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Multi-attraction, hourly tourism demand forecasting

Zheng, W.; Huang, L.; Lin, Z.

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W. Zheng

L. Huang


Forecasting tourist demand for multiple tourist attractions on an hourly basis provides important insights for effective and efficient management, such as staffing and resource optimization. However, existing forecasting models are not well equipped to hand the hourly data, which is dynamic and nonlinear. This study develops an improved, artificial intelligent-based model, known as Correlated Time Series oriented Long Short-Term Memory with Attention Mechanism, to solve this problem. The validity of the model is verified through a forecasting exercise for 77 attractions in Beijing, China. The results show that our model significantly outperforms the baseline models. The study advances the tourism demand forecasting literature and offers practical implications for resource optimization while enhancing staff and customer satisfaction.


Zheng, W., Huang, L., & Lin, Z. (2021). Multi-attraction, hourly tourism demand forecasting. Annals of Tourism Research, 90, Article 103271.

Journal Article Type Article
Acceptance Date Jun 23, 2021
Publication Date 2021-09
Deposit Date Jun 25, 2021
Publicly Available Date Jul 7, 2023
Journal Annals of Tourism Research
Print ISSN 0160-7383
Publisher Elsevier Masson
Peer Reviewed Peer Reviewed
Volume 90
Article Number 103271
Public URL


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