From Patchwork to Network: A Comprehensive Framework for Demand Analysis and Fleet Optimization of Urban Air Mobility
Xuan Jiang, Xuanyu Zhou, Yibo Zhao, Shangqing Cao, Dingyi Zhuang, Jinhua Zhao, Haris Koutsopoulos, Shenhao Wang, Mark Hansen, Raja Sengupta
TL;DR
<3-5 sentence high-level summary> The paper presents LPSim, a GPU-accelerated framework to co‑optimize urban air mobility demand, heterogeneous fleet operations, and ground transportation within an existing regional airport network. It develops a day‑to‑day UAM/ground allocation equilibrium and a two‑phase fleet sizing approach based on a time‑expanded network flow model. In a San Francisco Bay Area case study, the framework demonstrates substantial travel‑time savings for a subset of trips with 241 aircraft and about 5,000 daily flights, while highlighting the critical role of ground access integration and dynamic scheduling. The study also discusses practical challenges, including economic viability and airspace management, and points to future work needed for real‑world deployment.</paper_summary>
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.