An Intelligent Train Operation Method Based on Event-Driven Deep Reinforcement Learning

Liqing ZHANG, Leong Hou U*, Mingliang ZHOU, Zhenning LI

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

15 Citations (Scopus)

Abstract

Train operation control in urban railways is challenging due to its high dynamics, complex environment, and level of comfort and safety. To address these challenges, in this article, the authors propose a new deep reinforcement-based train operation (DRTO) method which includes: 1) A deterministic deep reinforcement learning algorithm, 2) a dynamic incentive system, which is used to ensure safe operation in a multitrain environment, and 3) an event-driven method, which is used to improve the DRTO performance based on an event-driven strategy. To evaluate the performance, we thoroughly compare the proposed method with other operation control solutions on both synthetic and real datasets. Our results demonstrate that DRTO is effective in: 1) Decreasing the energy consumption of train operation, 2) increasing passenger comfort, and 3) achieving a good tradeoff between efficiency and safety. In addition, the effectiveness of the event-driven strategy and the dynamic incentive system is demonstrated in the experiments.

Original languageEnglish
Pages (from-to)6973-6980
Number of pages8
JournalIEEE Transactions on Industrial Informatics
Volume18
Issue number10
Early online date24 Dec 2021
DOIs
Publication statusPublished - Oct 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2005-2012 IEEE.

Keywords

  • Deep deterministic policy gradient (DDPG)
  • deep reinforcement-based train operation (DRTO)
  • event driven

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