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The Baidu Index: Uses in predicting tourism flows –A case study of the Forbidden City

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  • Huang, Xiankai
  • Zhang, Lifeng
  • Ding, Yusi

Abstract

Tourist overcrowding of sites during the ‘Golden Week’ is a not an uncommon situation in China today. Consequently the prediction of tourist numbers is important for tourist attractions management and planning. Most existing methods rely on well-structured statistical data published by the government. However, this approach is limited in two aspects: 1) there may be significant delays in the publication of such data and 2) the sample size can be small, leading to inaccurate predictions. This paper proposes a novel approach for predicting tourist flows based on the Baidu Index. The Index provides search history containing different keywords on a daily basis dating back to 2006. The approach uses co-integration theory and Granger causality analysis to find the relationship between the internet search data and the actual tourist flow. The paper compares analysis results obtained by two kinds of predictive models, with or without considering Baidu Index. The study shows that there is a long-term equilibrium relationship and Granger causal relation between the observed number of tourists and a set of related keywords in the Baidu Index. It indicated a positive correlation between the increasing Baidu keyword search index and the increasing observed tourist flow.

Suggested Citation

  • Huang, Xiankai & Zhang, Lifeng & Ding, Yusi, 2017. "The Baidu Index: Uses in predicting tourism flows –A case study of the Forbidden City," Tourism Management, Elsevier, vol. 58(C), pages 301-306.
    • Handle: RePEc:eee:touman:v:58:y:2017:i:c:p:301-306
    DOI: 10.1016/j.tourman.2016.03.015
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    Cited by:

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    9. Bi, Jian-Wu & Liu, Yang & Li, Hui, 2020. "Daily tourism volume forecasting for tourist attractions," Annals of Tourism Research, Elsevier, vol. 83(C).
    10. Ling Tang & Chengyuan Zhang & Tingfei Li & Ling Li, 2021. "A novel BEMD-based method for forecasting tourist volume with search engine data," Tourism Economics, , vol. 27(5), pages 1015-1038, August.
    11. Katerina Volchek & Anyu Liu & Haiyan Song & Dimitrios Buhalis, 2019. "Forecasting tourist arrivals at attractions: Search engine empowered methodologies," Tourism Economics, , vol. 25(3), pages 425-447, May.
    12. Kulshrestha, Anurag & Krishnaswamy, Venkataraghavan & Sharma, Mayank, 2020. "Bayesian BILSTM approach for tourism demand forecasting," Annals of Tourism Research, Elsevier, vol. 83(C).
    13. Liu, Yuan-Yuan & Tseng, Fang-Mei & Tseng, Yi-Heng, 2018. "Big Data analytics for forecasting tourism destination arrivals with the applied Vector Autoregression model," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 123-134.
    14. Wang, Chen & Chu, Zhongzhu & Gu, Wei, 2021. "Assessing the role of public attention in China's wastewater treatment: A spatial perspective," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
    15. Yang, Yang & Fan, Yawen & Jiang, Lan & Liu, Xiaohui, 2022. "Search query and tourism forecasting during the pandemic: When and where can digital footprints be helpful as predictors?," Annals of Tourism Research, Elsevier, vol. 93(C).
    16. Chumnumpan, Pattarin & Shi, Xiaohui, 2019. "Understanding new products’ market performance using Google Trends," Australasian marketing journal, Elsevier, vol. 27(2), pages 91-103.
    17. Eden Xiaoying Jiao & Jason Li Chen, 2019. "Tourism forecasting: A review of methodological developments over the last decade," Tourism Economics, , vol. 25(3), pages 469-492, May.
    18. Gu, Fu & Wang, Jiqiang & Guo, Jianfeng & Fan, Ying, 2020. "Dynamic linkages between international oil price, plastic stock index and recycle plastic markets in China," International Review of Economics & Finance, Elsevier, vol. 68(C), pages 167-179.
    19. Chuan Zhang & Ao‐Yun Hu & Yu‐Xin Tian, 2023. "Daily tourism forecasting through a novel method based on principal component analysis, grey wolf optimizer, and extreme learning machine," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 2121-2138, December.
    20. Li, Hengyun & Gao, Huicai & Song, Haiyan, 2023. "Tourism forecasting with granular sentiment analysis," Annals of Tourism Research, Elsevier, vol. 103(C).
    21. Chuan Zhang & Yu-Xin Tian & Ling-Wei Fan, 2020. "Improving the Bass model’s predictive power through online reviews, search traffic and macroeconomic data," Annals of Operations Research, Springer, vol. 295(2), pages 881-922, December.
    22. Gang Xie & Xin Li & Yatong Qian & Shouyang Wang, 2021. "Forecasting tourism demand with KPCA-based web search indexes," Tourism Economics, , vol. 27(4), pages 721-743, June.

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