Adaptive spatial-temporal graph attention network for traffic speed prediction

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外文标题：Adaptive spatial-temporal graph attention network for traffic speed prediction

外文摘要：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.

外文关键词：

traffic speed predictionspatial-temporal correlationself-adaptive adjacency ma-trixgraph attention network(GAT)bidirectional gated recurrent unit(BiGRU)

作者：

张玺君、ZHANG Baoqi、ZHANG Hong、NIE Shengyuan、ZHANG Xianli

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作者单位：

School of Computer and Communication,Lanzhou University of Technology,Lanzhou 730050,P.R.China

关键词：

traffic speed prediction spatial-temporal correlation self-adaptive adjacency ma-trix graph attention network(GAT) bidirectional gated recurrent unit(BiGRU)

出版年：

2024

DOI：

10.3772/j.issn.1006-6748.2024.03.001

高技术通讯(英文版)

中国科学技术信息研究所（ISTIC）

高技术通讯(英文版)

影响因子：0.058

ISSN：1006-6748

年,卷(期)：2024.30(3)