Data Augmented Deep Behavioral Cloning for Urban Traffic Control Operations Under a Parallel Learning Framework
Li, Xiaoshuang1,2; Ye, Peijun1,3; Jin, Junchen1; Zhu, Fenghua1,3; Wang, Fei-Yue1,4,5
Source PublicationIEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
ISSN1524-9050
2021-01-28
Volume23Issue:6Pages:5128-5137
Abstract

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.

KeywordGenerative adversarial networks Data models Gallium nitride Task analysis Complex systems Intelligent traffic signal operations deep behavioral cloning
DOI10.1109/TITS.2020.3048151
WOS KeywordVEHICLES ; LIGHTS ; SYSTEM
Indexed BySCI
Language英语
Funding ProjectNational 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
Funding OrganizationNational Key Research and Development Program of China ; National Natural Science Foundation of China ; Youth Innovation Promotion Association, Chinese Academy of Sciences
WOS Research AreaEngineering ; Transportation
WOS SubjectEngineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology
WOS IDWOS:000733560900001
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/46876
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 AuthorJin, Junchen
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
3.Qingdao Acad Intelligent Ind, Parallel Intelligence Res Ctr, Qingdao 266109, Peoples R China
4.Univ Chinese Acad Sci, Ctr China Econ & Social Secur, Beijing 100149, Peoples R China
5.Macau Univ Sci & Technol, Inst Syst Engn, Macau 999078, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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,2021,23(6):5128-5137.
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,23(6),5128-5137.
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 23.6(2021):5128-5137.
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