LLM-RailATD: Large Language Model-Driven Autonomous Train Dispatching for High-Speed Railways in Case of Emergencies

Efficient and reliable train dispatching is essential for ensuring the safety and punctuality of high-speed railways (HSRs) during emergencies. Current train dispatching relies heavily on manual operations performed by dispatchers and is governed by stringent operational procedures and safety regulations that require strict adherence to established rules. These complex tasks are highly prone to human error, especially in high-stress environments, and existing intelligent methods addressing specific aspects still require significant human intervention. To address these challenges, this article leverages the advanced comprehension, strategic planning, and coordination capabilities of large language models (LLMs) to introduce LLM-RailATD, an autonomous train dispatching method designed explicitly for HSRs during emergencies. LLM-RailATD operates through a structured four-stage approach-describe, plan, execute, and reason (DPER)-to interpret train operation scenarios, plan and execute complex train dispatching tasks, and autonomously handle errors arising during the dispatching process. Computational experiments based on real railway data illustrate the effectiveness of LLM-RailATD in autonomous train dispatching, which achieves a success rate (SR) of 74% in emergency scenarios. In addition, ablation studies validate the contributions of the individual modules of LLM-RailATD and the prompt design, highlighting their importance in achieving reliable performance.