Optimizing Handoff Schemes in Communication-Based Train Control: Reinforcement Learning to Reduce the Age of Information

This article explores the critical challenges and limitations of modern communication-based train control (CBTC) systems, particularly focusing on the dynamic and uncertain nature of train–ground communication. The concept of the Age of Information (AoI) is introduced, highlighting the discrepancies between the actual and derived states used for control, which can compromise system performance and safety. Existing solutions, such as wireless local area networking technologies and reinforcement learning handoff schemes, are discussed, emphasizing their contributions to reducing handoff latency and improving the freshness of information between the train and access points. However, their limitations in adequately addressing issues related to the AoI are critically examined. The article argues for the development of novel control approaches that consider these effects to minimize the information gap and enhance the overall performance of CBTC systems.