[Paper Review] Air Taxi Skyport Location Problem for Airport Access

Srushti Rath, Joseph Y.J. Chow|arXiv (Cornell University)|Apr 1, 2019

Aviation Industry Analysis and TrendsEconomics, Econometrics and Finance40 references20 citations

TL;DR

This paper formulates the air taxi skyport location problem as a choice-constrained, elastic-demand variant of the single-allocation p-hub median problem, integrating traveler mode choice behavior based on trip cost and time. Using New York City’s 149 taxi zones and 20M+ for-hire trips to airports, it identifies that at least 9 strategically placed skyports in Manhattan, Queens, and Brooklyn can effectively serve airport access demand under varying pricing and transfer time scenarios, with revenue-maximizing models showing better demand distribution and performance.

ABSTRACT

Witnessing the rapid progress and accelerated commercialization made in recent years for the introduction of air taxi services in near future across metropolitan cities, our research focuses on one of the most important consideration for such services, i.e., infrastructure planning (also known as skyports). We consider design of skyport locations for air taxis accessing airports, where we present the skyport location problem as a modified single-allocation p-hub median location problem integrating choice-constrained user mode choice behavior into the decision process. Our approach focuses on two alternative objectives i.e., maximizing air taxi ridership and maximizing air taxi revenue. The proposed models in the study incorporate trade-offs between trip length and trip cost based on mode choice behavior of travelers to determine optimal choices of skyports in an urban city. We examine the sensitivity of skyport locations based on two objectives, three air taxi pricing strategies, and varying transfer times at skyports. A case study of New York City is conducted considering a network of 149 taxi zones and 3 airports with over 20 million for-hire-vehicles trip data to the airports to discuss insights around the choice of skyport locations in the city, and demand allocation to different skyports under various parameter settings. Results suggest that a minimum of 9 skyports located between Manhattan, Queens and Brooklyn can adequately accommodate the airport access travel needs and are sufficiently stable against transfer time increases. Findings from this study can help air taxi providers strategize infrastructure design options and investment decisions based on skyport location choices.

Motivation & Objective

• To address the critical infrastructure challenge of skyport placement for urban air mobility (UAM) services, particularly for airport access.
• To model traveler mode choice behavior—balancing trip cost and travel time—using a binary logit model to reflect real-world preferences.
• To optimize skyport locations under two objectives: maximizing ridership (RDR) and maximizing revenue (REV).
• To evaluate sensitivity of skyport locations and demand allocation under varying pricing strategies and transfer time assumptions.
• To provide actionable insights for air taxi providers and planners on infrastructure investment and design based on demand behavior and operational trade-offs.

Proposed method

• Formulates the skyport location problem as a modified single-allocation p-hub median model with elastic demand and choice constraints.
• Incorporates a binary mode choice logit model to represent traveler decisions between ground and air taxi modes based on cost and time trade-offs.
• Uses a linearized mixed-integer programming (MIP) formulation solvable via commercial solvers like Gurobi for NYC case study.
• Integrates real-world data from NYC TLC, including 20+ million for-hire vehicle trips to JFK, LGA, and EWR airports.
• Tests three pricing scenarios (short-, medium-, and long-term) and varies transfer time at skyports to assess robustness of solutions.
• Applies sensitivity analysis on the number of skyports (p) to determine minimum threshold for stable performance.

Experimental results

Research questions

• RQ1 What is the optimal number and location of skyports in a major city like New York to efficiently serve airport access demand under elastic, choice-constrained demand?
• RQ2 How do different pricing strategies (short-, medium-, long-term) affect skyport location and demand allocation under ridership and revenue objectives?
• RQ3 How does increasing transfer time at skyports impact the stability and performance of the optimal skyport configuration?
• RQ4 How do the RDR (ridership-maximizing) and REV (revenue-maximizing) models compare in terms of demand distribution and system performance?
• RQ5 To what extent is the optimal skyport configuration robust to changes in transfer time and pricing assumptions?

Key findings

• At least 9 skyports are required to ensure stable and effective coverage of airport access demand in New York City, even as transfer times increase.
• The REV model (revenue-maximizing) produces a fairer distribution of demand across skyports compared to the RDR model (ridership-maximizing), which tends to concentrate demand at fewer hubs.
• The REV model demonstrates superior performance from a revenue perspective, indicating that profit-oriented planning may lead to more balanced and scalable operations.
• Skyport locations are stable under increasing transfer times, suggesting that the optimal configuration remains viable even with operational delays.
• The case study confirms that the proposed model can be effectively applied to real-world urban networks using available trip data and commercial solvers.
• The method is generalizable to other cities and can be extended to include additional use cases such as medical facilities, sports venues, and major transit hubs.