基于多头注意力时空图卷积网络的交通事故预测

doi:10.12066/j.issn.1007-2861.2676

上海大学学报(自然科学版) ›› 2025, Vol. 31 ›› Issue (4): 678-690.doi: 10.12066/j.issn.1007-2861.2676

基于多头注意力时空图卷积网络的交通事故预测

姜天豪, 王瑞

上海大学通信与信息工程学院, 上海 200444

收稿日期:2024-12-27 出版日期:2025-08-31 发布日期:2025-09-16
通讯作者: 王瑞(1982—),男,教授,博士,研究方向为天地一体化物联网技术、智能信息处理、模式识别. E-mail:rwang@shu.edu.cn

Traffic accident prediction based on multi-head attention spatio-temporal graph convolutional network

JIANG Tianhao, WANG Rui

School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China

Received:2024-12-27 Online:2025-08-31 Published:2025-09-16

摘要/Abstract

摘要： 提出一种结合多头注意力(multi-head attention,MHA)机制和自适应邻接矩阵的新型时空图卷积网络(spatio-temporal graphconvolutional network,STGCN)模型.MHA机制对时空特征和外部环境因素进行加权融合,自适应邻接矩阵对道路网络的连接权重进行动态调整,提升了对空间依赖性的刻画能力.结果表明,该模型在伦敦道路网络数据集上的表现优于已有模型,在多个指标上显著提升了预测精度.

关键词: 交通事故预测, 时空图卷积网络, 多头注意力机制, 自适应邻接矩阵

Abstract: This paper proposed a novel spatio-temporal graph convolutional network (STGCN) enhanced with a multi-head attention (MHA) mechanism and adaptive adjacency matrices. The MHA mechanism dynamically weighted spatio-temporal features and external environmental factors, while the adaptive adjacency matrices adjusted the connection weights of road networks to improve the model's ability to capture spatial dependencies. Experimental results demonstrated that the model outperformed state-of-the-art (SOTA) models on the London road network dataset, achieving significant improvements across multiple evaluation metrics.

Key words: traffic accident prediction, spatio-temporal graph convolutional network (STGCN), multi-head attention (MHA) mechanism, adaptive adjacency matrix

中图分类号:

TP183

姜天豪, 王瑞. 基于多头注意力时空图卷积网络的交通事故预测[J]. 上海大学学报(自然科学版), 2025, 31(4): 678-690.

JIANG Tianhao, WANG Rui. Traffic accident prediction based on multi-head attention spatio-temporal graph convolutional network[J]. Journal of Shanghai University（Natural Science Edition）, 2025, 31(4): 678-690.

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基于多头注意力时空图卷积网络的交通事故预测

Traffic accident prediction based on multi-head attention spatio-temporal graph convolutional network

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