Print Email Facebook Twitter Deep Deterministic Policy Gradient for High-Speed Train Trajectory Optimization Title Deep Deterministic Policy Gradient for High-Speed Train Trajectory Optimization Author Ning, Lingbin (Beijing Jiaotong University) Zhou, Min (Beijing Jiaotong University) Hou, Zhuopu (Beijing Jiaotong University) Goverde, R.M.P. (TU Delft Transport and Planning) Wang, Fei Yue (Chinese Academy of Sciences) Dong, Hairong (Beijing Jiaotong University) Date 2022 Abstract 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. Subject deep deterministic policy gradientenergy efficiencyHigh-speed railwaytrain trajectory optimization To reference this document use: http://resolver.tudelft.nl/uuid:ead36177-821d-4233-a014-71f860d662fd DOI https://doi.org/10.1109/TITS.2021.3105380 Embargo date 2023-07-01 ISSN 1524-9050 Source IEEE Transactions on Intelligent Transportation Systems, 23 (8), 11562-11574 Bibliographical note Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. Part of collection Institutional Repository Document type journal article Rights © 2022 Lingbin Ning, Min Zhou, Zhuopu Hou, R.M.P. Goverde, Fei Yue Wang, Hairong Dong Files PDF Deep_Deterministic_Policy ... zation.pdf 3.67 MB Close viewer /islandora/object/uuid:ead36177-821d-4233-a014-71f860d662fd/datastream/OBJ/view