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

TL;DR

This paper introduces an LLM-enhanced holonic architecture for Urban Air Mobility, enabling scalable, adaptive, and resilient urban transportation systems through decentralized control and AI-driven real-time management.

Contribution

It proposes a novel holonic system architecture incorporating LLMs for managing complex UAM operations, addressing scalability and adaptability challenges.

Findings

Demonstrates dynamic resource allocation in UAM scenarios

Shows real-time replanning and disruption management

Enhances resilience and efficiency of urban transport networks

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.