An Agent-Based Traffic Recommendation System: Revisiting and Revising Urban Traffic Management Strategies
Jin, Junchen1,2; Rong, Dingding1; Pang, Yuqi3; Ye, Peijun4; Ji, Qingyuan5,6; Wang, Xiao4; Wang, Ge7; Wang, Fei-Yue4
Source PublicationIEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
ISSN2168-2216
2022-06-03
Pages13
Corresponding AuthorJi, Qingyuan(qingyuan.ji@zju.edu.cn)
AbstractStrategic traffic management is crucial for combating traffic congestion at the macroscopic level. However, such a field is still relatively unexplored, particularly for microscopic control objects, such as intersections and coordinated intersection groups. This article proposes a human-in-the-loop recommendation system for strategic urban traffic management, which follows an agent-based structure. A regional agent dispatcher is defined to assign agents for operation whenever ``operation on-demand'' is required. Such a requirement is identified by a daily-dependent operational mode on strategic traffic operations at a control object level. The strategic management scheme for each control object is guided by a strategic agent (customized), which is essentially a deep recommender model with a specific architecture. By featuring the multiagent design, a customized operational scheme can be generated at the intersection level, which instructs the corresponding controller to take specific operations. The utility of the recommendation system is demonstrated via a case study using real-world traffic data. In both offline and online evaluations, the system performs consistently at traffic operational recommendations in different scenarios and has the potential to provide more reasonable traffic operational strategies than a human-operated system.
KeywordSwitches Control systems Timing Task analysis Behavioral sciences Analytical models Research and development Agent-based recommendation system deep recommendation human-in-the-loop operation on-demand traffic management strategies
DOI10.1109/TSMC.2022.3177027
WOS KeywordSIGNAL CONTROL ; FRAMEWORK ; INTELLIGENCE ; ARCHITECTURE ; INTERNET ; LIGHTS
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[62173329] ; National Key Research and Development Program of China[2018AAA0101502]
Funding OrganizationNational Natural Science Foundation of China ; National Key Research and Development Program of China
WOS Research AreaAutomation & Control Systems ; Computer Science
WOS SubjectAutomation & Control Systems ; Computer Science, Cybernetics
WOS IDWOS:000809367300001
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/49597
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 AuthorJi, Qingyuan
Affiliation1.Zhejiang Supcon Informat Co Ltd, Traff Res & Dev Dept, Hangzhou 310052, Peoples R China
2.Zhejiang Univ, Coll Elect Engn, Hangzhou 310027, Peoples R China
3.Zhejiang Lab, Intelligent Transport Res Ctr, Hangzhou 311121, Peoples R China
4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
5.Enjoyor Co Ltd, Smart Transport Res Inst, Hangzhou 310030, Peoples R China
6.Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310027, Peoples R China
7.Univ Chinese Acad Sci, Coll Econ & Management, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Jin, Junchen,Rong, Dingding,Pang, Yuqi,et al. An Agent-Based Traffic Recommendation System: Revisiting and Revising Urban Traffic Management Strategies[J]. IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS,2022:13.
APA Jin, Junchen.,Rong, Dingding.,Pang, Yuqi.,Ye, Peijun.,Ji, Qingyuan.,...&Wang, Fei-Yue.(2022).An Agent-Based Traffic Recommendation System: Revisiting and Revising Urban Traffic Management Strategies.IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS,13.
MLA Jin, Junchen,et al."An Agent-Based Traffic Recommendation System: Revisiting and Revising Urban Traffic Management Strategies".IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS (2022):13.
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