Vol. 47, No. 112, The “Quaker City” Excusion, Part II, 38-54.

Claveria, O., Monte, E. & Torra, S. (2013). Tourism demand forecasting with different neural networks models, IREA Working Papers: 201321, University of Barcelona, Research Institute of Applied Economics.

Claveria, O. & Torra, S. (2014). Forecasting tourism demand to Catalonia: Neural networks vs. time series models, Economic Modelling, 36 (2014) 220–228.

Crouch G.I. & Ritchie J.R.B. (1999). Tourism, Competitiveness, and Societal Prosperity, Journal of Business Research 44, pp. 137-152.

CLIA-Cruise Lines International Association (2013). Cruise Industry Report, Contribution of Cruise Tourism to the Economies of Europe, 2013 Edition, Norway.

Cruise Critic, (2014) Cruise Reviews & News, (2014, January 10,). Retrieved from the http://www.cruisecritic.com/ports/newport_print.cfm?ID=213 (10.01.2014).

Dickinson, B. & Vladmin, A. (2007). Selling the Sea: An inside Look at the Cruise Industry, Second Edition, John Wiley and Sons, Inc.

Frechtling, D. C. (2001) Forecasting Tourism Demand: Methods and Strategies, Butterworth-Heinemann, Oxford.

Guzel, K. (2006), Kurvaziyer Turizmin Türkiye’deki Geleceği, Yayınlanmamış Yüksek Lisans Tezi, İstanbul Üniversitesi Deniz Bilimleri ve İşletmeciliği Enstitüsü.

Goh, C. & Law, R. (2011). The Methodological Progress of Tourism Demand Forecasting: A Review of Related Literature, Journal of Travel & Tourism Marketing, (28) 3, 296-317.

Hu, C. (2002). Advanced Tourism Demand Forecasting: Artificial Neural Network and Box-Jenkins Modeling, n.p.: ProQuest, UMI Dissertations Publishing,

Izmir Port Authority, (2013)

Izmir Chamber of Commerce, (2013)

Katsoufis G.P., (2006). A Decision Making Framework for Cruise Ship Design, Unpublished B.S. Engineering Theses, University of Miami.

Kaynar, O. (2012). Forecasting Industrial Production Index with Soft Computing Techniques, Economic Computation and Economic Cybernetics Studies and Research, 46(3), 113-138.

Khare, M. & Nagendra, S. (2007) Artificial neural networks in vehicular pollution modelling, Studies in Computational Intelligence, Volume 41, Springer-Verlag.

Klein, A.R. (2002). Cruise Ship Blues; To Underside of the Cruise Industry, Consortium Book Sales & Dist. Inc., Canada.

Kuvulmaz, J., Usanmaz, S. & Engin, S. N. (2005). Time-Series Forecasting by Means of Linear and Nonlinear Models; MICAI 2005: Advances in Artificial Intelligence, Lecture Notes in Computer Science Volume 3789, 504-513.

Leung, M.T., Chen, A.S. & Daouk, H. (2000). Forecasting exchange rates using general regression neural networks, Computers & Operations Research, 27(11-12), 1093–1110.

Lewis, C. D. (1982). Industrial and business forecasting methods: A practical guide to exponential smoothing and curve fitting, London, UK: Butterworth Scientific.

Li, G. (2004). Tourism forecasting-an almost ideal demand system approach. Unpublished Ph.D. thesis, University of Surrey.

Lim, C. (2006). A survey of tourism demand modelling practice: issues and implications, In Dwyer, L. and Forsyth, P. (Eds.), International Handbook on the Economics of Tourism, Edward Elgar Publishing Inc.

Lin, C.J., Chen, H.F. & Lee, T.S. (2011). Forecasting Tourism Demand Using Time Series, Artificial Neural Networks and Multivariate Adaptive Regression Splines: Evidence from Taiwan, International Journal of Business Administration, 2(2) 14-24.

Marwala, T. (2013). Neural Approaches to Economic Modeling in Economic Modeling Using Artificial Intelligence Methods, Springer-Verlag London.

Ministry of Culture and Tourism of Turkey (2007). Tourism Strategy Action Plan 2023

Moreno, J.J.M., Poll, A.P. & Gracia, P.M. (2011). Artificial neural networks applied to forecasting time series, Psicothema, 23 (2), 322-329.

Oral, Z. & Esmer, S. (2010) Ege Bölgesi Kurvaviyer Turizmin Mevcut Durumu ve Geleceği, Türkiye’nin Kıyı ve Deniz Alanları VIII. Ulusal Kongresi, 27 Nisan – 1 Mayıs, Trabzon, 805-817.

Palmer, A., Montaño, J. J. & Sesé, A. (2006), Designing an artificial neural network for forecasting tourism time-series, Tourism Management, 27 (5) 781-790.

Patan, K. (2008). Artificial Neural Networks for the Modelling and Fault Diagnosis of Technical Processes, Lecture Notes in Control and Information Sciences, Springer-Verlag.

Pattie, D.C. & Snyder, J. (1996). Using A Neural Network To Forecast Visitor Behavior, Annals of Tourism Research 23 (1) 151–164.

Pavlic, I. (2013) Cruise Tourism Demand Forecasting- The Case of Dubrovnik, Tourism and Hospitality Management, 19 (1), 125-142.

Petropoulos, C., Nikolopoulos, K., Patelis, A. & Assimakopoulos, V. (2005) A Technical Analysis Approach to Tourism Demand Forecasting, Applied Economics Letters, (12), 327-333.

Smith, A.K. (2002) Neural Networks for Business: An Introduction. In Smith, K.A & Gupta, J.T.D (Eds.), Neural Networks in Business: Techniques and Applications, Idea Group Publishing, 1-25.

Song, H., Wong, K.K.F. & Chon, K.K.S (2003). Modelling And Forecasting the Demand For Hong Kong Tourism, Hospitaly Management (22) 435-451.

Song, H., Witt, S.F. & Li, G. (2009). The Advanced Econometrics of Tourism Demand, Routledge Advances in Tourism Series, Taylor & Francis.

Teixeira, J.P. & Fernandes, P.O. (2012). Tourism Time Series Forecast - Different ANN Architectures with Time Index Input, Procedia Technology, 5, 445 – 454.

Tomlinson S. (2007) Smooth Sailing -Navigating the Sea of Law Applicable to the Cruise Line Industry, Jeffrey S. Moorad Sports Law Journal, 14 (1), 127-158

Wild, G.P. & Dearing, J. (2000). Development of and Prospects for Cruising in Europe, Maritime Policy and Management, 27 (4), 315-333.

Wilkinson, P.F. (2006), The Cruising Industry. In Dowling K. R. (Ed.) Cruise Ship Tourism, CABI Publishing, UK, 170-184.

Witt, S. F. (2000), Tourism Demand Modelling and Forecasting: Modern Econometric Approaches, Elsevier Science, New York.

Wong, B. K., Jiang, L. & Lam, J. (2000). A bibliography of neural network business application research: 1994-1998. Computers and Operations Research, 27(11), 1045-1076.

Yasar, O. (2012). The Popular Resort Port of Cruise Tourism in the Eastern Mediterranean Basin: Turkey, International Journal of Human Sciences, 9 (1), 412-440.

Yilmaz, I. & Kaynar, O. (2011). Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils, Expert Systems with Applications, (38), 5958–5966.

Zhang, G. P. (2004) Business Forecasting with Artificial Neural Networks: An Overview. In Zhang P. (Ed.) Neural Networks in Business Forecasting, Idea Group Publishing, 1-22

Zhang, G. P. & Qi, M. (2005) Neural network forecasting for seasonal and trend time series, European Journal of Operational Research, 160 (2), 502-514.




DOI: http://dx.doi.org/10.18533/ijbsr.v4i3.431

Refbacks

  • There are currently no refbacks.




Copyright (c)

 ...........................................................................................................................

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

............................................................................................................................

If you find difficulties in submitting manuscript please forward your doc file to support@thejournalofbusiness.org. Our support team will assist you in submission process and other technical matters.

In order to get notifications on inbox please add  this domain thejournalofbusiness.org in your email safe list.

International journal of business and social research (Print)
ISSN 2164-2540

International journal of business and social research (Online)
ISSN 2164-2559

[International Journal of Business and Social Research (IJBSR) previously published by MIR Center for Socio-Economic Research, MD, USA. From February 2018 this journal is published by the LAR Center Press, OR, USA]