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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 Demand Forecasting Model Using Neural Network

Abstract Views: 311  |  PDF Views: 151

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