Leveraging recent advances in generative AI, multi-agent systems are increasingly being developed to enhance the functionality and efficiency of smart city applications. This paper explores the transformative potential of large language models (LLMs) and emerging Retrieval-Augmented Generation (RAG) technologies in Intelligent Transportation Systems (ITS), paving the way for innovative solutions to address critical challenges in urban mobility. We begin by providing a comprehensive overview of the current state-of-the-art in mobility data, ITS, and Connected Vehicles (CV) applications. Building on this review, we discuss the rationale behind RAG and examine the opportunities for integrating these Generative AI (GenAI) technologies into the smart mobility sector. We propose a conceptual framework aimed at developing multi-agent systems capable of intelligently and conversationally delivering smart mobility services to urban commuters, transportation operators, and decision-makers. Our approach seeks to foster an autonomous and intelligent approach that (a) promotes science-based advisory to reduce traffic congestion, accidents, and carbon emissions at multiple scales, (b) facilitates public education and engagement in participatory mobility management, and (c) automates specialized transportation management tasks and the development of critical ITS platforms, such as data analytics and interpretation, knowledge representation, and traffic simulations. By integrating LLM and RAG, our approach seeks to overcome the limitations of traditional rule-based multi-agent systems, which rely on fixed knowledge bases and limited reasoning capabilities. This integration paves the way for a more scalable, intuitive, and automated multi-agent paradigm, driving advancements in ITS and urban mobility.

本研究针对智能交通系统在城市出行中面临的关键挑战，探讨了大型语言模型（LLM）和检索增强生成（RAG）技术在提升综合出行效率方面的潜力。文章提出了一种概念框架，旨在结合生成AI技术开发智能化、多智能体的出行服务系统，以减少交通拥堵、事故和碳排放，并推动公众参与交通管理。

GenAI-powered Multi-Agent Paradigm for Smart Urban Mobility:
  Opportunities and Challenges for Integrating Large Language Models (LLMs) and
  Retrieval-Augmented Generation (RAG) with Intelligent Transportation Systems

基于生成AI的多智能体范式在智能城市交通中的机遇与挑战：集成大型语言模型和检索增强生成技术与智能交通系统的探讨