基于位置感知时空图卷积网络的交通流预测  

Predicting Traffic Flow Based on Position-Aware Spatial-Temporal Graph Convolution Network

作　　者：王子彤 李晨亮 Wang Zitong;Li Chenliang(Key Laboratory of Aerospace Information Security and Trusted Computing,Ministry of Education,Wuhan University,Wuhan 430072,China;School of Cyber Science and Engineering,Wuhan University,Wuhan 430072,China)

机构地区：[1]武汉大学空天信息安全与可信计算教育部重点实验室,武汉430072 [2]武汉大学国家网络安全学院,武汉430072

出　　处：《数据分析与知识发现》2025年第2期94-105,共12页Data Analysis and Knowledge Discovery

摘　　要：【目的】为更灵活地捕捉交通流数据的时空特征,实现更精确的多变量交通流预测,本文提出一种位置感知时空图卷积网络多变量时间序列预测模型PASTGCN。【方法】将交通流时序数据的空间位置和周期性时间特征表示为显式时空位置嵌入;在时空卷积结构基础上,在时间卷积网络中引入空间信息,实现空间感知的时间序列建模。考虑到空间关系的动态变化,使用静态和动态的双重图学习方法捕捉空间依赖。【结果】在两个真实世界交通流数据集上进行实验,结果表明PASTGCN模型能有效预测多变量交通流速,误差对比现有深度学习模型最好可降低1.59%。【局限】实验数据集有限,图学习方法及多次图卷积使算法的时间复杂度较高。【结论】PASTGCN模型能有效利用时空位置信息实现更准确的时空交通流预测。[Objective]To more flexibly capture the spatial-temporal features of traffic flow data and achieve more accurate multivariate traffic flow prediction,this paper proposes a Position-Aware Spatial-Temporal Graph Convolutional Network(PASTGCN).[Methods]First,the traffic data's spatial and periodic temporal position features are represented as explicit position embeddings.Then,based on the spatiotemporal convolutional structure,we incorporated spatial information into the temporal convolutional network for space-aware sequence modeling.Finally,we used static and dynamic dual graph learning methods to capture spatial dependencies.[Results]We conducted experiments on two real-world traffic flow datasets.The PASTGCN model effectively predicted multivariate traffic flows and reduced errors by up to 1.59%compared to existing deep learning models.[Limitations]The experimental datasets are limited,and the proposed graph learning method increased the time complexity.[Conclusions]The PASTGCN model can effectively utilize spatial-temporal position information to achieve more accuratetraffic flow prediction.

关 键 词：时间序列预测 图卷积网络 时间卷积网络 深度学习 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]