A novel residual graph convolution deep learning model for short-term network-based traffic forecasting
文献类型：期刊论文
| 作者 | Zhang, Yang2,3; Cheng, Tao2; Ren, Yibin4,5; Xie, Kun1 |
| 刊名 | INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
 |
| 出版日期 | 2019-12-04 |
| 页码 | 27 |
| 关键词 | Short-term traffic forecasting spatial-temporal dependency network topology graph convolution residual long short-term memory |
| ISSN号 | 1365-8816 |
| DOI | 10.1080/13658816.2019.1697879 |
| 通讯作者 | Zhang, Yang(yang.zhang.16@ucl.ac.uk) |
| 英文摘要 | 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. |
| 资助项目 | UK Economic and Social Research Council[ES/L011840/1] ; China Scholarship Council[201603170309] ; University College London |
| WOS研究方向 | Computer Science ; Geography ; Physical Geography ; Information Science & Library Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000500113700001 |
| 出版者 | TAYLOR & FRANCIS LTD |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/163861]  |
| 专题 | 海洋研究所_海洋环流与波动重点实验室 |
| 通讯作者 | Zhang, Yang |
| 作者单位 | 1.Old Dominion Univ, Dept Civil & Environm Engn, Norfolk, VA USA 2.UCL, Dept Civil Environm & Geomat Engn, SpaceTimeLab Big Data Analyt, London, England 3.Natl Univ Def Technol, Coll Syst Engn, Changsha, Hunan, Peoples R China 4.Chinese Acad Sci, Inst Oceanol, CAS Key Lab Ocean Circulat & Waves, Qingdao, Shandong, Peoples R China 5.Chinese Acad Sci, Ctr Ocean Megasci, Qingdao, Shandong, Peoples R China |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：海洋研究所
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