计算机科学

计算机科学 ›› 2025, Vol. 52 ›› Issue (10): 90-97.doi: 10.11896/jsjkx.241000045

• 数据库&大数据&数据科学 • 上一篇    下一篇

用于交通预测的时空传播图神经网络

雷二帅, 禹素萍, 范红, 许武军   

  1. 东华大学信息科学与技术学院 上海 201620
  • 收稿日期:2024-10-10 修回日期:2025-03-15 出版日期:2025-10-15 发布日期:2025-10-14
  • 通讯作者: 禹素萍(201027987@qq.com)
  • 作者简介:(2222051@mail.dhu.edu.cn)

Spatial-Temporal Propagation Graph Neural Network for Traffic Prediction

LEI Ershuai, YU Suping, FAN Hong, XU Wujun   

  1. College of Information Science and Technology,Donghua University,Shanghai 201620,China
  • Received:2024-10-10 Revised:2025-03-15 Online:2025-10-15 Published:2025-10-14
  • About author:LEI Ershuai,born in 2001,MS candidate,is a member of CCF(No.V0832G).His main research interests include spatial-temporal data mining and traffic forecasting.
    YU Suping,born in 1977,Ph.D,asso-ciate professor,master supervisor.Her main research interest is multimedia information acquisition and processing.

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摘要： 在交通预测领域,数据间存在着复杂且长程的时空关系,现有的图结构未能充分挖掘数据间隐含的时空关系。针对上述问题,在MTGNN的基础上进行了一系列的改进,提出了一种用于交通预测的时空传播图神经网络(Spatial-Temporal Propagation Graph Neural Network,STPGNN)。首先,用多尺度卷积模块来捕获不同尺度的时序信息,并通过特征融合模块进行融合,以捕获复杂的时序信息。其次,在MTGNN单向自适应图结构的基础上,设计并加入了双向图学习层,以深入挖掘并利用数据间隐含的双向空间关系。接着,针对网络层级间的信息传递,提出一种新的层级间信息传递方法,将每层中多尺度的时序信息依次传递至下一层,以更好地挖掘复杂且长程的时空关系。最后,根据网络各层级的时间与空间信息,通过输出卷积获得预测结果。在METR-LA,PEMS-BAY和NE-BJ等数据集上进行了实验验证,结果表明,STPGNN能够有效提高预测精度,在常用的3个指标上优于一些现有的方法,尤其是在进行更长时的预测时,表现更为出色。

关键词: 交通预测, 信息传递, 图神经网络, 多尺度卷积

Abstract: In the field of traffic prediction,there are complex and long-range spatial-temporal relationships between data.The existing graph structures fail to fully explore the implicit spatial-temporal relationships between data.To address the above problems,this study makes a series of improvements on the multivariate time series forecasting with graph neural networks(MTGNN),and proposes a spatial-temporal propagation graph neural network(STPGNN) for traffic prediction.Firstly,it captures information at different time scales through the multi-scale convolution module,and uses the feature fusion module to fuse to capture complex temporal information.After that,on the basis of the unidirectional adaptive graph structure of MTGNN,a bidirectional graph learning layer is designed and added to deeply explore and utilize the implicit bidirectional spatial relationship between data.Next,for the information transfer between network layers,a new information transfer method is proposed,which transmits the multi-scale time information in each layer to the next layer in turn,so as to better explore the complex and long-range spatial-temporal relationship.Finally,according to the temporal and spatial information of each level of the network,the prediction results are obtained by output convolution.Experiments are carried out on datasets such as METR-LA,PEMS-BAY and NE-BJ.The results show that STPGNN can effectively improve prediction accuracy and is better than some existing methods in three commonly indicators.This is especially true when it comes to longer-term forecasts.

Key words: Traffic prediction,Information transfer,Graph neural network,Multi-scale convolution

中图分类号: 

  • TP183
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