Data-driven framework for urban air mobility planning: Integrating socioeconomic and operational factors for optimal network design in São Paulo, Brazil

Urban Air Mobility (UAM) has been positioned as a promising alternative for large metropolitan regions, using electric vertical take-off and landing (eVTOL) aircraft connected in vertiport networks, with potential benefits in travel time, emissions, and noise compared with traditional transport modes. However, the early design of these networks depends on the strategic location of the vertiports in areas with a high ability to pay, integration with the existing aeronautical infrastructure, and compatibility with operational concepts developed by air navigation service providers, which recommend using helicopter routes and already certified infrastructure in the initial phases. This study proposes an income-based spatial demand analysis model for the location of UAM vertiports in the São Paulo Metropolitan Region (RMSP), using as reference the population, trip, and income data from the 2023 Origin-Destination (OD) Survey of the São Paulo Metro, the distribution of helipads registered by the Brazilian National Civil Aviation Agency (ANAC) and the network of Helicopter Special Routes (REH) connected to the urban airport system provided by the Aeronautical Information Service Web (AISWEB). Methodologically, the helipads are grouped using clustering algorithms based on income, and the medoids of the clusters are selected as candidate vertiports. The graph composed of REH waypoints and these candidate vertiports, along with the main airports of the RMSP, is then used to compute the shortest paths using an adapted Dijkstra’s algorithm. Finally, we propose a comprehensive step-by-step procedure to estimate the initial origin–destination demand for UAM. The results suggest that combining income patterns, reuse of the existing helipad infrastructure in the RMSP, and route modeling over the REH network can support an initial phase of UAM focused on maximizing market viability and demand satisfaction, in line with operational guidelines from DECEA, FAA, and Eurocontrol. An iterative network expansion using real helicopter movement data further demonstrates the framework’s scalability. Finally, a discussion of policy implications highlights the equity risks of income-based planning and proposes empirical movement data as a market-observed criterion for evidence-based network expansion decisions.