Forecasting hourly attraction tourist volume with search engine and social media data for decision support |
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| Institution: | 1. School of Economics and Management, Beijing Jiaotong University, Beijing, China;2. School of Information Technology and Management, University of International Business and Economics, Beijing, China;1. Smart Tourism Eudcation Platform (STEP), College of Hotel and Tourism Management, Kyung Hee University, South Korea;2. Department of Tourism, Hospitality and Event Management, University of Florida, USA;3. Information Systems Institute, Leipzig University, Germany |
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| Abstract: | Developing a tourism forecasting function in decision support systems has become critical for businesses and governments. The existing forecasting models considering spatial relations contain insufficient information, and the spatial aggregation of simple tourist volume series limits the forecasting accuracy. Using human-generated search engines and social media data has the potential to address this issue. In this paper, a spatial aggregation-based multimodal deep learning method for hourly attraction tourist volume forecasting is developed. The model first extracts the daily features of attractions from search engine data; then mines the spatial aggregation relationships in social media data and multi-attraction tourist volume data. Finally, the model fuses hourly features with daily features to make forecasting. The model is tested using a dataset containing several attractions with real-time tourist volume at 15-minute intervals from November 27, 2018, to March 18, 2019, in Beijing. And the empirical and Diebold-Mariano test results demonstrate that the proposed framework can outperform state-of-the-art baseline models with statistically significant improvements at the 1% level. Compared with the best baseline model, the MAPE values are reduced by 50.0% and 27.3% in 4A attractions and 5A attractions, respectively; and the RMSE values are reduced by 48.3% and 26.1%, respectively. The method in this paper can be used as a function embedded in the decision support system to help multi-department collaboration. |
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