Intelligent human-computer interactive training assistant system for rail systems

Abstract

In recent years, railway construction in China has developed vigorously. With continuous improvements in the high-speed railway network, the focus is gradually shifting from large-scale construction to large-scale operations. However, several challenges have emerged within the high-speed railway dispatching and command system, including the heavy workload faced by dispatchers, the difficulty of quantifying subjective expertise, and the need for effective training of professionals. Amid the growing application of artificial intelligence technologies in railway systems, this study leverages Large Language Model (LLM) technology. LLMs bring enhanced intelligence, predictive capabilities, robust memory, and adaptability to diverse real-world scenarios. This study proposes a human-computer interactive intelligent scheduling auxiliary training system built on LLM technology. The system offers capabilities including natural dialogue, knowledge reasoning, and human feedback learning. With broad applicability, the system is suitable for vocational education, guided inquiry, knowledge-based Q&A, and other training scenarios. Validation results demonstrate its effectiveness in auxiliary training, providing substantial support for educators, students, and dispatching personnel in colleges and professional settings.

Keywords

High-speed railway / Dispatch training assistance / Large language model / Human-computer interactive training assistant system / Reinforcement learning

CRediT authorship contribution statement

Yuexuan Li: Writing – original draft, Software, Methodology, Investigation, Formal analysis, Data curation. Junhua Chen: Writing – review & editing, Supervision, Resources, Project administration, Conceptualization. Xiangyong Luo: Writing – review & editing, Visualization, Validation, Methodology. Han Zheng: Writing – review & editing, Supervision, Methodology, Funding acquisition, Conceptualization.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work was supported by the Talent Fund of Beijing Jiaotong University (Grant No. 2024XKRC055).

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