From Patchwork to Network: A Comprehensive Framework for Demand Analysis and Fleet Optimization of Urban Air Mobility

2025-10-05

AI Summary

Urban Air Mobility (UAM) faces significant scalability challenges due to high infrastructure costs and complex air-ground coordination. To address this, this paper proposes an air-ground integrated UAM network modeling and optimization framework leveraging existing regional airports. We develop LPSim—a large-scale parallel simulation platform—that uniquely integrates multi-GPU acceleration, demand-balancing search algorithms, dynamic scheduling of heterogeneous electric vertical take-off and landing (eVTOL) fleets, and coupled ground shuttle systems. By jointly optimizing demand forecasting, fleet composition, and multimodal connectivity, the approach substantially lowers deployment barriers. Empirical evaluation in the San Francisco Bay Area demonstrates an average travel time reduction of 20.7 minutes across 230,000 trips, validating the “light-infrastructure, strong-coordination” paradigm. This work provides a scalable methodology and technical foundation for transitioning UAM from conceptual exploration to practical, operationally viable deployment.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Scalability of ML Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Urban Air Mobility (UAM) presents a transformative vision for metropolitan transportation, but its practical implementation is hindered by substantial infrastructure costs and operational complexities. We address these challenges by modeling a UAM network that leverages existing regional airports and operates with an optimized, heterogeneous fleet of aircraft. We introduce LPSim, a Large-Scale Parallel Simulation framework that utilizes multi-GPU computing to co-optimize UAM demand, fleet operations, and ground transportation interactions simultaneously. Our equilibrium search algorithm is extended to accurately forecast demand and determine the most efficient fleet composition. Applied to a case study of the San Francisco Bay Area, our results demonstrate that this UAM model can yield over 20 minutes' travel time savings for 230,000 selected trips. However, the analysis also reveals that system-wide success is critically dependent on seamless integration with ground access and dynamic scheduling.

Problem

• Optimizing urban air mobility network with existing infrastructure
• Developing parallel simulation for demand and fleet co-optimization
• Analyzing travel time savings and ground integration dependencies

Innovation

• Leverages existing airports with optimized heterogeneous fleet
• Uses multi-GPU parallel simulation for co-optimization
• Extends equilibrium algorithm for demand forecasting