LLM助力交通管理，融合多源信息实现智能决策支持。
Large Language Models in Transportation Systems Management and Operations: From Text Reasoning to Multi-modal Decision Support
- 用大模型整合文本、视觉和传感器数据，统一处理异构信息。
- 多模态大模型在融合多种输入时表现更优，提升决策效率。
- 适合交通运营方、智慧城市团队及研究者参考应用落地路径。
交通系统管理与运营（TSMO）日益依赖对多元数据的及时解析，包括各类传感器流、事故报告、出行者反馈和视觉观测。大型语言模型（LLM），特别是新兴的多模态大语言模型（MM-LLMs），为将结构化与非结构化输入整合成面向操作员的决策支持提供了新机制。本文综述了基于LLM与MM-LLM的TSMO应用，涵盖三大领域：交通运营与服务（供给）、出行与车队服务（需求）、数据、建模与决策支持。通过遵循PRISMA指南的筛选流程，我们综合现有研究，区分面向实际运营的应用与原型或新兴概念。进一步识别出数据异构性、实时推理、可解释性、多模态融合及治理等共性挑战。最后，提出当前空白与未来方向，包括本地化适配、边缘部署、基准测试及跨机构协作。总体而言，基于LLM的系统在作为决策支持层方面最具前景，而当需融合异构文本、视觉与传感器输入时，MM-LLMs尤为有价值。
原文摘要 · Abstract (English)
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.
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