Mark T. Kotwicz Herniczek
(Advisor: Prof. Brian J. German)

will defend a doctoral thesis entitled,

A Framework for the Analysis of an Urban Air Mobility Commuting Service

On

Friday, April 21st at 10:00 a.m. ET
teleconference
https://teams.microsoft.com/l/meetup-join/19%3ameeting_ZGYzMGJmNDYtOWRiNC00YWRlLTgwNjgtYzAzYjY4MmNmMjE3%40thread.v2/0?context=‌%7b%22Tid%22%3a%22482198bb-ae7b-4b25-8b7a-6d7f32faa083%22%2c%22Oid%22%3a%22de6af0b9-060d-426d-bcfa-8c61b9b77bad%22%7d

Abstract
Urban Air Mobility (UAM), defined by the FAA as air-transportation of passengers or cargo at low altitudes within urban and suburban areas, has rapidly gained industry, researcher, and investor interest, with hundreds of companies working on UAM related technology, thousands of UAM related conference and journal articles published, and the total UAM global market projected to be between $74 and $641 billion (USD) by 2035. There is particular interest and anticipation regarding the usage of electric Vertical Take-Off and Landing (eVTOL) vehicles to provide large-scale, accessible, and affordable intracity air-taxi transportation services.

This work investigates the feasibility of a UAM service for commuting purposes and develops a comprehensive and reproducible framework built upon publicly available data that consists of four key elements: (1) demand estimation, (2) vertiport placement optimization, (3) aircraft scheduling optimization, and (4) vertiport sizing.

The dissertation provides contributions in several different areas:

First, a speed-flow traffic estimation model is presented based on publicly available data that is capable of estimating nationwide historical road traffic congestion by fusing OpenStreetMap data and Highway Performance Monitoring System data. The model facilitates integration of historical congestion data into operations research without the need for simulation.

Second, a scalable, discrete mode-choice demand model for UAM is developed that enables fast demand estimation based on the relative utility of available travel modes. This demand model is capable of national-level demand estimation and enables vertiport placement optimization and scheduling optimization. The model is also applied to identify cities of interest for UAM and to explore sensitivity of demand to parameters such as service cost, service delay, and number of vertiports.

Third, several vertiport placement optimization methods are implemented, including k-means clustering, mixed-integer linear programming, genetic algorithms, and combinatorial methods, with the goal of maximizing demand. Demand is shown to be very sensitive to vertiport placement, highlighting the need for optimal vertiport positioning. The computational requirements and solution quality of each vertiport placement method are compared, providing insights into appropriate optimization methods for vertiport placement problems of different sizes.

Lastly, an aircraft scheduling optimization framework is described that minimizes fleet size and number of deadhead flights for a given level of demand, enabling rapid estimation of fleet-size requirements. A simple vertiport sizing model based on FAA guidance is also provided, that estimates the minimum area required for a vertiport given a certain throughput of vehicles. The scheduling and vertiport sizing model are applied to each case-study city of interest to explore infrastructure requirements and their effect on the scalability of UAM operations. The sensitivities of fleet and vertiport size to vehicle and operational parameters are also investigated.

Collectively, the developed models form a cohesive framework that provide a better understanding of the future potential for commuter UAM services, particularly regarding their scalability and issues that need to be overcome to reach the scale envisioned by current proponents of UAM.

Committee

• Prof. Brian J. German – School of Aerospace Engineering (advisor)
• Prof. Graeme J. Kennedy – School of Aerospace Engineering
• Prof. Karen M. Feigh – School of Aerospace Engineering
• Dr. Tristan A. Hearn – Advanced Air Vehicles Program, NASA Glenn Research Center
• Danielle J. Rinsler – Aviation Policy, Amazon