Tourism Demand Forecasting Based on a Hybrid Temporal Neural Network Model for Sustainable Tourism

This paper introduces a novel hybrid forecasting model for tourism demand that combines Bidirectional Long Short-Term Memory (BiLSTM) and Transformer networks, addressing the challenge of capturing both short-term fluctuations and long-term trends in complex tourism data. Unlike traditional models, such as ARIMA, which often struggle with nonlinear patterns, our hybrid approach leverages the sequential learning capabilities of BiLSTM and the self-attention mechanism of the Transformer to effectively model intricate temporal dependencies. Our experiments on Thailand’s domestic tourism data showed that the hybrid model outperformed traditional methods and standalone deep learning models, where it achieved a 12% reduction in the RMSE, a 15% reduction in the MAE, and a 10% increase in the R2. This improved accuracy offers significant practical benefits for sustainable tourism, enabling policymakers and tourism managers to optimize resource allocation, anticipate peak season demand, and develop strategies to mitigate over-tourism. The model’s robustness and adaptability make it a valuable tool for data-driven decision-making in the tourism sector.