基于注意力和线性层融合的动态图卷积交通量预测模型
Dynamic graph convolutional traffic flow prediction model based on attention and linear layer fusion
摘要
Abstract
Accurate traffic volume prediction is crucial for optimizing the operational efficiency of road networks and alleviating urban traffic congestion.Traditional models rely on predefined static graph structures,making it difficult to capture dynamic spatiotemporal correlations,and single-time-scale modeling struggles to comprehensively extract multi-scale features.To address these issues,a dual dynamic adaptive spatiotemporal modeling framework is proposed.In the temporal dimension,this framework employs a dynamic time feature extraction multi-head attention mechanism to adaptively adjust temporal weights to capture key dynamic features.In the spatial dimension,it designs a dynamic graph convolutional network that generates an adjacency matrix in real-time through a self-attention mechanism to represent dynamic spatial dependencies between nodes,thus achieving spatiotemporal dual dynamic collaborative modeling.Furthermore,this framework introduces a learnable linear fusion layer to adaptively integrate multi-time-scale prediction results and collaboratively optimize local and global feature representations.Experiments on real road datasets demonstrate that this framework significantly outperforms the baseline model,validating its superior spatiotemporal feature capture and prediction performance.关键词
交通量预测/动态时间特征提取多头注意力机制/动态图卷积/线性层融合Key words
traffic volume prediction/dynamic time feature extraction with multi head attention mechanism/dynamic graph convolution/linear layer fusion分类
信息技术与安全科学引用本文复制引用
尉辉,肖洪波,邹北骥,奎晓燕,肖捡花,和佳聚,合尼古力..基于注意力和线性层融合的动态图卷积交通量预测模型[J].大数据,2026,12(1):126-145,20.基金项目
国家自然科学基金项目(No.62202198、No.U22A2034、No.62177047) （No.62202198、No.U22A2034、No.62177047）
湘江实验室重点项目(No.23XJ02005) （No.23XJ02005）
湖南省自然科学基金项目(No.2024JJ7372) （No.2024JJ7372）
湖南省教育厅科学研究重点项目(No.24A0550、No.24A0018) （No.24A0550、No.24A0018）
湖南省教育厅教学改革项目(No.202401001340、No.202502001316) （No.202401001340、No.202502001316）
湖南省科技厅重点研发计划(No.2024JK2135) （No.2024JK2135）
湖南省普通高等学校科技创新团队支持项目"武陵山片区智慧农业信息处理与控制技术"(No.ZNKZD2024-3) （No.ZNKZD2024-3）
新疆维吾尔自治区自然科学基金项目(No.2024D01A52) （No.2024D01A52）
中南大学前沿交叉项目(No.2023QYJC020) The National Natural Science Foundation of China(No.62202198,No.U22A2034,No.62177047),Key Project of Xiangjiang Laboratory(No.23XJ02005),The Natural Science Foundation of Hunan Province(No.2024JJ7372),Key Scientific Research Projects of Hunan Provincial Department of Education(No.24A0550,No.24A0018),The Teaching Reform Project of Hunan Provincial Department of Education(No.202401001340,No.202502001316),The Key Research and Development Plan of Hunan Provincial Department of Science and Technology(No.2024JK2135),The Science and Technology Innovation Team Support Project for Ordinary Higher Education Institutions in Hunan Province"Wuling Mountain Area Smart Agriculture Intommation Processing and Control Technology"(No.ZNKZD2024-3),The Natural Science Foundation of Xinjiang Uygur Autonomous Region(No.2024D01A52),Frontier Cross Project of Central South University(No.2023QYJC020) （No.2023QYJC020）
