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Coordinated Ramp Metering with Equity Consideration Using Reinforcement Learning
Lu, Chao1,2; Huang, Jie2,3; Deng, Lianbo4; Gong, Jianwei1
2017-07-01
Source PublicationJOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS
ISSN2473-2907
Volume143Issue:7Pages:11
Corresponding AuthorLu, Chao(chaolu@bit.edu.cn)
AbstractReinforcement learning (RL) has been applied to solve ramp-metering problems and attracted increasing attention in recent studies. However, improving traffic efficiency is the main concern of these applications, and the issue relating to user equity has not been well considered. A new RL-based system is developed in this paper to deal with equity-related problems. With the definition of three RL elements, including reward, action, and state, this system can capture the information of user equity and balance it with traffic efficiency. Simulation experiments using real traffic data collected from a real-world motorway stretch are designed to test the performance of the new system. Compared with a widely used ramp-metering algorithm ALINEA, the new system shows superior performance on improving both traffic efficiency and user equity. Specifically, with suitable parameter settings, the new system can reduce the total time spent (TTS) by motorway users by 18.5% and maintain an equally distributed total waiting time (TWT) with a low standard deviation for TWT across on-ramps close to 0. (C) 2017 American Society of Civil Engineers.
KeywordRamp metering Reinforcement learning Asymmetric cell transmission model ALINEA
DOI10.1061/JTEPBS.0000036
WOS KeywordFREEWAY ; CONGESTION ; NETWORK ; DESIGN
Indexed BySCI
Language英语
Funding ProjectChina Scholarship Council and University of Leeds (CSC-University of Leeds scholarship) ; National Natural Science Foundation of China[91420203] ; National Natural Science Foundation of China[51275041] ; Beijing Institute of Technology Research Fund Program for Young Scholars
Funding OrganizationChina Scholarship Council and University of Leeds (CSC-University of Leeds scholarship) ; National Natural Science Foundation of China ; Beijing Institute of Technology Research Fund Program for Young Scholars
WOS Research AreaEngineering ; Transportation
WOS SubjectEngineering, Civil ; Transportation Science & Technology
WOS IDWOS:000401978300001
PublisherASCE-AMER SOC CIVIL ENGINEERS
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/64493
Collection中国科学院地理科学与资源研究所
Corresponding AuthorLu, Chao
Affiliation1.Beijing Inst Technol, Sch Mech Engn, Beijing 100081, Peoples R China
2.Univ Leeds, Inst Transport Studies, Leeds LS2 9JT, W Yorkshire, England
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Reg Sustainable Dev Modeling, Beijing 100101, Peoples R China
4.Cent S Univ, Sch Traff & Transportat Engn, Changsha 410075, Hunan, Peoples R China
Recommended Citation
GB/T 7714
Lu, Chao,Huang, Jie,Deng, Lianbo,et al. Coordinated Ramp Metering with Equity Consideration Using Reinforcement Learning[J]. JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS,2017,143(7):11.
APA Lu, Chao,Huang, Jie,Deng, Lianbo,&Gong, Jianwei.(2017).Coordinated Ramp Metering with Equity Consideration Using Reinforcement Learning.JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS,143(7),11.
MLA Lu, Chao,et al."Coordinated Ramp Metering with Equity Consideration Using Reinforcement Learning".JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS 143.7(2017):11.
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