Study on facility location of air taxi skyports using a prescriptive analytics approach

Abstract

The current study identifies infrastructure locations for air taxi operations in New York City (NYC) using a prescriptive analytics approach. Based on the potential air taxi demand estimated by prior studies, 14 unique sites are identified by an unsupervised machine learning technique called clustering large applications (CLARA) in Step-1. However, developing all the suggested stations simultaneously might be challenging for any business, and therefore, an integer programming model is developed in Step-2 to propose opening these centers in multiple phases, considering parameters such as rental cost, daily number of trips, easy access to road facilities, and employee salary, while maximizing the demand satisfaction. Sensitivity analysis is then conducted to identify the impact of various market penetration strategies (such as aggressive, defensive and balanced), inflation in the rental cost and employee salary, and the emergence of latent demand on the phased opening of the facilities. The clustering algorithm results indicate seven air taxi facilities to be established in the Manhattan borough, four in Queens, two in Brooklyn, and one in New Jersey, spread across three phases. Larger vertiports are suggested for airports, such as John F Kennedy and LaGuardia international airports, since they serve approximately 55% of the overall demand. Results also showed that varying percentages of the budgeted rental cost and employee salary have minimal effect on the total number of stations.

Keywords

• Urban air mobility
• Electric vertical takeoff and landing
• Air taxi
• Facility location
• Unsupervised machine learning and integer programming approach
• Prescriptive analytics