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Forecasting tourist arrivals at attractions: Search engine empowered methodologies

Author

Listed:
  • Katerina Volchek

    (The Hong Kong Polytechnic University, Hong Kong)

  • Anyu Liu

    (University of Surrey, UK)

  • Haiyan Song

    (The Hong Kong Polytechnic University, Hong Kong)

  • Dimitrios Buhalis

    (Bournemouth University, UK)

Abstract

Tourist decision to visit attractions is a complex process influenced by multiple factors of individual context. This study investigates how the accuracy of tourism demand forecasting can be improved at the micro level. The number of visits to five London museums is forecast and the predictive powers of Naïve I, seasonal Naïve, seasonal autoregressive moving average, seasonal autoregressive moving average with explanatory variables, SARMAX-mixed frequency data sampling and artificial neural network models are compared. The empirical findings extend understanding of different types of data and forecasting algorithms to the level of specific attractions. Introducing the Google Trends index on pure time-series models enhances the forecasts of the volume of arrivals to attractions. However, none of the applied models outperforms the others in all situations. Different models’ forecasting accuracy varies for short- and long-term demand predictions. The application of higher frequency search query data allows for the generation of weekly predictions, which are essential for attraction- and destination-level planning.

Suggested Citation

  • 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.
    • Handle: RePEc:sae:toueco:v:25:y:2019:i:3:p:425-447
    DOI: 10.1177/1354816618811558
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    References listed on IDEAS

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    2. Li, Hengyun & Hu, Mingming & Li, Gang, 2020. "Forecasting tourism demand with multisource big data," Annals of Tourism Research, Elsevier, vol. 83(C).
    3. 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).
    4. Siqi Emily Lu & Brent Moyle & Sacha Reid & Elaine Yang & Biqiang Liu, 2023. "Technology and museum visitor experiences: a four stage model of evolution," Information Technology & Tourism, Springer, vol. 25(2), pages 151-174, June.
    5. Edmond H. C. Wu & Jihao Hu & Rui Chen, 2022. "Monitoring and forecasting COVID-19 impacts on hotel occupancy rates with daily visitor arrivals and search queries," Current Issues in Tourism, Taylor & Francis Journals, vol. 25(3), pages 490-507, February.
    6. Tomas Havranek & Ayaz Zeynalov, 2021. "Forecasting tourist arrivals: Google Trends meets mixed-frequency data," Tourism Economics, , vol. 27(1), pages 129-148, February.
    7. Tarmo Kalvet & Maarja Olesk & Marek Tiits & Janika Raun, 2020. "Innovative Tools for Tourism and Cultural Tourism Impact Assessment," Sustainability, MDPI, vol. 12(18), pages 1-30, September.
    8. Fengzhi Sun & Zihan Li & Mingzhi Xu & Mingcan Han, 2024. "New Changes in Chinese Urban Tourism Pattern under the Impact of COVID-19 Pandemic: Based on Internet Attention," Sustainability, MDPI, vol. 16(14), pages 1-22, July.
    9. Doris Chenguang Wu & Shiteng Zhong & Richard T R Qiu & Ji Wu, 2022. "Are customer reviews just reviews? Hotel forecasting using sentiment analysis," Tourism Economics, , vol. 28(3), pages 795-816, May.
    10. Jackson de Souza & Luiz Mendes-Filho & Dimitrios Buhalis, 2020. "Evaluating the effectiveness of tourist advertising to improve the competitiveness of destinations," Tourism Economics, , vol. 26(6), pages 1001-1020, September.
    11. 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.
    12. Archana Yadav & Biswajit Patra & Tanmay Basu, 2024. "Modeling International Tourist Arrivals: An NLP Perspective," SN Operations Research Forum, Springer, vol. 5(4), pages 1-19, December.
    13. Jessica Bollenbach & Stefan Neubig & Andreas Hein & Robert Keller & Helmut Krcmar, 2024. "Enabling active visitor management: local, short-term occupancy prediction at a touristic point of interest," Information Technology & Tourism, Springer, vol. 26(3), pages 521-552, September.
    14. Yi-Chung Hu, 2023. "Tourism combination forecasting using a dynamic weighting strategy with change-point analysis," Current Issues in Tourism, Taylor & Francis Journals, vol. 26(14), pages 2357-2374, July.
    15. Jiao, Xiaoying & Chen, Jason Li & Li, Gang, 2021. "Forecasting tourism demand: Developing a general nesting spatiotemporal model," Annals of Tourism Research, Elsevier, vol. 90(C).
    16. 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.
    17. 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.
    18. Han Liu & Yongjing Wang & Haiyan Song & Ying Liu, 2023. "Measuring tourism demand nowcasting performance using a monotonicity test," Tourism Economics, , vol. 29(5), pages 1302-1327, August.
    19. Ke Xu & Junli Zhang & Junhao Huang & Hongbo Tan & Xiuli Jing & Tianxiang Zheng, 2024. "Forecasting Visitor Arrivals at Tourist Attractions: A Time Series Framework with the N-BEATS for Sustainable Tourism," Sustainability, MDPI, vol. 16(18), pages 1-31, September.

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