High-Frequency Tourist Flow Forecasting with Gated Recurrent Units and Attention Mechanisms

Changlong Wang, Hehong Xue and Suqi Wang

SAGE Open, 2025, vol. 15, issue 3, 21582440251358303

Abstract: Accurate prediction of tourist flow is essential for effective crowd management and service optimization in popular tourist destinations, especially during peak hours and high-frequency periods. This study proposes a novel deep learning model—BiGRU-Attn, which integrates Gated Recurrent Units (GRU), Attention Mechanisms, and Bidirectional Recurrent Neural Networks (BiRNN) to capture complex temporal dependencies in sequential data. The model is evaluated using real-world, high-frequency passenger flow data from Fuzhou National Forest Park, a major sport tourism site in China. Comparative experiments against five baseline models—including both bidirectional and conventional recurrent architectures—demonstrate the superior predictive performance of BiGRU-Attn. It achieved the highest R 2 (.948) and the lowest RMSE (218.986), MAE (131.914), and MAPE (0.340) in 72-hr forecasts. During peak-hour periods (12:00–18:00), BiGRU-Attn maintained an R 2 above .9, consistently outperforming all other models. Notably, bidirectional models (BiGRU-Attn and DBi-LSTM) showed clear advantages over their conventional counterparts, particularly in capturing short-term fluctuations. These findings underscore the potential of BiGRU-Attn to support smart tourism applications, enabling more accurate real-time visitor flow forecasting, proactive resource planning, and enhanced visitor experience management in dynamic environments.

Keywords: tourist flow prediction; recurrent neural networks; attention mechanisms; forecasting models (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:sae:sagope:v:15:y:2025:i:3:p:21582440251358303

DOI: 10.1177/21582440251358303

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