Urban Air Mobility: Demand Estimation and Feasibility Analysis

This dissertation comprises multiple studies surrounding demand estimation, feasibility and capacity analysis, and environmental impact of the Urban Air Mobility (UAM) or Advanced Air Mobility (AAM). UAM is a concept aerial transportation mode designed for intracity transport of passengers and cargo utilizing autonomous (or piloted) electric vehicles capable of Vertical Take-Off and Landing (VTOL) from dense and congested areas. While the industry is preparing to introduce this revolutionary mode in urban areas, realizing the scope and understanding the factors affecting the attractiveness of this mode is essential. The success of UAM depends on its operational efficiency and the relative utility it offers to current travelers. The studies presented in this dissertation primarily focus on analyzing urban travelers' current behavior using revealed preference data and estimating the potential UAM demand for different trip purposes in multiple U.S. urban areas. Chapter II presents a methodology to estimate commuter demand for UAM operations in the Northern California region. A mode-choice model is calibrated from the commuter mode-choice behavior observed in the survey data. An integrated demand estimation framework is developed utilizing the calibrated mode-choice model to estimate UAM demand and place vertiports. The feasibility of commuter UAM operations in Northern California is further analyzed through a series of sensitivity analyses. This study was published in Transportation Research Part A: Policy and Practice journal. In an effort to analyze the feasibility of UAM operations in different use cases, demand estimation frameworks are developed to estimate UAM demand in the airport access trips segment. Chapter III and Chapter IV focus on developing the UAM Concept of Operations (ConOps) and demand estimation methodology for airport access trips to Dallas-Fort Worth International Airport (DFW)/Dallas Love Field Airport (DAL) and Los Angeles International Airport (LAX), respectively. Both studies utilize the latest available originating passenger survey data to understand arriving passengers' mode-choice behavior at the airport. Mode-choice conditional logit models are calibrated from the survey data, further used to estimate UAM demand. The former study is published in the AIAA Aviation 2021 Conference proceeding, and the latter is published in ICNS 2021 Conference proceedings. UAM vertiport capacity may be a barrier to the scalability of UAM operations. A heavy concentration of UAM demand is observed in specific areas such as Central Business Districts (CBD) during the spatial analysis of estimated UAM demand. However, vertiport size could be limited due to land availability and high infrastructure costs in CBDs. Therefore, operational efficiency is critical for capturing maximum UAM demand with limited vertiport size. The study included in Chapter V focuses on analyzing factors impacting vertiport capacity. A discrete-event simulation model is developed to simulate a full day of commuter operations at the San Francisco Financial District's busiest vertiport. Besides calculating the capacity of different fundamental vertiport designs, sensitivity analyses are carried to understand the impact of several assumptions such as service time at landing pads, service time at parking stall, charging rate, etc. The study explores the importance of pre-positioning UAM vehicles during the time of imbalance between arrival and departure requests. This study is published in ICNS 2021 Conference proceedings. Community annoyance from aviation noise has often been a reason for limiting commercial operations at several major airports globally. Busy airports are located in urban areas with high population densities where noise levels in nearby communities could govern capacity constraints. Commercial aviation noise is only a concern during landing and take-offs. Hence, the impact is limited to communities close to the airport. However, UAM vehicles would be operated at much lower altitudes and have more frequent taking-off and landing operations. Since the UAM operations would mostly be over dense urban spaces, the noise potential is significantly high. Chapter VI includes a study on preliminary estimation of noise levels from commuter UAM operations in Northern California and the Dallas-Fort Worth region. This study is published in the AIAA Aviation 2021 Conference proceedings. The final chapter in this dissertation explores the impact of airspace restrictions on UAM demand potential in New York City. Integration of UAM operations in the current National Airspace System (NAS) has been recognized as critical in developing the UAM ecosystem. Several pieces of urban airspace are currently controlled by Air Traffic Control (ATC), where commercial operation density is high. Even though the initial operations are expected to be controlled by the current ATC, the extent to which UAM operations would be allowed in the controlled spaces is still unclear. As the UAM system matures and the ecosystem evolves, integrating UAM traffic with other airspace management might relax certain airspace restrictions. Relaxation of airspace restrictions could increase the attractiveness of UAM due to a decrease in travel time/cost and relatively more optimal placement of vertiports. Quantifying the impact of different levels of airspace restrictions requires an integrated framework that can capture utility changes for UAM under different operational ConOps. This analysis uses a calibrated mode-choice model, restriction-sensitive vertiport placement methodology, and demand estimation process. This study has been submitted for ICNS 2022 Conference.