Knowledge Commons of Institute of Automation,CAS
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 | |
发表期刊 | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS |
ISSN | 1524-9050 |
2021-01-28 | |
卷号 | 23期号:6页码:5128-5137 |
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. |
关键词 | Generative adversarial networks Data models Gallium nitride Task analysis Complex systems Intelligent traffic signal operations deep behavioral cloning |
DOI | 10.1109/TITS.2020.3048151 |
关键词[WOS] | VEHICLES ; LIGHTS ; SYSTEM |
收录类别 | SCI |
语种 | 英语 |
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 |
项目资助者 | National Key Research and Development Program of China ; National Natural Science Foundation of China ; Youth Innovation Promotion Association, Chinese Academy of Sciences |
WOS研究方向 | Engineering ; Transportation |
WOS类目 | Engineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology |
WOS记录号 | WOS:000733560900001 |
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 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/46876 |
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 | Jin, Junchen |
作者单位 | 1.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 |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
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,2021,23(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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