An optimization framework for urban air mobility (UAM) planning and operations

This paper presents an optimized decision-support framework for the planning and operation of Urban Air Mobility (UAM) systems. Alleviating traffic congestion in metropolitan areas has been a persistent challenge for decades, leading to increased interest in aerial mobility solutions. Recent advancements in distributed electric propulsion, battery technology, and autonomous navigation have made electric vertical take-off and landing (eVTOL) aircraft a feasible option for intercity transport. For efficient UAM systems, we optimize the high-level planning of UAMs, i.e., determine the numbers of eVTOLs, vertiport spaces, and chargers, together with lower-level operations to control each eVTOL's operational state between in-service, charging, idling, and relocating. Accounting for spatio-temporal demand and cost heterogeneity, we formulate the UAM optimization framework as a mixed-integer programming problem. In our numerical study, we analyze a scenario involving three hypothetical vertiports in the Seoul Metropolitan Area, South Korea. The results reveal relationships between the optimal solution and several exogenous factors critical to eVTOL operations, including the targeted level of service for users and eVTOL charging speed. Additionally, we conduct Monte Carlo simulations to demonstrate the robustness of our solution against stochastic demand and variations in electric consumption.