基于小波分解和长短时记忆网络的地铁进站量短时预测

针对城市地铁车站进站客流量短时预测问题，提出了小波分解和长短时记忆网络(LSTM)相结合的组合预测模型，小波分解和重构可以有效处理数据的波动性，长短时记忆网络可以学习时序信息。以北京地铁西直门站为实例，实现了组合模型对进站量的预测，发现本方法能够得到比较准确的预测效果，平均绝对百分误差为5.48%，与单独使用LSTM和经验模态分解与LSTM结合这两种方法相比分别下降了8.59%和2.94%，表明本方法有更好的预测精度。

Short-term forecasting of the subway passenger flow based on wavelet decomposition and long short-term memory network

In this study,we develop a two-stage passenger flow forecasting model by combining wavelet decomposition and the long short-term memory (LSTM) neural network.Further,the wavelet decomposition and reconstruction can effectively process the fluctuating data,and the LSTM network may be used to learn the time series data.The passenger flow was evaluated by the developed model based on a prediction case study in the Xizhimen subway station in Beijing.In this study,this method was observed to obtain accurate predication results;the mean absolute percentage error was 5.48% and decreased by 8.59% and 2.94%,respectively,when compared with those associated with the single use of LSTM and the use of a combination of empirical mode decomposition with LSTM.Therefore,the proposed method is observed to exhibit improved forecasting accuracy.