论文摘要
Enhancing traffic efficiency and alleviating（even circumventing） traffic congestion with advanced traffic signal control（TSC） strategies are always the main issues to be addressed in urban transportation systems. Since model predictive control（MPC） has a lot of advantages in modeling complex dynamic systems, it has been widely studied in traffic signal control over the past 20 years. There is a need for an in-depth understanding of MPC-based TSC methods for traffic networks. Therefore, this paper presents the motivation of using MPC for TSC and how MPC-based TSC approaches are implemented to manage and control the dynamics of traffic flows both in urban road networks and freeway networks. Meanwhile, typical performance evaluation metrics, solution methods, examples of simulations,and applications related to MPC-based TSC approaches are reported. More importantly, this paper summarizes the recent developments and the research trends in coordination and control of traffic networks with MPC-based TSC approaches. Remaining challenges and open issues are discussed towards the end of this paper to discover potential future research directions.
论文目录
文章来源
类型: 期刊论文
作者: Bao-Lin Ye,Weimin Wu,Keyu Ruan,Lingxi Li,Tehuan Chen,Huimin Gao,Yaobin Chen
来源: IEEE/CAA Journal of Automatica Sinica 2019年03期
年度: 2019
分类: 信息科技,工程科技Ⅱ辑
专业: 公路与水路运输
单位: IEEE,the School of Mechanical and Electrical Engineering, Jiaxing University,the State Key Laboratory of Industrial Control, Instituteof Cyber-Systems and Control Technology, Zhejiang University,the Department of Electrical and Computer Engineering, Purdue School of Engineering and Technology,Indiana University-Purdue University Indianapolis,Mechanical Engineering and Mechanics, Ningbo University
基金: supported in part by the National Natural Science Foundation of China(61603154,61773343,61621002,61703217),the Natural Science Foundation of Zhejiang Province(LY15F030021,LY19F030014),Open Research Project of the State Key Laboratory of Industrial Control Technology,Zhejiang University,China(ICT1800407)
分类号: U491.54
页码: 623-640
总页数: 18
文件大小: 513K
下载量: 97
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