Reinforcement learning (RL) has recently been applied to solve railway timetabling problems. In this paper, we provide a detailed overview of the state-of-the-art research for RL in railway timetabling. Specifically, we categorize RL into basic RL and deep reinforcement learning (DRL), and further divide the research of railway timetabling into scheduling and rescheduling for exhaustively review and discussion. The present research on RL in railway timetabling is still in the primary stage and the scale of problems that can be solved is still limited. However, the applications of RL shows great promise and excitement, with significant potential for addressing various challenges in railway planning and management in the future.
A Literature Review of Reinforcement Learning in Railway Timetabling
Lecture Notes in Civil Engineering
International Conference on Traffic and Transportation Studies ; 2024 ; Lanzhou, China August 23, 2024 - August 25, 2024
The Proceedings of the 11th International Conference on Traffic and Transportation Studies ; Chapter : 60 ; 541-550
2024-11-14
10 pages
Article/Chapter (Book)
Electronic Resource
English
Passenger Perspectives in Railway Timetabling: A Literature Review
Taylor & Francis Verlag | 2016
|Passenger Perspectives in Railway Timetabling: A Literature Review
Online Contents | 2016
|