摘要
为丰富地铁内部换乘客流预测理论,更好地制定地铁运营计划,提出了一种基于时间序列分解方法(STL)与门控循环单元(GRU)的地铁换乘客流预测模型。该模型将预测过程分为3个阶段,第1阶段为原始地铁刷卡数据预处理,采用基于图的深度优先搜索算法识别乘客的出行路径,构建换乘客流时间序列;第2阶段运用STL时间序列分解算法将换乘客流时间序列转化为趋势量、周期量以及余量,并利用3σ原则对余量进行异常值的剔除与填充;第3阶段基于深度学习库Keras,完成GRU模型的搭建、训练及预测。以北京地铁西直门站的换乘客流数据为研究对象,对模型的有效性进行了验证,结果表明:与长短时记忆神经网络(LSTM)、门控循环单元、STL时间序列分解方法与长短时记忆神经网络组合模型(STL-LSTM)相比,STL-GRU组合预测模型可提升工作日(不含周五)、周五、休息日的换乘客流预测精度,预测结果的平均绝对百分比误差至少分别降低了2.3、1.36、6.42个百分点。
A metro transfer passenger flow prediction model was proposed based on the seasonal decomposition of time series by loess(STL)and Gated Recurrent Unit(GRU),in order to enrich the research on metro internal transfer passenger flow prediction and to better formulate the metro operation plan.The prediction process was divided into three stages by the model.In the first stage,the raw automatic fare collection(AFC)data are preprocessed,where the travel path of passengers is identified using the graph-based depth-first search algorithm and the transfer passenger flow time series are constructed.In the second stage,the transfer passenger flow time series are decomposed into the trend component,seasonal component and remainder component by the STL;while the outliers of remainder component are eliminated and filled using the 3σprinciple.In the third stage,the GRU model is built and the related training and prediction are processed through the deep learning library Keras.The model performance was validated with the passenger flow data of Xizhimen Station of Beijing metro.The result shows that,compared to the following 3 models which are long short-term memory neural network(LSTM),GRU and STL-LSTM model,the STL-GRU prediction model can improve the prediction accuracy of transfer passenger flow on weekdays(excluding Friday),Friday and weekends,and the mean absolute percentage errors of the prediction results can be reduced by at least 2.3%,1.36%,and 6.42%,respectively.
作者
赵建东
朱丹
刘佳欣
ZHAO Jiandong;ZHU Dan;LIU Jiaxin(School of Traffic and Transportation,Beijing Jiaotong University,Beijing 100044,China;Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport,Beijing Jiaotong University,Beijing 100044,China)
出处
《华南理工大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2022年第5期22-31,共10页
Journal of South China University of Technology(Natural Science Edition)
基金
国家重点研发计划项目(2019YFB1600200)
国家自然科学基金资助项目(71871011,71890972/71890970,71621001)。
