A DQN-based intelligent control method for heavy haul trains on long steep downhill section

The cyclic air braking strategy on the long and steep downhill section is one of the biggest challenges for heavy haul railway lines in China. To deal with this problem, this paper presents an intelligent control method based on the Deep-Q-Network (DQN) algorithm for the heavy haul train running on the long and steep downhill section. The aim of the optimal train control problem in the paper is to enhance the train operation performance in regard to the operational safety, maintenance costs and running efficiency. In the train control model, the characteristics of the heavy haul train, the speed limits and constraints on the air-refilling time of the train pipe are taken into consideration. Then the train control process on the long and steep downhill section is described as a Markov decision process for the application of the reinforcement learning (RL) technique. Further, the critical elements of RL are designed and an intelligent control method on the basis of the DQN algorithm is developed to address the optimal train control problem in this paper. Finally, experimental simulations are carried out with the actual data of the Shuozhou-Huanghua Line such that the effectiveness and robustness of the proposed DQN-based control method are verified.

• Record URL:
  https://doi.org/10.1016/j.trc.2021.103249
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0968090X2100262X
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • © 2021 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Liu, Wentao
  • Su, Shuai
  • Tang, Tao
  • Wang, Xi
• Publication Date: 2021-8

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: 103249
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 129
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Air brakes; Freight trains; Heavy haul; Hills; Intelligent control systems; Machine learning; Markov processes
• Geographic Terms: China
• Subject Areas: Freight Transportation; Operations and Traffic Management; Railroads; Vehicles and Equipment;

Filing Info

• Accession Number: 01782757
• Record Type: Publication
• Files: TRIS
• Created Date: Sep 24 2021 10:20AM