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Tourism Demand Forecasting Model Using Neural Network


Affiliations
1 Department of Tourism and M.I.C.E., Chung Hua University, Taiwan, Province of China
 

Travel agencies should be able to judge the market demand for tourism to develop sales plans accordingly. However, many travel agencies lack the ability to judge the market demand for tourism, and thus make risky business decisions. Based on the above, this study applied the Artificial Neural Network combined with the Genetic Algorithm (GA) to establish a prediction model of air ticket sales revenue. GA was used to determine the optimum number of input and hidden nodes of a feedforward neural network. The empirical results suggested that the mean absolute relative error(MARE) of the proposed hybrid model's predicted value of air ticket sales revenue and the actual value was 10.51%and the correlation coefficient was 0.913. The proposed model had good predictive capability and could provide travel agency operators with reliable and highly efficient analysis data.

Keywords

Artificial Neural Network, Genetic Algorithm, Air Ticket Sales, Prediction Model.
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  • Tourism Bureau, M.O.T.C. Republic of China (Taiwan). (2016a). Outbound Departures of Nationals of the Republic of China. Available: http://admin.taiwan.net.tw/statistics (30 May 2016).

  • Zhu, Z and Zhao, J. (2011). Comparative Study on the Sources of e-Business Competitive Advantages between Travel Agencies and Online Travel Service Firms.Journal of China University of Geosciences Social Sciences Edition, 11, 114-119.

  • Chen, S. Y. and Liu, H.H. (2010). A Study on Business Models of on-line Travel agency-The Case of ezTravel.Journal of Leisure and Tourism Industry Research, 5, 90-99.

  • Dai, M.H.(2011). Unfair Competition in Travel Service Industry.Journal of Eastern Liaoning University (Social Sciences), 13, 38-42.

  • Chang, H.T., Chen, P.C., Huang, H.C., and Lin, D.H. (2013). A Study on the Application of Neural Network to the Prediction of Weight Control.International Journal of Engineering Research and Development, 5, 78-85.

  • Huang, H.C. and Ho,C.C. (2012). Back-Propagation Neural Network Combined With a Particle Swarm Optimization Algorithm for Travel Package Demand Forecasting.International Journal of Digital Content Technology and its Applications, 4, 194-203.

  • Tourism Bureau, M.O.T.C. Republic of China (Taiwan). (2016b). Statute for the Development of Tourism. Available: http://admin.taiwan.net.tw/law(30 May 2016).

  • Stynes, D.J. (1983). An Introduction to Recreation Forecastings. In Liber, S. R. et al. (eds) Recreation planning and management, UK: E. & F. N. Spon Ltd.

  • Lin, Y.H. and Chung, L.C. (2011). Positioning Analysis of Taiwan Travel Market-Perspectives of Travel Related Practitioners from China and Taiwan.Journal of Island Tourism Research, 4, 1-23.

  • Oblak, L., Stirn, L.Z., Moro, M., Hrovatin, J., Mole, S., and Kuzman, M.K. (2012). Choice of Quantitative Method for Forecasting of Parquet Sales.Drvna Industrija, 63, 249-254.

  • ]Wong,H.L., Ku, S.C., and Chen, H.W. (2012). ARIMA and Neural Network Model for Passenger Flow Forecasting-A Case of Hong Kong Airport.Ming Hsin Journal, 38, 69-83.

  • Huang, S.Y., Chiu, A.A., and Wang, B.C. (2012). Applying Intellectual Capital on Financial Distress Prediction Model in Taiwan Information Technology and Electronic Industry.International Journal of Advancements in Computing Technology, 4, 270-280.

  • Chan, Y.M. (1993). Forecasting Tourism: A Sine Wave Time Series Regression Approach.Journal of Travel Research, 32, 58-60.

  • Wang, C.H. (2004). Predicting Tourism Demand using Fuzzy Time Series and Hybrid Grey Theory.Tourism Management, 25, 367-374.

  • Chu, F.L. (1998). Forecasting Tourism: A Combined Approach.Tourism Management, 19, 515-520.

  • Lin, C.J., Chen, H.F., and 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, 14-24.

  • Aibinu, A.M., Salami, M.J.E., and Shafie, A.A. (2010). Determination of Complex-Valued Parametric Model Coefficients Using Artificial Neural Network Technique.Advances in Artificial Neural Systems, 2010, Article ID 984381, 11 pages.

  • Chang, F.C. (2005). Neural Network.Taiwan: Tung-Hua Books Ltd..

  • Yeh, I.C. (2009). Application and Practice of Neural Networks. Taiwan: Scholars Books Ltd..

  • Huang, H.C., Chang, A.Y., and Ho, C.C.(2013). Using Artificial Neural Networks to Establish a Customer-cancellation Prediction Model. Przeglad Elektrotechniczny, 89, 178-180.

  • Leondes, C. (1997). Advances in Theory and Applications: Neural Network Systems Techniques and Applications. USA: Elsevier.

  • Samsudin, R., Saad, P., and Shabri, A. (2010). Hybridizing GMDH and Least Squares SVM Support Vector Machine for Forecasting Tourism Demand.International Journal of Research and Reviews in Applied Sciences, 3, 274-279.

  • Petrevska, B. (2012). Forecasting International Tourism Demand: The Evidence of Macedonia.UTMS Journal of Economics, 3, 45-55.

  • Huang, H.C.(2013a). Artificial Intelligence Technology in Travel Agency Operating Revenue Forecasts.Journal of Theoretical and Applied Information Technology, 54, 92-95.

  • Chaiboonsri, C. and Chaitip, P. (2012). The Modeling International Tourism Demand for Length of Stay in India: Social Development, Economics Development.Journal of Knowledge Management, Economics and Information Technology, 2,81-98.

  • Huang, H.C. (2013b). A Hybrid Neural Network Prediction Model of Air Ticket Sales.Telkomnika Indonesian Journal of Electrical Engineering, 11, 6413-6419.

  • Huang, H.C. (2012). Using a Hybrid Neural Network to Predict the CNY/USD Exchange Rate.International Journal of Advancements in Computing Technology, 4, 322-328.

  • Woarawichai, C., Kuruvit, K., and Vashirawongpinyo, P. (2012). Applying Genetic Algorithms for Inventory Lot-Sizing Problem with Supplier Selection under Storage Capacity Constraints.International Journal of Computer Science Issues, 9, 18-23.

  • Ibrahim, S.S. and Bamatraf, M.A. (2013). Interpretation Trained Neural Networks Based on Genetic Algorithms. International Journal of Artificial Intelligence and Applications, 4, 13-22.

  • Ramzani, B., PourReza, T., Mehran, S., and Salmalian, K. (2014). Modeling and Optimization of Energy Absorber Using Finite Element and Genetic Algorithm.Pensee Journal, 76, 190-206.


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  • Tourism Demand Forecasting Model Using Neural Network

Abstract Views: 366  |  PDF Views: 179

Authors

Han-Chen Huang
Department of Tourism and M.I.C.E., Chung Hua University, Taiwan, Province of China
Cheng-I Hou
Department of Tourism and M.I.C.E., Chung Hua University, Taiwan, Province of China

Abstract


Travel agencies should be able to judge the market demand for tourism to develop sales plans accordingly. However, many travel agencies lack the ability to judge the market demand for tourism, and thus make risky business decisions. Based on the above, this study applied the Artificial Neural Network combined with the Genetic Algorithm (GA) to establish a prediction model of air ticket sales revenue. GA was used to determine the optimum number of input and hidden nodes of a feedforward neural network. The empirical results suggested that the mean absolute relative error(MARE) of the proposed hybrid model's predicted value of air ticket sales revenue and the actual value was 10.51%and the correlation coefficient was 0.913. The proposed model had good predictive capability and could provide travel agency operators with reliable and highly efficient analysis data.

Keywords


Artificial Neural Network, Genetic Algorithm, Air Ticket Sales, Prediction Model.

References