Maximum capture problem for urban air mobility network design

Urban air mobility (UAM) technology has the potential to revolutionize daily commutes. By leveraging low-altitude airspace, electric vertical take-off and landing (eVTOL) vehicles can provide faster urban transportation between skyports than ordinary surface transport modes. This paper develops a multiple allocation incomplete p-hub location problem for determining the optimal UAM network design under the flying range constraint of eVTOL vehicles. The traditional methods used mathematical programming (MP) to analyse either 1) a deterministic allocation with a flight range limitation or 2) a stochastic allocation using a choice model without a range constraint. We combine the two methods by adopting a recently proposed eUnit choice model in a mixed integer linear programming (MILP) formulation to consider the interaction between travel choice behavior and travel cost incurred by skyport locations and their linkages in the random utility maximization framework. Three schemes of the multiple allocation incomplete p-hub location problem are provided, including revenue maximization, profit maximization, and profit maximization with pricing strategy. Numerical examples are provided to investigate the influence of flight range constraint and the eUnit bound on the solutions, which show significant influence on the UAM network topology and demand allocation.