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

文献类型：期刊论文

作者Li, Xiaoshuang1,2; Ye, Peijun2,5; Jin, Junchen2; Zhu, Fenghua2,5; Wang, Fei-Yue2,3,4
刊名IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
出版日期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
DOI10.1109/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.
WOS关键词VEHICLES ; LIGHTS ; SYSTEM
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研究方向Engineering ; Transportation
语种英语
WOS记录号WOS:000733560900001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
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
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.

入库方式： OAI收割

来源：自动化研究所

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