Multi-attraction, hourly tourism demand forecasting

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

Abstract

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.

Citation

Zheng, W., Huang, L., & Lin, Z. (2021). Multi-attraction, hourly tourism demand forecasting. Annals of Tourism Research, 90, Article 103271. https://doi.org/10.1016/j.annals.2021.103271

• 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
• DOI: https://doi.org/10.1016/j.annals.2021.103271
• Public URL: https://durham-repository.worktribe.com/output/1240855