基于循环神经网络的城市轨道交通短时客流预测

Short-term passenger flow forecasting of urban rail transit based on recurrent neural network

为更好地预测城市轨道交通的短时客流情况,提出了基于循环神经网络模型的预测方法。首先,针对轨道交通进出站客流数据,利用Pearson相关系数确定短时客流影响因素;然后,改进K-means聚类算法划分高、中、低客流量三类轨道站点,分析客流时空分布规律及高峰时间段;最后,采用分别基于长短时记忆神经网络(LSTM)与门控循环单元(GRU)的短时客流预测方法,预测不同类型站点在不同时段的客流。实验结果表明:5 min为预测的最佳时间粒度,在此时间粒度下GRU模型整体性能优于LSTM模型。

In order to better predict the short-term passenger flow of urban rail transit, a prediction method based on the recurrent neural network model is proposed. Firstly, based on the actual passenger flow data of each rail transit station, the Pearson correlation coefficient is used to determine the influencing factors of short-term passenger flow of rail transit, such as the weather conditions, historical passenger flow, whether it is a peak time period, whether it is a working day, etc. Secondly, the K-means clustering algorithm is used to classify rail transit stations into three types: high, medium, and low passenger flow stations. Then the distribution of passenger flow for each station type in time and space is analyzed, to determine the peak period of passenger flow for each station type. Finally, two urban rail transit short-term passenger flow prediction methods based on long-short term memory neural network(LSTM) and gated recurrent unit(GRU) respectively are proposed to predict the passenger flow of each type of station in different time period. The experimental results show that 5 min is the best time granularity for short-term passenger flow prediction of the two models. In this time granularity, the overall performance of the GRU model is better than the LSTM model.