Knowledge Commons of Institute of Automation,CAS
Pattern Sensitive Prediction of Traffic Flow Based on Generative Adversarial Framework | |
Lin, Yilun1,2,3; Dai, Xingyuan1,2,3; Li, Li3,4; Wang, Fei-Yue1,3 | |
发表期刊 | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS |
ISSN | 1524-9050 |
2019-06-01 | |
卷号 | 20期号:6页码:2395-2400 |
通讯作者 | Li, Li(li-li@tsinghua.edu.cn) |
摘要 | Traffic flow prediction is one of the most popular topics in the field of the intelligent transportation system due to its importance. Powered by advanced machine learning techniques, especially the deep learning method, prediction accuracy noticeably increases in recent years. However, most existing methods applied a data-driven paradigm and tend to ignore the outliers, which result in poor performance while handling burst phenomena in the traffic system. To overcome this problem, the prediction model needs to recognise different patterns and handle them in different ways. In this paper. we propose a new prediction model (called pattern sensitive network) that can handle different traffic patterns automatically. By using adversarial training. our model can make more accurate predictions in unusual states without compromising its performance in usual states. Experiments demonstrate that our method can work well in both usual traffic states and unusual traffic states. |
关键词 | Traflic flow prediction deep learning generative adversarial network |
DOI | 10.1109/TITS.2018.2857224 |
关键词[WOS] | INTELLIGENT TRANSPORTATION SYSTEMS |
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[71232006] ; National Natural Science Foundation of China[71232006] ; National Natural Science Foundation of China[61533019] ; National Natural Science Foundation of China[61533019] ; National Natural Science Foundation of China[71232006] |
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:000470039700035 |
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/173211/23644 |
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 | Li, Li |
作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100080, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Qingdao Acad Intelligent Ind, Qingdao 266109, Shandong, Peoples R China 4.Tsinghua Univ, Dept Automat, Tsinghua Natl Lab Informat Sci & Technol, Beijing 100084, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Lin, Yilun,Dai, Xingyuan,Li, Li,et al. Pattern Sensitive Prediction of Traffic Flow Based on Generative Adversarial Framework[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2019,20(6):2395-2400. |
APA | Lin, Yilun,Dai, Xingyuan,Li, Li,&Wang, Fei-Yue.(2019).Pattern Sensitive Prediction of Traffic Flow Based on Generative Adversarial Framework.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,20(6),2395-2400. |
MLA | Lin, Yilun,et al."Pattern Sensitive Prediction of Traffic Flow Based on Generative Adversarial Framework".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 20.6(2019):2395-2400. |
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