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Daily tourism volume forecasting for tourist attractions

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  • Bi, Jian-Wu
  • Liu, Yang
  • Li, Hui

Abstract

A novel approach based on long short-term memory (LSTM) networks that can incorporate multivariate time series data, including historical tourism volume data, search engine data and weather data, is proposed for forecasting the daily tourism volume of tourist attractions. The proposed approach is applied to forecast the daily tourism volume of Jiuzhaigou and Huangshan Mountain Area, two famous tourist attractions in China. Through these two applications, the validity of the proposed approach is verified. In addition, the forecasting power of the approach with historical data, search engine data and weather data is stronger than that without search engine data or without both search engine data and weather data, which provides evidence that search engine data and weather data are of great significance to tourism volume forecasting.

Suggested Citation

  • Bi, Jian-Wu & Liu, Yang & Li, Hui, 2020. "Daily tourism volume forecasting for tourist attractions," Annals of Tourism Research, Elsevier, vol. 83(C).
    • Handle: RePEc:eee:anture:v:83:y:2020:i:c:s0160738320300670
    DOI: 10.1016/j.annals.2020.102923
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    17. Zhenjie Liao & Lijuan Zhang, 2021. "Spatial distribution evolution and accessibility of A-level scenic spots in Guangdong Province from the perspective of quantitative geography," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-18, November.
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    19. 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.
    20. Jianxin Zhang & Yuting Yan & Jinyue Zhang & Peixue Liu & Li Ma, 2023. "Investigating the Spatial-Temporal Variation of Pre-Trip Searching in an Urban Agglomeration," Sustainability, MDPI, vol. 15(14), pages 1-17, July.
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    22. Bi, Jian-Wu & Li, Hui & Fan, Zhi-Ping, 2021. "Tourism demand forecasting with time series imaging: A deep learning model," Annals of Tourism Research, Elsevier, vol. 90(C).

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