Vertiport Terminal Scheduling and Throughput Analysis for Multiple Surface Directions

TL;DR

This paper presents a MILP-based scheduling framework and throughput analysis for vertiport terminals supporting VTOL operations with multiple surface directions, improving efficiency and capacity in urban air mobility.

Contribution

It introduces a holistic MILP model for vertiport scheduling that accounts for multiple approach directions and derives capacity bounds, advancing UAM infrastructure optimization.

Findings

MILP reduces delays by up to 50%

Throughput levels match theoretical upper bounds

Validated with a case study from existing vertiport topology

Abstract

Vertical Take-Off and Landing (VTOL) vehicles are gaining traction in both the delivery drone market and passenger transportation, driving the development of Urban Air Mobility (UAM) systems. UAM seeks to alleviate road congestion in dense urban areas by leveraging urban airspace. To handle UAM traffic, vertiport terminals (vertiminals) play a critical role in supporting VTOL vehicle operations such as take-offs, landings, taxiing, passenger boarding, refueling or charging, and maintenance. Efficient scheduling algorithms are essential to manage these operations and optimize vertiminal throughput while ensuring safety protocols. Unlike fixed-wing aircraft, which rely on runways for take-off and climbing in fixed directions, VTOL vehicles can utilize multiple surface directions for climbing and approach. This flexibility necessitates specialized scheduling methods. We propose a Mixed Integer Linear Program (MILP) formulation to holistically optimize vertiminal operations, including taxiing, climbing (or approach) using multiple directions, and turnaround at gates. The proposed MILP reduces delays by up to 50%. Additionally, we derive equations to compute upper bounds of the throughput capacity of vertiminals, considering its core elements: the TLOF pad system, taxiway system, and gate system. Our results demonstrate that the MILP achieves throughput levels consistent with the theoretical maximum derived from these equations. We also validate our framework through a case study using a well-established vertiminal topology from the literature. Our MILP can be used to find the optimal configuration of vertiminal. This dual approach, MILP and throughput analysis, allows for comprehensive capacity analysis without requiring simulations while enabling efficient scheduling through the MILP formulation.