Pattern Sensitive Prediction of Traffic Flow Based on Generative Adversarial Framework

doi:10.1109/TITS.2018.2857224

CASIA OpenIR > 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

	Pattern Sensitive Prediction of Traffic Flow Based on Generative Adversarial Framework
	Lin, Yilun1,2,3 ; Dai, Xingyuan1,2,3 ; Li, Li 3,4; 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	被引频次：62[WOS] [WOS记录] [WOS相关记录]
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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