A DQN-based intelligent control method for heavy haul trains on long steep downhill section. (August 2021)

• Record Type: Journal Article
• Authors: Liu, Wentao; Su, Shuai; Tang, Tao; Wang, Xi
• Is Part Of: Transportation research. Volume 129(2021)
• Publication Date: 2021-08
• Subjects: Intelligent train control -- Heavy haul trains -- Long steep downhill section -- Deep-Q-Network; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2021.103249
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: The train control model is formulated considering the characteristics of Chinese heavy haul railway. The indexes are designed to evaluate the control performance of heavy haul train. An intelligent train control method based on DQN algorithm is proposed. The proposed method is verified using the real-world data of Shuozhou-Huanghua Line. Abstract: 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.