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A multi-source behavioral data framework for interpretable urban tourism forecasting.

Zirui Nie1, Zhonghua Nie2

  • 1Graduate School of Urban Environmental Sciences, Tokyo Metropolitan University, Tokyo, 192-0364, Japan.

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Summary

This study introduces a hybrid forecasting model using Long Short-Term Memory (LSTM) and Graph Neural Networks (GNNs) to predict urban tourism demand. The model accurately forecasts demand by integrating diverse data sources, enhancing smart tourism management.

Keywords:
Deep learningEmotion analysisGraph neural networkMulti-source behavioral dataStructural equation modelingTourism forecasting

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Area of Science:

  • Data Science
  • Urban Planning
  • Tourism Management

Background:

  • Urban tourism demand prediction is complex due to volatility and intricate behavioral patterns.
  • Existing forecasting methods struggle with the multifaceted nature of tourism data.

Purpose of the Study:

  • To develop a hybrid forecasting framework for accurate and robust urban tourism demand prediction.
  • To leverage multi-source behavioral data for enhanced forecasting capabilities.

Main Methods:

  • Integration of Long Short-Term Memory (LSTM) networks with Graph Neural Networks (GNNs).
  • Utilized multi-source data: social media sentiment, online travel agency (OTA) activity, meteorological data, and mobile signaling records.
  • Dataset collected from eight Chinese cities (2022-2024).

Main Results:

  • Achieved a Mean Absolute Percentage Error (MAPE) of 6.31% and 83.7% trend accuracy.
  • The hybrid model outperformed single-model benchmarks.
  • Sentiment indices, user engagement, and holiday effects identified as key determinants of accuracy.

Conclusions:

  • The proposed framework offers a scalable and interpretable intelligent forecasting paradigm for smart tourism.
  • Data heterogeneity and model adaptability are crucial for enhancing predictive performance in urban tourism.