Role-Aware Agentic Retrieval-Augmented Generation for Urban Rail Transit Fault Management

Abstract

Urban rail transit systems require reliable decision-support methods to assist operators in handling complex fault events. Although large language models provide strong reasoning and knowledge integration capabilities, their open-ended generation may produce responses that are difficult to align with operational procedures. To address this issue, we propose a role-aware agentic retrieval-augmented generation framework for transit fault management. The framework integrates keyword-based retrieval, role-aware multi-agent reasoning, and constrained action generation within a unified architecture. Operational procedures and historical cases retrieved from transit knowledge repositories provide evidence for agent reasoning, while constrained outputs ensure that generated instructions remain consistent with predefined operational actions. Experimental results on a real-world urban rail transit emergency exercise dataset demonstrate that the proposed framework improves the semantic score from 0.893 to 0.965 and increases Role F1 score from 0.824 to 0.915 compared with an Agentic Retrieval-Augmented Generation (RAG) baseline while maintaining comparable inference efficiency. These results demonstrate the potential of the proposed framework for decision support in urban rail transit fault scenarios.