Institutional Repository of Key Laboratory of Ocean Circulation and Wave Studies, Institute of Oceanology, Chinese Academy of Sciences
| 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 Publication | INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
 |
| ISSN | 1365-8816 |
| Pages | 27 |
| Corresponding Author | Zhang, Yang(yang.zhang.16@ucl.ac.uk) |
| Abstract | Short-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. |
| Keyword | Short-term traffic forecasting spatial-temporal dependency network topology graph convolution residual long short-term memory |
| DOI | 10.1080/13658816.2019.1697879 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | UK Economic and Social Research Council[ES/L011840/1] ; China Scholarship Council[201603170309] ; University College London |
| WOS Research Area | Computer Science ; Geography ; Physical Geography ; Information Science & Library Science |
| WOS Subject | Computer Science, Information Systems ; Geography ; Geography, Physical ; Information Science & Library Science |
| WOS ID | WOS:000500113700001 |
| Publisher | TAYLOR & FRANCIS LTD |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.qdio.ac.cn/handle/337002/163862 |
| Collection | 海洋环流与波动重点实验室 |
| Corresponding Author | Zhang, Yang |
| Affiliation | 1.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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