Ph.D. Proposal: Mark T. Kotwicz Herniczek

Wed Jul 13 2022 10:00 AM
Virtual
"An Analysis of Urban Air Mobility as a Commuting Service"

Ph.D. Proposal

Mark T. Kotwicz Herniczek

(Advisor: Prof. Brian German)
will propose a doctoral thesis entitled

"An Analysis of Urban Air Mobility as a Commuting Service"

Wednesday, July 13
10:00 a.m.  EDT
teleconference

https://teams.microsoft.com/l/meetup-join/19%3ameeting_YmFmM2ZjOWEtY2RjMi00OWE4LTgxYWYtMzNjNjRlMjhlMmM1%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 TakeOff 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 aims to develop a comprehensive and reproducible framework built upon publicly available data and consists of four key elements: (1) demand estimation, (2) vertiport placement optimization, (3) aircraft scheduling optimization and vertiport sizing, and (4) airspace corridor structure design.

Several areas of contributions are anticipated from this work:

First, a surrogate traffic estimation model is presented that is capable of estimating nationwide historical traffic congestion by leveraging open street map data to minimize the number of traffic-aware API calls. The model facilitates integration of historical and live traffic data into operations research.

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, capable of national-level demand estimation and enabling vertiport placement optimization and scheduling optimization. The model will be applied to identify cities of interest for UAM and explore sensitivity of demand to parameters such as service cost, number of vertiports, and cost of alternative modes of transportation.

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 will be compared, providing insights into appropriate optimization methods for vertiport placement problems of different sizes.

Next, an aircraft scheduling optimization framework is described that minimizes number of aircraft and number of deadhead flights for a given demand timeline, 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 vertiport sizing model will be applied to each case-study city-of-interest to explore infrastructure requirements and their effect on the scalability of UAM operations.

Lastly, an airspace structure optimization framework is developed that generates minimum-length corridors that adhere to a set of airspace metrics such as maximum vehicle density, airspace complexity, and network complexity. Potential airspace corridor structures will be examined for each case-study city and the impact of corridor structure on eVTOL trajectory distance and demand of a UAM commuter service will be explored.

Collectively, the developed models form a cohesive framework that gives us a better understanding of the future for commuter UAM services, particularly regarding their potential scalability and the issues that will 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

Location

Virtual