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

doi:10.1109/TITS.2020.2973736

CORC > 自动化研究所 > 中国科学院自动化研究所 > 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

	An End-to-End Recommendation System for Urban Traffic Controls and Management Under a Parallel Learning Framework
	Jin, Junchen 1,2; Guo, Haifeng 1,3; Xu, Jia 1,4; Wang, Xiao 2; Wang, Fei-Yue 2
刊名	IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
	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
DOI	10.1109/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
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000626338600023
资助机构	China Post-Doctoral Science Foundation ; Natural Science Foundation of Zhejiang Province ; National Natural Science Foundation of China
内容类型	期刊论文
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.Enjoyor Co Ltd, Hangzhou 310030, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 3.Zhejiang Univ Technol, Coll Informat Engn, Hangzhou 310013, Peoples R China 4.Zhejiang Univ, Coll Civil Engn & Architecture, Hangzhou 310058, 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.