基于时序知识图谱嵌入的短期地铁客流量预测  

Short-term Subway Passenger Flow Forecasting Based on Graphical Embedding of Temporal Knowledge

作　　者：毛慧慧 赵小乐 杜圣东[1] 滕飞[1] 李天瑞[1] MAO Huihui;ZHAO Xiaole;DU Shengdong;TENG Fei;LI Tianrui(School of Computing and Artificial Intelligence,Southwest Jiaotong University,Chengdu 611756,China)

机构地区：[1]西南交通大学计算机与人工智能学院,成都611756

出　　处：《计算机科学》2023年第7期213-220,共8页Computer Science

基　　金：国家重点研发计划(2019YFB2101801)。

摘　　要：短期地铁客流量预测任务是城市智能地铁运营工作的重要组成部分,旨在预测未来短时间内地铁站点的客流量。针对现有方法未能充分利用站点的流入流出客流量信息的问题,提出了一种基于时序知识图谱嵌入(Temporal Knowledge Graph Embedding)结合残差网络(ResNet)和长短期记忆网络(LSTM)的短期地铁客流量预测方法,简称TKG-ResLSTM。首先,基于地铁客流量数据构建地铁客流量时序知识图谱,并使用时序知识图谱嵌入技术从中获取地铁站点的动态客流量模式。然后,将抽取出的动态客流量模式转换成动态相似矩阵,应用到基于深度学习的地铁客流量预测框架中完成地铁客流量预测任务。最后,利用北京地铁和A市地铁客流量数据集,分别在10 min, 15 min和30 min的时间间隔下进行实验评估。结果表明TKG-ResLSTM能够有效地抽取出地铁站点的动态客流量模式,在不使用外部信息的情况下,相比ResLSTM,TKG-ResLSTM在北京地铁数据集10min时间间隔下的预测均方根误差降低了0.41。Subway short-term passenger flow forecasting is an essential component in urban subway operation,and it aims to forecast the passenger flow of subway stations in a short time in the future.Aiming at the problem that the existing methods fail to make full use of the passenger flow information of stations,a short-term subway passenger flow forecasting method based on temporal knowledge graph embedding combined with residual network and long short-term memory network is proposed,which is called TKG-ResLSTM.First,we use subway passenger flow data to construct a temporal knowledge graph of subway passenger flow,and apply the graphical embedding of temporal knowledge to obtain the dynamic patterns of subway stations passenger flow.Then,the extracted dynamic patterns of passenger flow are converted into dynamic similarity matrices and applied to theforecasting architecture of subway passenger flow based on deep learning to complete the subway passenger flow forecasting task.Finally,experimental evaluations are carried out at time granularities of 10 min,15 min,and 30 min using the Beijing subway and city A subway passenger flow datasets,respectively.Experimental results show that TKG-ResLSTM can effectively extract the dynamic patterns of subway stations passenger flow.Without using external information,TKG-ResLSTM reduces the root mean square error of forecasting by 0.41 compared with ResLSTM in the time granularity of 10 min of the Beijing subway dataset.

关 键 词：深度学习 时序知识图谱 时空预测 动态嵌入 城市地铁网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]