Urban Air Mobility as a System of Systems: An LLM-Enhanced Holonic Approach
This work addresses scalability and adaptability issues in UAM systems for urban transportation, representing an incremental advancement by integrating LLMs into a holonic framework.
The paper tackles the challenges of Urban Air Mobility (UAM) by proposing an intelligent holonic architecture enhanced with Large Language Models (LLMs) to manage system complexities, demonstrating through a case study that it enables dynamic resource allocation and real-time replanning for more resilient and efficient urban transportation networks.
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.