用大模型让城市空中交通自主协调，提升韧性与效率。
Urban Air Mobility as a System of Systems: An LLM-Enhanced Holonic Approach
- 构建基于大模型的分布式智能单元架构，实现空车、地面交通与起降场实时协同。
- 在多模式交通案例中实现动态资源分配与实时重规划，无需中央控制。
- 适合研究智能交通、分布式系统及人本化城市出行的学者与工程师。
城市空中交通（UAM）是一种新兴的系统之系统（SoS），面临系统架构、规划、任务管理与执行的挑战。传统方法在动态复杂环境中难以实现可扩展性、适应性与资源无缝集成。本文提出一种融合大语言模型（LLM）的智能分层架构，其中各智能体（holon）具备半自主性，可实时协调空中出租车、地面交通与垂直起降场。LLM处理自然语言输入，生成自适应计划，并应对天气变化或空域关闭等扰动。通过电动滑板车与空中出租车的多模式交通案例研究，验证了该架构在无中心控制下实现动态资源调配、实时重规划与自主适应的能力，构建更具韧性和效率的城市交通网络。本工作为韧性、以人为本的UAM生态系统奠定基础，未来将探索混合人工智能融合与真实世界验证。
原文摘要 · Abstract (English)
Urban Air Mobility (UAM) is an emerging System of System (SoS) that faces challenges in system architecture, planning, task management, and execution. Traditional architectural approaches struggle with scalability, adaptability, and seamless resource integration within dynamic and complex environments. This paper presents an intelligent holonic architecture that incorporates Large Language Model (LLM) to manage the complexities of UAM. Holons function semi autonomously, allowing for real time coordination among air taxis, ground transport, and vertiports. LLMs process natural language inputs, generate adaptive plans, and manage disruptions such as weather changes or airspace closures.Through a case study of multimodal transportation with electric scooters and air taxis, we demonstrate how this architecture enables dynamic resource allocation, real time replanning, and autonomous adaptation without centralized control, creating more resilient and efficient urban transportation networks. By advancing decentralized control and AI driven adaptability, this work lays the groundwork for resilient, human centric UAM ecosystems, with future efforts targeting hybrid AI integration and real world validation.
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