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
Source PublicationIEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
ISSN1524-9050
2019-06-01
Volume20Issue:6Pages:2395-2400
Corresponding AuthorLi, Li(li-li@tsinghua.edu.cn)
AbstractTraffic 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.
KeywordTraflic flow prediction deep learning generative adversarial network
DOI10.1109/TITS.2018.2857224
WOS KeywordINTELLIGENT TRANSPORTATION SYSTEMS
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61533019] ; National Natural Science Foundation of China[71232006]
Funding OrganizationNational Natural Science Foundation of China
WOS Research AreaEngineering ; Transportation
WOS SubjectEngineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology
WOS IDWOS:000470039700035
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23644
Collection复杂系统管理与控制国家重点实验室_先进控制与自动化
复杂系统管理与控制国家重点实验室_复杂系统研究
Corresponding AuthorLi, Li
Affiliation1.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 AffilicationInstitute 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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