A Tool for the optimization of vertiport locations for urban and suburban passenger transportation

Innovative Air Mobility (IAM), depends critically on the strategic placement of vertiports to enable safe and efficient operations. This paper presents a Hub Location Problem (HLP) framework for passenger vertiport siting that integrates demand estimation, operational conditions, and cost structures. Potential demand for the IAM air taxi is derived from mobile network data. Demand for taxi is estimated using logistic regression, with the likelihood of taxi passengers shifting to air taxis being calculated based on socioeconomic profiles and travel times. A case study in the Madrid region illustrates the ability of the framework to capture realistic demand and infrastructure requirements, offering a holistic and data-driven approach to the planning of IAM networks. A sensitivity analysis of the model is performed, showing that the selected locations are consistent in moderate user adoption scenarios, but change significantly when considering very high or very low adoption scenarios.