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

doi:10.1109/TITS.2020.2973736

CASIA OpenIR > 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

	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, Xiao2 ; Wang, Fei-Yue2
发表期刊	IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
ISSN	1524-9050
	2021-03-01
卷号	22 期号:3 页码:1616-1626
通讯作者	Guo, Haifeng(guohf@zjut.edu.cn)
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.
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
DOI	10.1109/TITS.2020.2973736
收录类别	SCI
语种	英语
资助项目	China Post-Doctoral Science Foundation[2019M660136] ; Natural Science Foundation of Zhejiang Province[LY20E080023] ; National Natural Science Foundation of China[U1811463]
项目资助者	China Post-Doctoral Science Foundation ; Natural Science Foundation of Zhejiang Province ; National Natural Science Foundation of China
WOS研究方向	Engineering ; Transportation
WOS类目	Engineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology
WOS记录号	WOS:000626338600023
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	被引频次：45[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/44086
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	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
第一作者单位	中国科学院自动化研究所
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.