Urban Air Mobility (UAM) has emerged as a promising mobility option for intra-city, inter-city, and regional trips. However, studies on its critical infrastructure planning (i.e., vertiport and vertipad) often ignore demand heterogeneity and rely on uniform configurations that risk overinvestment. To address this limitation, we develop an integrated optimization framework that jointly determines vertiport locations and vertipad capacities under infrastructure cost, capacity, and demand constraints. The model integrates a multinomial logit formulation to estimate UAM demand with multi-server queuing theory to capture passenger waiting times at vertiports. To solve the resulting nonlinear programming problem, we employ a Lagrangian relaxation approach combined with Benders decomposition. Empirical results from a high-density area in Shanghai show that UAM adoption is driven more by service quality than by network size. Average waiting time and ground access disutility are the dominant determinants: overly strict vertiport service-distance requirements can render deployment infeasible, whereas relaxing them reduces investment but lowers adoption. Sensitivity analyses further indicate that demand-side uncertainty (value of time and public acceptance) primarily affects required vertipad capacity rather than vertiport locations. In addition, flight demand peaks around 20 km, and expanding per-site vertipad limits yields diminishing returns beyond 2–3 vertipads. Overall, these findings suggest that effective UAM planning depends more on meeting passenger expectations than on large-scale infrastructure expansion. The proposed approach offers planners a scalable and practical tool for balancing service quality and cost, ensuring that early-stage UAM deployment avoids overbuilding and remains financially sustainable.
Lyu et al. (2026) studied this question.