基于卷积长短时记忆神经网络的城市轨道交通短时客流预测  

Metro short-term traffic flow prediction with ConvLSTM

作　　者：王秋雯 陈彦如[1] 刘媛春[2] WANG Qiu-wen;CHEN Yan-ru;LIU Yuan-chun(School of Economics and Management,Southwest Jiaotong University,Chengdu 610031,China;School of Software,Jiangxi Normal University,Nanchang 330022,China)

机构地区：[1]西南交通大学经济管理学院,成都610031 [2]江西师范大学软件学院,南昌330022

出　　处：《控制与决策》2021年第11期2760-2770,共11页Control and Decision

基　　金：国家重点研发计划项目(2018YFB1601402)。

摘　　要：我国城市轨道交通正处在快速发展阶段,城轨交通短时客流预测对保障运营安全、优化线网结构,进而构建智慧城市具有重要意义.城轨短时客流除了具有周期性、随机性等时间特征之外,跨时段的断面客流具有相似性,并且相邻站点客流之间存在空间联系.对此,充分考虑以上城轨短时客流的时空特征,基于卷积长短时记忆神经网络(ConvLSTM)与自适应k-means聚类算法,提出城轨短时客流预测的深度学习模型k-ConvLSTM,并通过实验对模型关键参数进行寻优;同时,基于深圳市地铁IC卡的真实客流数据对模型的有效性进行检验.结果表明,kConvLSTM在均方根误差、绝对误差均值、绝对误差百分比方面,均优于仅考虑时空特征的深度学习模型--卷积网络(CNN)与长短时记忆网络(LSTM)的并行混合模型和ConvLSTM内嵌式网络模型,仅考虑时间特征的深度学习模型--LSTM网络和双向长短时记忆网络(Bi-LSTM),以及浅层机器学习模型--BP神经网络和支持向量回归模型(SVR).China’s urban rail transit is developing rapidly.Short-term passenger flow prediction is of great significance for operational safety,network optimization,and then smart city building.While the urban rail passenger flow is cyclical and random in the aspect of temporal characteristics,passenger flows in certain time slots are similar and passenger flows at adjacent stations are spatially correlated.Considering the above spatiotemporal characteristics,this research proposes a deep learning model,named k-means-ConvLSTM(k-ConvLSTM)for urban rail short-term passenger flow prediction based on the ConvLSTM and the adaptive k-means clustering algorithm.Experiments are designed to optimize the key parameters of the model.Also,in order to examine the performance of the proposed model,abundant experiments are conducted based on the real passenger flow data of the Shenzhen Metro IC card.The results show that proposed kConvLSTM model performs better than deep learning models that only consider spatiotemporal characteristics—parallel architecture comprising the convolutional neural networks(CNN)and the long short-term memory network(LSTM),the ConvLSTM,and deep learning models that only consider temporal characteristics—the LSTM and the bi-directional long short-term memory network(Bi-LSTM),and shallow learning models—the back propagation neural network(BPNN)and the support vector regression model(SVR),in terms of root mean square errors,mean absolute errors and mean absolute percentage errors.

关 键 词：城轨交通短时客流 时空特征 跨时段客流聚类 卷积长短时记忆神经网络 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]