Data-driven modeling of urban airspace availability for air mobility operations in the São Paulo Metropolitan Region - Turchetti, João Vitor; Condé Rocha Murça, Mayara (Transportes, 2024)
Urban Air Mobility (UAM) is an emerging form of transportation that is expected to introduce novel flight networks into already busy and complex airspace surrounding major cities and metropolitan regions. This work provides a data-driven approach to modeling the urban airspace availability for emerging UAM operations toward supporting their safe and efficient integration. Using historical flight tracking data, clustering analysis is first performed to learn the current patterns of urban airspace use by conventional traffic and identify the airspace volumes that are least constrained and best accessible for UAM flights. Meteorological data is then incorporated into the machine learning framework to create a probabilistic model of the spatiotemporal distribution of conventional traffic flows. This model enables the prediction of active airport arrival/departure patterns and the resulting airspace availability for UAM given dynamic operational conditions. The data-based approach is demonstrated for the São Paulo metropolitan area, which is the largest in Brazil and a promising market for UAM. It allowed for a high-fidelity characterization of the São Paulo urban airspace use patterns as well as for accurate predictions of the available airspace for UAM, bringing novel insights and capabilities in support of dynamic and efficient urban airspace management.