Show simple item record

dc.contributor.authorTang, Cong
dc.contributor.authorSun, Jingru
dc.contributor.authorSun, Yichuang
dc.contributor.authorPeng, Mu
dc.contributor.authorGan, Nianfei
dc.date.accessioned2020-09-11T00:06:38Z
dc.date.available2020-09-11T00:06:38Z
dc.date.issued2020-08-21
dc.identifier.citationTang , C , Sun , J , Sun , Y , Peng , M & Gan , N 2020 , ' A General Traffic Flow Prediction Approach Based on Spatial-Temporal Graph Attention ' , IEEE Access . https://doi.org/10.1109/ACCESS.2020.3018452
dc.identifier.issn2169-3536
dc.identifier.otherPURE: 22503827
dc.identifier.otherPURE UUID: e168db2c-a823-4ea5-9d3b-4a171f8e955f
dc.identifier.otherScopus: 85090551802
dc.identifier.urihttp://hdl.handle.net/2299/23119
dc.description.abstractAccurate and reliable traffic flow prediction is critical to the safe and stable deployment ofintelligent transportation systems. However, it is very challenging since the complex spatial and temporaldependence of traffic flows. Most existing works require the information of the traffic network structure andhuman intervention to model the spatial-temporal association of traffic data, resulting in low generality of themodel and unsatisfactory prediction performance. In this paper, we propose a general spatial-temporal graphattention based dynamic graph convolutional network (GAGCN) model to predict traffic flow. GAGCN usesthe graph attention networks to extract the spatial associations among nodes hidden in the traffic featuredata automatically which can be dynamically adjusted over time. And then the graph convolution networkis adjusted based on the spatial associations to extract the spatial features of the road network. Notably, theinformation of rode network structure and human intervention are not required in GAGCN. The forecastingaccuracy and the generality are evaluated with two real-world traffic datasets. Results indicate that ourGAGCN surpasses the state-of-the-art baselinesen
dc.language.isoeng
dc.relation.ispartofIEEE Access
dc.titleA General Traffic Flow Prediction Approach Based on Spatial-Temporal Graph Attentionen
dc.contributor.institutionCentre for Engineering Research
dc.contributor.institutionCommunications and Intelligent Systems
dc.contributor.institutionSchool of Physics, Engineering & Computer Science
dc.contributor.institutionDepartment of Engineering and Technology
dc.description.statusPeer reviewed
rioxxterms.versionAM
rioxxterms.versionofrecordhttps://doi.org/10.1109/ACCESS.2020.3018452
rioxxterms.typeJournal Article/Review


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record