电子科技2026，Vol.39Issue(8)：62-68,7.DOI:10.16180/j.cnki.issn1007-7820.2026.08.009
基于GCN-AGRU的短时交通流预测
Short-Term Traffic Flow Preiction Based on GCN-AGRU
摘要
Abstract
In view of the problem that the existing short-term traffic flow prediction methods do not consider the frequency-domain characteristics of traffic flow,a new GCN-AGRU(Graph Convolutional Neural Network-At-tention Gated Recurrent Unit)short-term traffic flow prediction model is constructedr.Combining the GCN(Graph Convolutional Neural Network)network based on adaptive graph structure with the GRU(Gated Recurrent Unit),and conducting frequency-domain analysis of the hidden state of the GRU by using FCA(Frequency Channel Atten-tion)attention,the periodic characteristics in traffic flow can be captured more accurately.Experiments were con-ducted based on the Wuhan Road network dataset and the PeMS04 dataset,and the prediction results of other mod-els were compared and analyzed.Compared with the optimal baseline model,the MAE(Mean Absolute Error)、RMSE(Root Mean Square Error)and MAPE(Mean Absolute Percentage Error)of the proposed model decreased by 9.84 percentage points,1.87 percentage points and 4.88 percentage points respectively,significantly improving the accuracy and stability of short-term traffic flow prediction.关键词
交通流预测/GCN/GRU/频域分析/周期性/自适应图/空间信息/注意力机制Key words
traffic flow prediction/GCN/GRU/frequency domain analysis/periodicity/adaptive graph/spatial information/attention mechanism分类
信息技术与安全科学引用本文复制引用
王庆国,赵磊..基于GCN-AGRU的短时交通流预测[J].电子科技,2026,39(8):62-68,7.基金项目
国家自然科学基金(41571396) （41571396）
武汉市重点研发计划(2024050702030122)National Natural Science Foundation of China(41571396) （2024050702030122）
Wuhan Key Research and Development Program(2024050702030122) （2024050702030122）
