Deep Deterministic Policy Gradient for High-Speed Train Trajectory Optimization

doi:10.1109/TITS.2021.3105380

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

	Deep Deterministic Policy Gradient for High-Speed Train Trajectory Optimization
	Ning, Lingbin 3; Zhou, Min 3; Hou, Zhuopu 3; Goverde, Rob M. P.2; Wang, Fei-Yue 1; Dong, Hairong 3
刊名	IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
	2021-08-25
页码	13
关键词	Rail transportation Training Heuristic algorithms Resistance Optimal control Trajectory optimization Switches High-speed railway train trajectory optimization deep deterministic policy gradient energy efficiency
ISSN号	1524-9050
DOI	10.1109/TITS.2021.3105380
通讯作者	Zhou, Min(zhoumin@bjtu.edu.cn) ; Dong, Hairong(hrdong@bjtu.edu.cn)
英文摘要	This paper proposes a novel train trajectory optimization approach for high-speed railways. We restrict our attention to single train operation scenarios with different scheduled/rescheduled running times aiming at generating optimal train recommended trajectories in real time, which can ensure punctuality and energy efficiency of train operation. A learning-based approach deep deterministic policy gradient (DDPG) is designed to generate optimal train trajectories based on the offline training from the interaction between the agent and the trajectory simulation environment. An allocating running time and selecting operation modes (ARTSOM) algorithm is proposed to improve train punctuality and give a series of discrete operation modes (full traction, cruising, coasting, full braking), and thus to produce a feasible training set for DDPG, which can speed up the training process. Numerical experiments show that an optimized speed profile can be generated by DDPG within seconds on a realistic railway line. In addition, the results demonstrate the generalization ability of trained DDPG in solving TTO problems with different running times and line conditions.
资助项目	National Natural Science Foundation of China[61925302] ; National Natural Science Foundation of China[61790573] ; State Key Laboratory of Rail Traffic Control and Safety[RCS2020ZZ002] ; Beijing Jiaotong University ; Key Project of China Railway Beijing Bureau Group Company, Ltd.[2020AY03]
WOS关键词	TRAFFIC MANAGEMENT ; LEARNING APPROACH ; MODEL ; INTEGRATION ; OPERATION ; ALGORITHM ; SYSTEM ; DELAY
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:000733470200001
资助机构	National Natural Science Foundation of China ; State Key Laboratory of Rail Traffic Control and Safety ; Beijing Jiaotong University ; Key Project of China Railway Beijing Bureau Group Company, Ltd.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/46866]
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	Zhou, Min; Dong, Hairong
作者单位	1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Delft Univ Technol, Dept Transport & Planning, NL-2628 CN Delft, Netherlands 3.Beijing Jiaotong Univ, State Key Lab Rail Traff Control & Safety, Beijing 100044, Peoples R China
推荐引用方式 GB/T 7714	Ning, Lingbin,Zhou, Min,Hou, Zhuopu,et al. Deep Deterministic Policy Gradient for High-Speed Train Trajectory Optimization[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2021:13.
APA	Ning, Lingbin,Zhou, Min,Hou, Zhuopu,Goverde, Rob M. P.,Wang, Fei-Yue,&Dong, Hairong.(2021).Deep Deterministic Policy Gradient for High-Speed Train Trajectory Optimization.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,13.
MLA	Ning, Lingbin,et al."Deep Deterministic Policy Gradient for High-Speed Train Trajectory Optimization".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2021):13.