Urban tourist volume forecasting using internet search trends and deep learning methods

Accurate forecasting of tourist arrivals in major urban destinations is critical for optimizing tourism resource allocation and formulating data-driven marketing strategies. To address this need, this study presents a novel prediction framework that integrates deep learning methodologies with online search behavior data. Specifically, we propose the DTN (Dynamic Tourism Network) model, which combines Disentangled Shape and Time series Normalization (Dish-TS) with Temporal Convolutional Networks (TCN), and utilizes Baidu Index data as a key indicator of online search trends to predict tourist arrivals in Sanya, China. Empirical validation across multiple evaluation metrics demonstrates that the DTN model consistently surpasses conventional deep learning approaches, achieving statistically significant improvements in predictive accuracy for tourist volume estimation. This advancement provides a robust analytical foundation for real‑time tourism demand forecasting in destination management systems. Notably, the proposed method has been evaluated only on a popular urban tourist destination with pronounced seasonality and available Baidu Index data; its applicability to other destination types or regions where different search engines dominate therefore requires further validation.