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

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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
    Ministry of Education Key Laboratory of Process Optimization and Intelligent Decision-making, Hefei University of Technology, Hefei 230009, China)

  • Changyong Liang

    (School of Management, Hefei University of Technology, Hefei 230009, China
    Ministry of Education Key Laboratory of Process Optimization and Intelligent Decision-making, Hefei University of Technology, Hefei 230009, China)

  • Binyou Wang

    (School of Management, Hefei University of Technology, Hefei 230009, China)

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.

Suggested Citation

  • Keqing Li & Wenxing Lu & Changyong Liang & Binyou Wang, 2019. "Intelligence in Tourism Management: A Hybrid FOA-BP Method on Daily Tourism Demand Forecasting with Web Search Data," Mathematics, MDPI, vol. 7(6), pages 1-14, June.
    • Handle: RePEc:gam:jmathe:v:7:y:2019:i:6:p:531-:d:238766
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    References listed on IDEAS

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    Cited by:

    1. Haodong Sun & Yang Yang & Yanyan Chen & Xiaoming Liu & Jiachen Wang, 2023. "Tourism demand forecasting of multi-attractions with spatiotemporal grid: a convolutional block attention module model," Information Technology & Tourism, Springer, vol. 25(2), pages 205-233, June.
    2. Jiahui Huang & Chenglong Zhang, 2024. "Daily Tourism Demand Forecasting with the iTransformer Model," Sustainability, MDPI, vol. 16(23), pages 1-22, December.
    3. Guanghai Zhang & Hongying Yuan, 2022. "Spatio-Temporal Evolution Characteristics and Spatial Differences in Urban Tourism Network Attention in China: Based on the Baidu Index," Sustainability, MDPI, vol. 14(20), pages 1-15, October.
    4. Abang Zainoren Abang Abdurahman & Wan Fairos Wan Yaacob & Syerina Azlin Md Nasir & Serah Jaya & Suhaili Mokhtar, 2022. "Using Machine Learning to Predict Visitors to Totally Protected Areas in Sarawak, Malaysia," Sustainability, MDPI, vol. 14(5), pages 1-16, February.
    5. Jian-Wu Bi & Tian-Yu Han & Yanbo Yao, 2024. "Collaborative forecasting of tourism demand for multiple tourist attractions with spatial dependence: A combined deep learning model," Tourism Economics, , vol. 30(2), pages 361-388, March.

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