A Retrieval-Augmented Generation System for Exploring Intelligent Transportation System Reference Architectures

At the core of Intelligent Transportation Systems (ITS) lies a wide range of applications leveraging communication and information technologies to support safer, more efficient, and more sustainable mobility. However, due to their inherent complexity, ITS architectural design remains challenging, particularly for designers unfamiliar with the domain. A promising approach is to build upon existing ITS reference architectures, which are validated frameworks encapsulating essential architectural knowledge. Yet these reference architectures remain underutilized, in part because the architectural elements and reusable components they provide are insufficiently known and disseminated. To address this gap, we propose a Retrieval-Augmented Generation (RAG) system that enables users to explore such architectures through a question–answer interface. The system combines the generative capabilities of large language models (LLMs) with structured knowledge from established ITS reference architectures, including ARC-IT and FRAME, and has been evaluated from both technical and user-oriented perspectives.