Park, Byeong Tak (KT Corporation)

Optimal Fleet Sizing for On-Demand Urban Air Mobility Services Using Queueing-Theoretical Approach

Scheduled for presentation during the Regular Session "S45a-Decision-Making for Urban Air Mobility and Autonomous Logistics" (FR-LM-T45), Friday, November 21, 2025, 11:10−11:30, Gold Coast

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Abstract

Urban Air Mobility (UAM) offers a solution to alleviate traffic congestion by providing on-demand mobility services in urban airspace. Due to the inherently stochastic nature of on-demand requests, maintaining high service availability—particularly vehicle availability at vertiports—poses a significant challenge in planning on-demand UAM services. In this study, we develop a closed Jackson network model with passenger loss to model the dynamics of on-demand UAM systems. We formulate a profit-maximization problem to optimize fleet size while ensuring minimum service availability. The proposed mathematical formulation explicitly incorporates demand uncertainty and evaluates the trade-off between proactive fleet provisioning and reactive passenger compensation strategies. We present an iterative exact search solution method to compute optimal fleet size and demonstrate its effectiveness in a Seoul City case study, where a fleet of 40 vehicles maximizes profit in the baseline. We further show that incorporating reactive passenger compensation strategies increases optimal fleet sizes (to 48 and 55 vehicles for moderate and high compensation levels, respectively) to maintain the target service availability, at the cost of significantly reduced profitability. These findings provide quantitative insights for UAM fleet operators and policymakers planning on-demand mobility services.