基于 LBS 和深度学习的旅游景区客流量的高时频预测

In order to achieve accurate high-frequency forecasts of tourist flow for tourist attractions, this study proposes a forecasting method based on LBS and deep learning techniques. This method generates spatial-temporally controllable forecasts by converting the LBS data and using the core model — Deep Bidirectional Gated Recurrent Unit (DBi-GRU) model — built based on Bidirectional Recurrent Neural Network and GRU algorithms. To test the performance of our proposed method, we take the Shenzhen Dameisha Waterfront Park as an example, and three analysis methods including fitting curves, error criteria, and DM tests are used to test the forecasting performance of our DBi-GRU model. Additionally, five other deep learning models are set as reference models to compare with our model. The experimental results show that, first, DBi-GRU model proposed in this study has ideal forecasting performance in high-frequency forecast of tourist flow for tourist attractions and yields highly accurate forecasts in peak periods of tourist flow, and its performance is much better than the other deep learning models. Second, Bidirectional Recurrent Neural Network based models, particularly the Bidirectional LSTM based model, generally provide better performance than conventional Recurrent Neural Network based models. Though the forecast accuracy of the Bidirectional LSTM based model is not as high as DBi-GRU model, there is no significant difference between their model capability. Third, using the same network parameters, GRU algorithm has higher forecast accuracy than LSTM and RNN algorithms which are used by previous researchers. This study develops a new method for high-frequency tourist flow forecasting, and the high-frequency information forecasted in this study provides information support for management tasks of tourist attraction such as crowd control, service arrangement, etc..