这篇综述为交通领域的从业者提供了LLM应用的完整地图——从传感器数据到决策支持，做智慧交通或城市管理的团队可以直接参考其中的案例和挑战，避免重复踩坑。
这篇综述论文系统回顾了大语言模型（LLM）和多模态大语言模型（MM-LLM）在交通系统管理与运营（TSMO）中的应用。研究覆盖了交通运营与服务、出行与车队服务、数据建模与决策支持三个领域，通过PRISMA方法筛选并分析了现有研究。论文指出，LLM在数据异构性、实时推理、可解释性、多模态融合和治理方面仍面临挑战，但作为决策支持层具有巨大潜力，特别是MM-LLM在整合文本、视觉和传感器数据时表现突出。未来方向包括本地化适配、边缘部署、基准测试和跨机构协作。
Large Language Models in Transportation Systems Management and Operations: From Text Reasoning to Multi-modal Decision Support
Transportation systems management and operations (TSMO) increasingly depends on timely interpretation of heterogeneous data, from various sensor streams, incident reports, traveler feedback, and visual observations. Large language models (LLMs), including emerging multi-modal large language models (MM-LLMs), provide a new mechanism for integrating these structured and unstructured inputs into operator-facing decision support. This survey paper reviews LLM- and MM-LLM-based applications in TSMO across three domains: transportation operations & services (supply), mobility & fleet services (demand), and data, modeling & decision support. Using a PRISMA-guided screening process, we synthesize current studies while distinguishing operationally oriented applications from prototype and emerging concepts. We further identify recurring challenges in data heterogeneity, real-time inference, explainability, multi-modal fusion, and governance. Finally, we outline existing gaps and future directions in localized adaptation, edge deployment, benchmarking, and cross-agency collaboration. Overall, LLM-based systems appear most promising as a decision-support layer, with MM-LLMs offering particular value when heterogeneous text, visual, and sensor inputs must be integrated.