IDEAS home Printed from https://ideas.repec.org/a/sae/toueco/v30y2024i2p361-388.html
   My bibliography  Save this article

Collaborative forecasting of tourism demand for multiple tourist attractions with spatial dependence: A combined deep learning model

Author

Listed:
  • Jian-Wu Bi
  • Tian-Yu Han
  • Yanbo Yao

Abstract

To forecast the tourism demand across a set of tourist attractions with spatial dependence, a new model is proposed, which has three stages: tourist attraction selection, base predictor generation, and base predictor combination. In stage 1, a method for selecting associated attractions based on multi-dimensional scaling is used to determine the strength of the spatial dependence between each pair of attractions. In stage 2, a hybrid base predictor based on LSTM networks and Autoregressive model is developed, where the LSTM networks are used to capture the spatial dependence among attractions, and the Autoregressive model is used capture the scale of tourist volume at each attraction. In stage 3, a strategy for combining these base predictors is proposed; it can alleviate the overfitting problem of LSTM and improve the stability of forecasts. Finally, the superiority of the model is verified through the data on tourist volumes at 77 attractions in Beijing.

Suggested Citation

  • 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.
    • Handle: RePEc:sae:toueco:v:30:y:2024:i:2:p:361-388
    DOI: 10.1177/13548166231153908
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/13548166231153908
    Download Restriction: no
    File URL: https://libkey.io/10.1177/13548166231153908?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Silva, Emmanuel Sirimal & Hassani, Hossein & Heravi, Saeed & Huang, Xu, 2019. "Forecasting tourism demand with denoised neural networks," Annals of Tourism Research, Elsevier, vol. 74(C), pages 134-154.
    2. Li, Hengyun & Hu, Mingming & Li, Gang, 2020. "Forecasting tourism demand with multisource big data," Annals of Tourism Research, Elsevier, vol. 83(C).
    3. Divino, Jose Angelo & McAleer, Michael, 2010. "Modelling and forecasting daily international mass tourism to Peru," Tourism Management, Elsevier, vol. 31(6), pages 846-854.
    4. Marrocu, Emanuela & Paci, Raffaele, 2013. "Different tourists to different destinations. Evidence from spatial interaction models," Tourism Management, Elsevier, vol. 39(C), pages 71-83.
    5. Christina Beneki & Bruno Eeckels & Costas Leon, 2012. "Signal Extraction and Forecasting of the UK Tourism Income Time Series: A Singular Spectrum Analysis Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 31(5), pages 391-400, August.
    6. Morley, Clive & Rosselló, Jaume & Santana-Gallego, Maria, 2014. "Gravity models for tourism demand: theory and use," Annals of Tourism Research, Elsevier, vol. 48(C), pages 1-10.
    7. Jiao, Xiaoying & Li, Gang & Chen, Jason Li, 2020. "Forecasting international tourism demand: a local spatiotemporal model," Annals of Tourism Research, Elsevier, vol. 83(C).
    8. Xie, Gang & Qian, Yatong & Wang, Shouyang, 2020. "A decomposition-ensemble approach for tourism forecasting," Annals of Tourism Research, Elsevier, vol. 81(C).
    9. Haiqi Li & Sung Yong Park & Joo Hwan Seo, 2011. "Quantile Elasticity of International Tourism Demand for South Korea Using the Quantile Autoregressive Distributed Lag Model," Tourism Economics, , vol. 17(5), pages 997-1015, October.
    10. Lindsay W. Turner & Stephen F. Witt, 2001. "Forecasting Tourism Using Univariate and Multivariate Structural Time Series Models," Tourism Economics, , vol. 7(2), pages 135-147, June.
    11. Bi, Jian-Wu & Liu, Yang & Li, Hui, 2020. "Daily tourism volume forecasting for tourist attractions," Annals of Tourism Research, Elsevier, vol. 83(C).
    12. Athanasopoulos, George & Hyndman, Rob J. & Song, Haiyan & Wu, Doris C., 2011. "The tourism forecasting competition," International Journal of Forecasting, Elsevier, vol. 27(3), pages 822-844.
    13. Song, Haiyan & Qiu, Richard T.R. & Park, Jinah, 2019. "A review of research on tourism demand forecasting," Annals of Tourism Research, Elsevier, vol. 75(C), pages 338-362.
    14. Garcia-Ferrer, Antonio & Queralt, Ricardo A., 1997. "A note on forecasting international tourism demand in Spain," International Journal of Forecasting, Elsevier, vol. 13(4), pages 539-549, December.
    15. Zheng, Weimin & Huang, Liyao & Lin, Zhibin, 2021. "Multi-attraction, hourly tourism demand forecasting," Annals of Tourism Research, Elsevier, vol. 90(C).
    16. Zhang, Yishuo & Li, Gang & Muskat, Birgit & Law, Rob & Yang, Yating, 2020. "Group pooling for deep tourism demand forecasting," Annals of Tourism Research, Elsevier, vol. 82(C).
    17. Law, Rob & Li, Gang & Fong, Davis Ka Chio & Han, Xin, 2019. "Tourism demand forecasting: A deep learning approach," Annals of Tourism Research, Elsevier, vol. 75(C), pages 410-423.
    18. 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.
    19. Athanasopoulos, George & Ahmed, Roman A. & Hyndman, Rob J., 2009. "Hierarchical forecasts for Australian domestic tourism," International Journal of Forecasting, Elsevier, vol. 25(1), pages 146-166.
    20. Christine Lim & Michael McAleer, 2000. "A seasonal analysis of Asian tourist arrivals to Australia," Applied Economics, Taylor & Francis Journals, vol. 32(4), pages 499-509.
    21. Cao, Zheng & Li, Gang & Song, Haiyan, 2017. "Modelling the interdependence of tourism demand: The global vector autoregressive approach," Annals of Tourism Research, Elsevier, vol. 67(C), pages 1-13.
    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).
    23. Saayman, Andrea & Viljoen, Armand & Saayman, Melville, 2018. "Africa’s outbound tourism: An Almost Ideal Demand System perspective," Annals of Tourism Research, Elsevier, vol. 73(C), pages 141-158.
    24. 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.
    25. Yang, Yang & Zhang, Honglei, 2019. "Spatial-temporal forecasting of tourism demand," Annals of Tourism Research, Elsevier, vol. 75(C), pages 106-119.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Cheng & Zheng, Weimin & Ge, Peng, 2022. "Tourism demand forecasting with spatiotemporal features," Annals of Tourism Research, Elsevier, vol. 94(C).
    2. 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.
    3. Zheng, Weimin & Huang, Liyao & Lin, Zhibin, 2021. "Multi-attraction, hourly tourism demand forecasting," Annals of Tourism Research, Elsevier, vol. 90(C).
    4. Xu, Shilin & Liu, Yang & Jin, Chun, 2023. "Forecasting daily tourism demand with multiple factors," Annals of Tourism Research, Elsevier, vol. 103(C).
    5. 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).
    6. Song, Haiyan & Qiu, Richard T.R. & Park, Jinah, 2019. "A review of research on tourism demand forecasting," Annals of Tourism Research, Elsevier, vol. 75(C), pages 338-362.
    7. Bi, Jian-Wu & Liu, Yang & Li, Hui, 2020. "Daily tourism volume forecasting for tourist attractions," Annals of Tourism Research, Elsevier, vol. 83(C).
    8. 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).
    9. Jian-Wu Bi & Tian-Yu Han & Hui Li, 2022. "International tourism demand forecasting with machine learning models: The power of the number of lagged inputs," Tourism Economics, , vol. 28(3), pages 621-645, May.
    10. Zhang, Yishuo & Li, Gang & Muskat, Birgit & Vu, Huy Quan & Law, Rob, 2021. "Predictivity of tourism demand data," Annals of Tourism Research, Elsevier, vol. 89(C).
    11. 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.
    12. Guan, Bo & Silva, Emmanuel Sirimal & Hassani, Hossein & Heravi, Saeed, 2022. "Forecasting tourism growth with State-Dependent Models," Annals of Tourism Research, Elsevier, vol. 94(C).
    13. 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.
    14. Salvatore Costantino & Maria Francesca Cracolici & J. Paul Elhorst, 2023. "A spatial origin‐destination approach for the analysis of local tourism demand in Italy," Papers in Regional Science, Wiley Blackwell, vol. 102(2), pages 393-419, April.
    15. Gunter, Ulrich & Zekan, Bozana, 2021. "Forecasting air passenger numbers with a GVAR model," Annals of Tourism Research, Elsevier, vol. 89(C).
    16. Kaijian He & Don Wu & Yingchao Zou, 2022. "Tourist Arrival Forecasting Using Multiscale Mode Learning Model," Mathematics, MDPI, vol. 10(16), pages 1-12, August.
    17. 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.
    18. 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.
    19. Zhang, Yishuo & Li, Gang & Muskat, Birgit & Law, Rob & Yang, Yating, 2020. "Group pooling for deep tourism demand forecasting," Annals of Tourism Research, Elsevier, vol. 82(C).
    20. Ulrich Gunter, 2021. "Improving Hotel Room Demand Forecasts for Vienna across Hotel Classes and Forecast Horizons: Single Models and Combination Techniques Based on Encompassing Tests," Forecasting, MDPI, vol. 3(4), pages 1-36, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:toueco:v:30:y:2024:i:2:p:361-388. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.