Deep Deterministic Policy Gradient for High-Speed Train Trajectory Optimization
文献类型：期刊论文
| 作者 | Ning, Lingbin3; Zhou, Min3 ; Hou, Zhuopu3; Goverde, Rob M. P.2; Wang, Fei-Yue1; Dong, Hairong3
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| 刊名 | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
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| 出版日期 | 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. |
| WOS关键词 | TRAFFIC MANAGEMENT ; LEARNING APPROACH ; MODEL ; INTEGRATION ; OPERATION ; ALGORITHM ; SYSTEM ; DELAY |
| 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研究方向 | Engineering ; Transportation |
| 语种 | 英语 |
| WOS记录号 | WOS:000733470200001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 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. |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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