计算机工程与应用2025，Vol.61Issue(22)：1-19,19.DOI:10.3778/j.issn.1002-8331.2502-0225
面向交通流预测的时空图神经网络发展综述
Review on Development of Spatio-Temporal Graph Neural Networks for Traffic Flow Prediction
摘要
Abstract
In recent years,the application of deep learning in traffic flow prediction has attracted wide attention,especially the spatio-temporal graph neural network has achieved remarkable success in capturing spatio-temporal dependencies and predicting traffic characteristics.Although some reviews have examined the application of spatio-temporal graph neural networks,most of these studies focus primarily on application scenarios and fail to provide an in-depth analysis from the perspective of model design.Furthermore,a unified model classification framework is absent.This paper proposes a hier-archical classification method that considers the key elements such as module selection,fusion mechanism,architecture design,and training strategy.The spatio-temporal graph neural networks can be divided into six categories,namely recur-rent graph convolutional network,spatio-temporal fully convolutional network,spatio-temporal attention network,spatio-temporal encoder network,spatio-temporal hybrid architecture network,and spatio-temporal networks with additional strategies.For each category,the unique model construction and fusion mechanisms are analyzed in detail,and the differ-ent model variants are compared.By analyzing both representative and recent works,the development trend of spatio-temporal graph neural networks is discussed,and the code addresses of open-source models are provided.Subsequently,the commonly used public datasets are gathered,and the performance of the latest advanced models is visually analyzed by comparing the results of previous experiments.Finally,the development opportunities and challenges in this field are summarized to offer insights for future research.关键词
交通流预测/时空图神经网络/时空依赖性/模型设计/模型分类标准Key words
traffic flow prediction/spatio-temporal graph neural networks/spatio-temporal dependencies/model design/model classification criteria分类
计算机与自动化引用本文复制引用
闫佳和,李红辉,孙婧,刘杰,张骏温,杨晓睿,徐邑..面向交通流预测的时空图神经网络发展综述[J].计算机工程与应用,2025,61(22):1-19,19.基金项目
国家重点研发计划(2023YFC3321600) （2023YFC3321600）
中国国家铁路集团有限公司科技研究开发计划(P2023T002). （P2023T002）
