Data Augmented Deep Behavioral Cloning for Urban Traffic Control Operations Under a Parallel Learning Framework

doi:10.1109/TITS.2020.3048151

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 先进控制与自动化团队

	Data Augmented Deep Behavioral Cloning for Urban Traffic Control Operations Under a Parallel Learning Framework
	Li, Xiaoshuang 1,2; Ye, Peijun 2,5; Jin, Junchen 2; Zhu, Fenghua 2,5; Wang, Fei-Yue 2,3,4
刊名	IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
	2021-01-28
页码	10
关键词	Generative adversarial networks Gallium nitride Cloning Data models Task analysis Complex systems Knowledge engineering Intelligent traffic signal operations parallel learning deep behavioral cloning generative adversarial networks
ISSN号	1524-9050
DOI	10.1109/TITS.2020.3048151
通讯作者	Jin, Junchen(junchen@kth.se)
英文摘要	It is indispensable for professional traffic signal engineers to perform manual operations of traffic signal control (TSC) to mitigate traffic congestion, especially with complicated scenarios. However, such a task is time-consuming, and the level of congestion mitigation heavily relies on individual expertise in engineering practice. Therefore, it is cost-effective to learn traffic engineers' knowledge to enhance the problem-solving skills for a large-scale urban traffic network. In this paper, a data augmented deep behavioral cloning (DADBC) method is proposed to imitate the problem-solving skills of traffic engineers. The method is under a conceptual framework, parallel learning (PL) framework, that incorporates machine learning techniques for solving decision-making problems in complex systems. The DADBC method enhances a hybrid demonstration by exploiting a generative adversarial network (GAN) and then uses the deep behavioral cloning (DBC) model to learn traffic engineers' control schemes. According to the validation results using the real manipulation data from Hangzhou, China, our method can imitate complex human behaviors in intervening traffic signal control operations to improve traffic efficiency in urban areas.
资助项目	National Key Research and Development Program of China[2018AAA0101502] ; National Natural Science Foundation of China[U1811463] ; National Natural Science Foundation of China[61533019] ; National Natural Science Foundation of China[62076237] ; Youth Innovation Promotion Association, Chinese Academy of Sciences
WOS关键词	VEHICLES ; LIGHTS ; SYSTEM
WOS研究方向	Engineering ; Transportation
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000733560900001
资助机构	National Key Research and Development Program of China ; National Natural Science Foundation of China ; Youth Innovation Promotion Association, Chinese Academy of Sciences
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/46875]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
通讯作者	Jin, Junchen
作者单位	1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 3.Macau Univ Sci & Technol, Inst Syst Engn, Macau 999078, Peoples R China 4.Univ Chinese Acad Sci, Ctr China Econ & Social Secur, Beijing 100149, Peoples R China 5.Qingdao Acad Intelligent Ind, Parallel Intelligence Res Ctr, Qingdao 266109, Peoples R China
推荐引用方式 GB/T 7714	Li, Xiaoshuang,Ye, Peijun,Jin, Junchen,et al. Data Augmented Deep Behavioral Cloning for Urban Traffic Control Operations Under a Parallel Learning Framework[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2021:10.
APA	Li, Xiaoshuang,Ye, Peijun,Jin, Junchen,Zhu, Fenghua,&Wang, Fei-Yue.(2021).Data Augmented Deep Behavioral Cloning for Urban Traffic Control Operations Under a Parallel Learning Framework.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,10.
MLA	Li, Xiaoshuang,et al."Data Augmented Deep Behavioral Cloning for Urban Traffic Control Operations Under a Parallel Learning Framework".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2021):10.