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- Title
基于Q-learning的高速铁路列车动态调度方法.
- Authors
韩忻辰; 俞胜平; 袁志明; 程丽娟
- Abstract
As the backbone of the national comprehensive transportation system, high-speed railway has achieved rapid and vigorous development in the past decade. At the same time, its rapid development has also caused the phenomena of complicated road networks and wide distribution areas. These phenomena have placed higher requirements on highspeed railway scheduling. Unexpected events will affect the time delay of trains, and even the delay time will spread along the road network, causing large-area trains to arrive or departure late. However, the manual scheduling method is poorly forward-looking and pertinent, and it is difficult to quickly adjust the affected trains. In view of the above problems, this paper establishes a high-speed railway dynamic scheduling model with the minimum the sum of delay time as the objective function. Based on this model, an environment for interacting with the agent is designed, and the Q-learning algorithm is used to solve the model. Finally, the simulation examples verify the rationality of the simulation environment and the effectiveness of the Q-learning algorithm for the dynamic scheduling problem. It can provide a good basis for dispatchers to make more optimal decisions.
- Subjects
WIDE area networks; TRAIN delays &; cancellations; TRAIN schedules; REINFORCEMENT learning; RAILROADS; DYNAMIC models; HIGH speed ground transportation
- Publication
Control Theory & Applications / Kongzhi Lilun Yu Yinyong, 2021, Vol 38, Issue 10, p1511
- ISSN
1000-8152
- Publication type
Article
- DOI
10.7641/CTA.2021.00612