Modern transportation systems face pressing challenges due to increasing demand, dynamic environments, and heterogeneous information integration. The rapid evolution of Large Language Models (LLMs) offers transformative potential to address these challenges. Extensive knowledge and high-level capabilities derived from pretraining evolve the default role of LLMs as text generators to become versatile, knowledge-driven task solvers for intelligent transportation systems. This survey first presents LLM4TR, a novel conceptual framework that systematically categorizes the roles of LLMs in transportation into four synergetic dimensions: information processors, knowledge encoders, component generators, and decision facilitators. Through a unified taxonomy, we systematically elucidate how LLMs bridge fragmented data pipelines, enhance predictive analytics, simulate human-like reasoning, and enable closed-loop interactions across sensing, learning, modeling, and managing tasks in transportation systems. For each role, our review spans diverse applications, from traffic prediction and autonomous driving to safety analytics and urban mobility optimization, highlighting how emergent capabilities of LLMs such as in-context learning and step-by-step reasoning can enhance the operation and management of transportation systems. We further curate practical guidance, including available resources and computational guidelines, to support real-world deployment. By identifying challenges in existing LLM-based solutions, this survey charts a roadmap for advancing LLM-driven transportation research, positioning LLMs as central actors in the next generation of cyber-physical-social mobility ecosystems. Online resources can be found in the project page: https://github.com/tongnie/awesome-llm4tr.

翻译：现代交通系统因需求增长、动态环境及异构信息整合而面临紧迫挑战。大型语言模型（LLMs）的快速发展为解决这些挑战提供了变革性潜力。通过预训练获得的海量知识与高级能力，使LLMs从默认的文本生成器演变为智能交通系统中多功能的、知识驱动的任务求解器。本综述首先提出LLM4TR——一个新颖的概念框架，将LLMs在交通领域的作用系统性地归纳为四个协同维度：信息处理器、知识编码器、组件生成器和决策促进器。通过统一的分类体系，我们系统阐释了LLMs如何连接碎片化的数据管道、增强预测分析能力、模拟类人推理，并在交通系统的感知、学习、建模与管理任务中实现闭环交互。针对每个角色，本文综述涵盖了从交通预测、自动驾驶到安全分析与城市出行优化的多样化应用，重点探讨了LLMs的涌现能力（如上下文学习与分步推理）如何提升交通系统的运营管理水平。我们进一步整理了实用指南，包括可用资源与计算规范，以支持实际部署。通过指出现有基于LLM的解决方案面临的挑战，本综述规划了推进LLM驱动交通研究的路线图，将LLMs定位为下一代信息-物理-社会移动生态系统的核心参与者。在线资源详见项目页面：https://github.com/tongnie/awesome-llm4tr。