Intelligence in Tourism Management: A Hybrid FOA-BP Method on Daily Tourism Demand Forecasting with Web Search Data

Keqing Li, Wenxing Lu, Changyong Liang and Binyou Wang
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Keqing Li: School of Management, Hefei University of Technology, Hefei 230009, China
Wenxing Lu: School of Management, Hefei University of Technology, Hefei 230009, China
Changyong Liang: School of Management, Hefei University of Technology, Hefei 230009, China
Binyou Wang: School of Management, Hefei University of Technology, Hefei 230009, China

Mathematics, 2019, vol. 7, issue 6, 1-14

Abstract: The Chinese tourism industry has been developing rapidly for the past several years, and the number of people traveling has been increasing year by year. However, many problems still beset current tourism management. Lack of effective management has caused numerous problems, such as tourists stranded during tourist season and the declining service quality of scenic spots, which have become the focus of tourists’ attention. Network search data can intuitively reflect the attention of most users through the combination of the network search index and the back propagation (BP) neural network model. This study predicts the daily tourism demand in the Huangshan scenic spot in China. The filtered keyword in the Baidu index is added to the hybrid neural network, and a BP neural network model optimized by a fruit fly optimization algorithm (FOA) based on the web search data is established in this study. Different forecasting methods are compared in this paper; the results prove that compared with other prediction models, higher accuracy can be obtained when it comes to the peak season using the FOA-BP method that includes web search data, which is a sustainable means of practically solving the tourism management problem by a more accurate prediction of tourism demand of scenic spots.

Keywords: tourism management; hybrid method; fruit fly optimization algorithm; neural network; web search data; forecast of daily tourism demand; optimization method (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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