Adaptive spatial-temporal graph attention network for traffic speed prediction  

作　　者：ZHANG Xijun ZHANG Baoqi ZHANG Hong NIE Shengyuan ZHANG Xianli 张玺君;ZHANG Baoqi;ZHANG Hong;NIE Shengyuan;ZHANG Xianli(School of Computer and Communication,Lanzhou University of Technology,Lanzhou 730050,P.R.China)

机构地区：[1]School of Computer and Communication,Lanzhou University of Technology,Lanzhou 730050,P.R.China

出　　处：《High Technology Letters》2024年第3期221-230,共10页高技术通讯（英文版）

基　　金：the National Natural Science Foundation of China(No.61461027,61762059);the Provincial Science and Technology Program supported the Key Project of Natural Science Foundation of Gansu Province(No.22JR5RA226)。

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.

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

分 类 号：U495[交通运输工程—交通运输规划与管理] TP18[交通运输工程—道路与铁道工程]