A clustering-metaheuristic-simulation approach to determine air taxi operating site location

Urban Air Mobility (UAM) can improve the transportation service offered in urban areas by potentially solving traffic congestion, thereby allowing customers to travel more efficiently across any city. This research primarily focuses on determining optimal locations for establishing electric vertical take-off and landing (eVTOL) air taxi infrastructure sites in urban cities using a three-phased approach. In Phase-1, Clustering Large Applications (CLARA) is developed to determine the potential set of air taxi infrastructure facilities. Next, an integrated metaheuristic-simulation approach is developed, in which the Genetic Algorithm (GA) model determines the sites to be opened, and based on this information, a simulation model (phase-3) is used to determine the routing-specific performance measures. We specifically consider New York City (NYC) as a case study and test the proposed approach using millions of estimated air taxi demands from prior studies. The results indicate that the number of operating stations required for efficient air taxi services is five (Central Park, JFK International Airport, lower Manhattan, Columbia University and Bronx), with an average customer time in system being about 32 min and a waiting time of 13 min per customer.