| 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
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| Source Publication | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
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| ISSN | 1524-9050 |
| 2019-06-01 | |
| Volume | 20Issue:6Pages:2395-2400 |
| Corresponding Author | Li, Li(li-li@tsinghua.edu.cn) |
| Abstract | 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. |
| Keyword | Traflic flow prediction deep learning generative adversarial network |
| DOI | 10.1109/TITS.2018.2857224 |
| WOS Keyword | INTELLIGENT TRANSPORTATION SYSTEMS |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | 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] |
| Funding Organization | National Natural Science Foundation of China |
| WOS Research Area | Engineering ; Transportation |
| WOS Subject | Engineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology |
| WOS ID | WOS:000470039700035 |
| Publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/23644 |
| Collection | 复杂系统管理与控制国家重点实验室_平行智能技术与系统团队 |
| Corresponding Author | Li, Li |
| Affiliation | 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 |
| First Author Affilication | Institute of Automation, Chinese Academy of Sciences |
| Recommended Citation 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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| Lin et al_2018_Patte（623KB） | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | View Download | |
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