计算机应用研究2024，Vol.41Issue(4)：1041-1046,6.DOI:10.19734/j.issn.1001-3695.2023.08.0362
基于周期图卷积与多头注意力GRU组合的交通流量预测模型
Traffic flow prediction model based on combining periodic graph convolution network and multi-head attention GRU
摘要
Abstract
To capture the complex spatial-temporal dynamics and periodic patterns in traffic flow data,and reduce the cumu-lative error effects caused by unexpected road conditions,this paper proposed a traffic flow prediction model based on combi-ning PGCN and MAGRU.Firstly,the spatial-temporal data fusion module constructed periodic graphs using the property of pe-riodic similarity in traffic flow data,and added spatial and temporal encoding information into the sequence data.Then,in the spatial-temporal feature extraction module,graph convolutional network(GCN)submodule captured spatial features from the periodic feature graphs,MAGRU submodule captured temporal features from the sequence data.Finally,the gated fusion mechanism fused the features extracted by both modules.It conducted the experiment on two real traffic flow datasets.The re-sults indicate that compared to several recent baseline models,the model achieves average reduction of 5.4％,22.8％,10.3％in MAE,RMSE and MAPE,exhibits an average improvement of 11.6％in R2 accuracy metric,which confirms that the model can provide more accurate predictions and reduce cumulative error effects.关键词
交通流量预测/图卷积网络/多头注意力机制/门控循环单元/门控融合机制/时空融合Key words
traffic flow prediction/graph convolutional network(GCN)/multi-head attention mechanism/gated recurrent unit(GRU)/gated fusion mechanism/spatial-temporal fusion分类
信息技术与安全科学引用本文复制引用
钟林岚,张安勤,田秀霞..基于周期图卷积与多头注意力GRU组合的交通流量预测模型[J].计算机应用研究,2024,41(4):1041-1046,6.基金项目
广东省人文社会科学重点研究基地-汕头大学地方政府发展研究所开放基金课题(07422002) （07422002）
