| ZHANG Xijun (张玺君),ZHANG Baoqi,ZHANG Hong,NIE Shengyuan,ZHANG Xianli.[J].高技术通讯(英文),2024,30(3):221~230 |
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| Adaptive spatial-temporal graph attention network for traffic speed prediction |
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| DOI：10. 3772 / j. issn. 1006-6748. 2024. 03. 001 |
| 中文关键词: |
| 英文关键词: traffic speed prediction, spatial-temporal correlation, self-adaptive adjacency matrix, graph attention network (GAT), bidirectional gated recurrent unit (BiGRU) |
| 基金项目: |
| Author Name | Affiliation | | ZHANG Xijun (张玺君) | (School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, P. R. China) | | ZHANG Baoqi | | | ZHANG Hong | | | NIE Shengyuan | | | ZHANG Xianli | |
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| 中文摘要: |
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| 英文摘要: |
| Considering the nonlinear structure and spatial-temporal correlation of traffic network, and the
influence of potential correlation between nodes of traffic network on the spatial features, this paper
proposes a traffic speed prediction model based on the combination of graph attention network with
self-adaptive adjacency matrix (SAdpGAT) and bidirectional gated recurrent unit (BiGRU). First-
ly, the model introduces graph attention network (GAT) to extract the spatial features of real road
network and potential road network respectively in spatial dimension. Secondly, the spatial features
are input into BiGRU to extract the time series features. Finally, the prediction results of the real
road network and the potential road network are connected to generate the final prediction results of
the model. The experimental results show that the prediction accuracy of the proposed model is im-
proved obviously on METR-LA and PEMS-BAY datasets, which proves the advantages of the pro-
posed spatial-temporal model in traffic speed prediction. |
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