Predicting Tourist Flow and Economic Impact Using a Transformers- Based Deep Learning Model with Multi-Modal Data Integration

Abstract

This study proposes a deep learning-based framework to predict tourist flow and assess its economic impact by integrating multi-modal data,including social media trends,OTA booking volumes,and economic indicators such as GDP and CPI.Using a Transformer model,we predict tourist flow with higher accuracy,achieving an MSE of 976 and an RMSE of 31.2.The model incorporates economic data to analyze its effect on tourism demand,showing that GDP growth and CPI significantly impact tourist behavior.Comparative analysis with traditional models like ARIMA and hybrid approaches(e.g.,CNN-LSTM)demonstrates that the Transformer model outperforms them in both prediction accuracy and computational efficiency.This methodology provides a novel approach to forecasting and economic analysis,offering valuable insights for policy-making and business strategy in tourism.