中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
STGSA: A Novel Spatial-Temporal Graph Synchronous Aggregation Model for Traffic Prediction

文献类型:期刊论文

作者Zebing Wei; Hongxia Zhao; Zhishuai Li; Xiaojie Bu; Yuanyuan Chen; Xiqiao Zhang; Yisheng Lv; Fei-Yue Wang
刊名IEEE/CAA Journal of Automatica Sinica
出版日期2023
卷号10期号:1页码:226-238
ISSN号2329-9266
关键词Deep learning graph neural network (GNN) multi-stream spatial-temporal feature extraction temporal graph traffic prediction
DOI10.1109/JAS.2023.123033
英文摘要The success of intelligent transportation systems relies heavily on accurate traffic prediction, in which how to model the underlying spatial-temporal information from traffic data has come under the spotlight. Most existing frameworks typically utilize separate modules for spatial and temporal correlations modeling. However, this stepwise pattern may limit the effectiveness and efficiency in spatial-temporal feature extraction and cause the overlook of important information in some steps. Furthermore, it is lacking sufficient guidance from prior information while modeling based on a given spatial adjacency graph (e.g., deriving from the geodesic distance or approximate connectivity), and may not reflect the actual interaction between nodes. To overcome those limitations, our paper proposes a spatial-temporal graph synchronous aggregation (STGSA) model to extract the localized and long-term spatial-temporal dependencies simultaneously. Specifically, a tailored graph aggregation method in the vertex domain is designed to extract spatial and temporal features in one graph convolution process. In each STGSA block, we devise a directed temporal correlation graph to represent the localized and long-term dependencies between nodes, and the potential temporal dependence is further fine-tuned by an adaptive weighting operation. Meanwhile, we construct an elaborated spatial adjacency matrix to represent the road sensor graph by considering both physical distance and node similarity in a data-driven manner. Then, inspired by the multi-head attention mechanism which can jointly emphasize information from different representation subspaces, we construct a multi-stream module based on the STGSA blocks to capture global information. It projects the embedding input repeatedly with multiple different channels. Finally, the predicted values are generated by stacking several multi-stream modules. Extensive experiments are constructed on six real-world datasets, and numerical results show that the proposed STGSA model significantly outperforms the benchmarks.
源URL[http://ir.ia.ac.cn/handle/173211/50738]  
专题自动化研究所_学术期刊_IEEE/CAA Journal of Automatica Sinica
推荐引用方式
GB/T 7714
Zebing Wei,Hongxia Zhao,Zhishuai Li,et al. STGSA: A Novel Spatial-Temporal Graph Synchronous Aggregation Model for Traffic Prediction[J]. IEEE/CAA Journal of Automatica Sinica,2023,10(1):226-238.
APA Zebing Wei.,Hongxia Zhao.,Zhishuai Li.,Xiaojie Bu.,Yuanyuan Chen.,...&Fei-Yue Wang.(2023).STGSA: A Novel Spatial-Temporal Graph Synchronous Aggregation Model for Traffic Prediction.IEEE/CAA Journal of Automatica Sinica,10(1),226-238.
MLA Zebing Wei,et al."STGSA: A Novel Spatial-Temporal Graph Synchronous Aggregation Model for Traffic Prediction".IEEE/CAA Journal of Automatica Sinica 10.1(2023):226-238.

入库方式: OAI收割

来源:自动化研究所

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