Knowledge Commons of Institute of Automation,CAS
Traffic Flow Imputation Using Parallel Data and Generative Adversarial Networks | |
Chen, Yuanyuan1,2; Lv, Yisheng1,3; Wang, Fei-Yue1,3 | |
发表期刊 | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS |
ISSN | 1524-9050 |
2020-04-01 | |
卷号 | 21期号:4页码:1624-1630 |
摘要 | Traffic data imputation is critical for both research and applications of intelligent transportation systems. To develop traffic data imputation models with high accuracy, traffic data must be large and diverse, which is costly. An alternative is to use synthetic traffic data, which is cheap and easy-access. In this paper, we propose a novel approach using parallel data and generative adversarial networks (GANs) to enhance traffic data imputation. Parallel data is a recently proposed method of using synthetic and real data for data mining and data-driven process, in which we apply GANs to generate synthetic traffic data. As it is difficult for the standard GAN algorithm to generate time-dependent traffic flow data, we made twofold modifications: 1) using the real data or the corrupted ones instead of random vectors as latent codes to generator within GANs and 2) introducing a representation loss to measure discrepancy between the synthetic data and the real data. The experimental results on a real traffic dataset demonstrate that our method can significantly improve the performance of traffic data imputation. |
关键词 | Generators Data models Gallium nitride Generative adversarial networks Training Loss measurement Biological system modeling Parallel data generative adversarial networks traffic flow imputation data augmentation deep learning |
DOI | 10.1109/TITS.2019.2910295 |
关键词[WOS] | INTELLIGENT TRANSPORTATION SYSTEMS ; PREDICTION |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[61533019] ; National Natural Science Foundation of China[61876011] ; National Natural Science Foundation of China[U1811463] |
项目资助者 | National Natural Science Foundation of China |
WOS研究方向 | Engineering ; Transportation |
WOS类目 | Engineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology |
WOS记录号 | WOS:000523478400024 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 人工智能+交通 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/38866 |
专题 | 多模态人工智能系统全国重点实验室_平行智能技术与系统团队 |
通讯作者 | Lv, Yisheng |
作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100190, Peoples R China 3.Qingdao Acad Intelligent Ind, Shandong 266109, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Chen, Yuanyuan,Lv, Yisheng,Wang, Fei-Yue. Traffic Flow Imputation Using Parallel Data and Generative Adversarial Networks[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2020,21(4):1624-1630. |
APA | Chen, Yuanyuan,Lv, Yisheng,&Wang, Fei-Yue.(2020).Traffic Flow Imputation Using Parallel Data and Generative Adversarial Networks.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,21(4),1624-1630. |
MLA | Chen, Yuanyuan,et al."Traffic Flow Imputation Using Parallel Data and Generative Adversarial Networks".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 21.4(2020):1624-1630. |
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