IOCAS-IR  > 海洋环流与波动重点实验室
A novel residual graph convolution deep learning model for short-term network-based traffic forecasting
Zhang, Yang1,2; Cheng, Tao1; Ren, Yibin3,4; Xie, Kun5
2019-12-04
Source PublicationINTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
ISSN1365-8816
Pages27
Corresponding AuthorZhang, Yang(yang.zhang.16@ucl.ac.uk)
AbstractShort-term traffic forecasting on large street networks is significant in transportation and urban management, such as real-time route guidance and congestion alleviation. Nevertheless, it is very challenging to obtain high prediction accuracy with reasonable computational cost due to the complex spatial dependency on the traffic network and the time-varying traffic patterns. To address these issues, this paper develops a residual graph convolution long short-term memory (RGC-LSTM) model for spatial-temporal data forecasting considering the network topology. This model integrates a new graph convolution operator for spatial modelling on networks and a residual LSTM structure for temporal modelling considering multiple periodicities. The proposed model has few parameters, low computational complexity, and a fast convergence rate. The framework is evaluated on both the 10-min traffic speed data from Shanghai, China and the 5-min Caltrans Performance Measurement System (PeMS) traffic flow data. Experiments show the advantages of the proposed approach over various state-of-the-art baselines, as well as consistent performance across different datasets.
KeywordShort-term traffic forecasting spatial-temporal dependency network topology graph convolution residual long short-term memory
DOI10.1080/13658816.2019.1697879
Indexed BySCI
Language英语
Funding ProjectUK Economic and Social Research Council[ES/L011840/1] ; China Scholarship Council[201603170309] ; University College London
WOS Research AreaComputer Science ; Geography ; Physical Geography ; Information Science & Library Science
WOS SubjectComputer Science, Information Systems ; Geography ; Geography, Physical ; Information Science & Library Science
WOS IDWOS:000500113700001
PublisherTAYLOR & FRANCIS LTD
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.qdio.ac.cn/handle/337002/163862
Collection海洋环流与波动重点实验室
Corresponding AuthorZhang, Yang
Affiliation1.UCL, Dept Civil Environm & Geomat Engn, SpaceTimeLab Big Data Analyt, London, England
2.Natl Univ Def Technol, Coll Syst Engn, Changsha, Hunan, Peoples R China
3.Chinese Acad Sci, Inst Oceanol, CAS Key Lab Ocean Circulat & Waves, Qingdao, Shandong, Peoples R China
4.Chinese Acad Sci, Ctr Ocean Megasci, Qingdao, Shandong, Peoples R China
5.Old Dominion Univ, Dept Civil & Environm Engn, Norfolk, VA USA
Recommended Citation
GB/T 7714
Zhang, Yang,Cheng, Tao,Ren, Yibin,et al. A novel residual graph convolution deep learning model for short-term network-based traffic forecasting[J]. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,2019:27.
APA Zhang, Yang,Cheng, Tao,Ren, Yibin,&Xie, Kun.(2019).A novel residual graph convolution deep learning model for short-term network-based traffic forecasting.INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,27.
MLA Zhang, Yang,et al."A novel residual graph convolution deep learning model for short-term network-based traffic forecasting".INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE (2019):27.
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