Vertiport location planning for urban air mobility airport shuttle services under Demand uncertainty

Gideon Gottschalg, Arne K. Strauss, Nikola Ivanov, Bojana Mirković and Juan Blasco Puyuelo

Transportation Research Part A: Policy and Practice, 2026, vol. 207, issue C

Abstract: In recent years, the number of electric vertical take-off and landing (eVTOL) aircraft designs and start-ups has significantly increased. However, there are still several challenges to be addressed before Urban Air Mobility (UAM) services using eVTOLs could commence. Besides regulatory and safety aspects, one key question is how to implement a profitable operation of a fleet of eVTOLs given uncertain passenger adoption. The main investment decision in the strategic planning phase involves the number and location of vertiports to build in the network, and the number of vehicles in the fleet. A risk-averse two-stage stochastic optimization framework is developed for an eVTOL airport shuttle service as the most likely initial UAM use case. In the first stage, a strategic decision on the number of eVTOLs and number and location of vertiports in the urban area and at the airport site must be made under uncertain demand. In the second stage, the operational phase of the chosen vertiport network is optimised for a realised demand scenario. The framework uses a simulation-based learning algorithm to find an approximate solution to the problem. A case study with real-world data from a large European hub airport demonstrates that (i) risk-averse strategy reduces the probability of financial loss from 34% to less than 10% compared to deterministic benchmarks, (ii) counterintuitive vertiport locations in low-density areas can enhance profitability, and (iii) partial exploitation of existing heliport infrastructure suggests a viable path to cost reduction and accelerated implementation. The results provide actionable guidance for UAM operators and policymakers.

Keywords: eVTOL; Vertiport network planning; Two-stage stochastic optimization; Demand uncertainty; Risk-averse planning (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1016/j.tra.2026.104957

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