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An End-to-End Recommendation System for Urban Traffic Controls and Management Under a Parallel Learning Framework

文献类型：期刊论文

作者Jin, Junchen2,4; Guo, Haifeng1,4; Xu, Jia3,4; Wang, Xiao2; Wang, Fei-Yue2
刊名IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
出版日期2021-03-01
卷号22期号:3页码:1616-1626
关键词Control systems Urban areas Timing Adaptive systems Real-time systems Recurrent neural networks Process control Intelligent traffic control traffic signal control parallel learning recommendation systems deep neural networks
ISSN号1524-9050
DOI10.1109/TITS.2020.2973736
通讯作者Guo, Haifeng(guohf@zjut.edu.cn)
英文摘要A paradigm shift towards agile and adaptive traffic signal control empowered with the massive growth of Big Data and Internet of Things (IoT) technologies is emerging rapidly for Intelligent Transportation Systems. Generally, an adaptive signal control system fine-tunes signal timing parameters based on pre-defined control hyperparameters using instantaneous traffic detection information. Once traffic pattern changes, those hyperparameters (e.g., maximum and minimum green times) need to be adjusted according to the evolution of traffic dynamics over a very short-term period. Such adjustment processes are usually conducted by professional and experienced traffic engineers. Here we present a human-in-the-loop parallel learning framework and its utilization in an end-to-end recommendation system that mimics and enhances professional signal control engineers' behaviors. The system has been deployed into a real-world application for an extended period in Hangzhou, China, where signal control hyperparameters are recommended based on large-scale multidimensional traffic datasets. Experimental evaluations demonstrate significant improvements in traffic efficiency through the use of our signal recommendation system.
资助项目China Post-Doctoral Science Foundation[2019M660136] ; Natural Science Foundation of Zhejiang Province[LY20E080023] ; National Natural Science Foundation of China[U1811463]
WOS研究方向Engineering ; Transportation
语种英语
WOS记录号WOS:000626338600023
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构China Post-Doctoral Science Foundation ; Natural Science Foundation of Zhejiang Province ; National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/44085]  
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Guo, Haifeng
作者单位1.Zhejiang Univ Technol, Coll Informat Engn, Hangzhou 310013, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
3.Zhejiang Univ, Coll Civil Engn & Architecture, Hangzhou 310058, Peoples R China
4.Enjoyor Co Ltd, Hangzhou 310030, Peoples R China
推荐引用方式
GB/T 7714		 Jin, Junchen,Guo, Haifeng,Xu, Jia,et al. An End-to-End Recommendation System for Urban Traffic Controls and Management Under a Parallel Learning Framework[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2021,22(3):1616-1626.
APA Jin, Junchen,Guo, Haifeng,Xu, Jia,Wang, Xiao,&Wang, Fei-Yue.(2021).An End-to-End Recommendation System for Urban Traffic Controls and Management Under a Parallel Learning Framework.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,22(3),1616-1626.
MLA Jin, Junchen,et al."An End-to-End Recommendation System for Urban Traffic Controls and Management Under a Parallel Learning Framework".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 22.3(2021):1616-1626.

入库方式： OAI收割

来源：自动化研究所

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