Urban Air Mobility as a System of Systems: An LLM-Enhanced Holonic Approach

2025-05-01

AI Summary

To address challenges in urban air mobility (UAM)—including poor system scalability in dynamic, complex environments, delayed task re-planning, and overreliance on centralized resource scheduling—this work proposes a large language model (LLM)-enhanced holonic intelligent architecture. Grounded in holonic systems theory, the framework integrates LLMs, multi-agent coordination, and real-time event-driven planning to enable autonomous air-ground capacity collaboration and natural-language-based interactive decision-making. Evaluated in a multimodal e-scooter–air-taxi scenario, the system demonstrates sub-second disturbance response, decentralized dynamic resource allocation, and fault-tolerant adaptability without single points of failure, significantly improving operational efficiency and system resilience. The core contribution is the first deep integration of LLMs into a holonic architecture, thereby transcending conventional centralized control paradigms and establishing a foundation for scalable, adaptive UAM orchestration.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• Addressing scalability and adaptability challenges in Urban Air Mobility systems
• Managing dynamic resource integration in complex urban transportation environments
• Enabling real-time coordination and disruption handling without centralized control

Innovation

• LLM-enhanced holonic architecture for UAM
• Real-time coordination among air taxis and vertiports
• Decentralized control with autonomous adaptation