Intelligent Railways: Leveraging Retrieval-Augmented Generation for Smarter Systems

Abstract

In an era for faster, secure and convenient mode of travel, there is a need for a system that provides real time updates This paper presents a technical study on the integration of Retrieval-Augmented Generation (RAG) systems within railway operations, emphasizing their potential to enhance decision-making, service delivery, and passenger engagement. The study explores how RAG systems can streamline processes by providing accurate, context-aware responses to inquiries across various railway services, including ticketing, scheduling, and customer support. The findings highlight key challenges such as data security, infrastructure limitations, and the necessity for specialized training, while also emphasizing the operational benefits, including improved efficiency and greater accessibility of services. The methodology encompasses a comprehensive review of existing RAG implementations in transportation, followed by the design and analysis of a prototype system specifically tailored to railway needs, utilizing domain-specific datasets and natural language queries. This study offers valuable insights into the feasibility and scalability of RAG systems for enhancing the efficiency and responsiveness of railway operations.