基于LBS和深度学习的旅游景区客流量的高时频预测

为实现精准的旅游景区客流量的高时频预测,本研究构建了一套基于LBS和深度学习模型的预测方法。此方法可通过对LBS数据的转换实现预测的空间范围与时频控制,并通过方法的核心模型——基于双向循环神经网络和GRU算法构建的深度双向GRU(DBi-GRU)模型完成预测。为检验方法的有效性,研究以深圳大梅沙海滨公园为例对方法进行实验测试。实验使用拟合曲线、误差指标及DM检验3种方法评估DBi-GRU模型的预测效果。此外,实验还设置了其他五种深度学习模型作为DBi-GRU的对照模型,测试基于不同深度学习算法的模型之间的预测水平差异。实验结果表明:(1)本研究提出的DBi-GRU模型在景区客流量高时频预测中具有理想的预测效果,在高峰时段的客流量预测方面也具有较高准确性,预测效果明显优于其他深度学习模型;(2)基于双向循环网络的模型的效果普遍优于基于常规循环网络的模型。尤其是基于双向LSTM算法的模型,虽然预测的准确度略逊色于DBi-GRU模型,但在模型性能上与其的差异并不显著;(3)在相同网络参数下,GRU算法较前人采用的LSTM和RNN算法有着更高的预测准确性。本研究为客流量预测领域的研究提供了一种...

High-temporal-frequency Forecast of Tourist Flow for Tourist Attraction based on LBS and Deep Learning

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..