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. / Doctor of Philosophy / Urban Air Mobility (UAM) or Advanced Air Mobility (AAM) are concept transportation modes currently in development. It proposes transporting passengers and cargo in urban areas using all-electric Vertical Take-Off and Landing (eVTOL) vehicles. UAM is a multi-modal concept involving low-altitude aerial transport. The high capital costs involved in developing vehicles and infrastructure suggests the need for meticulous planning and strong strategy development in the rolling out of UAM. Moreover, urban travelers are relatively more sensitive to travel time savings and travel time reliability; therefore, the efficiency of UAM is critical for its success. This dissertation comprises multiple studies surrounding demand estimation, feasibility and capacity analysis, and the environmental impact of UAM. To estimate the potential for UAM, we need first to understand the mode-choice making behavior of urban travelers and then estimate the relative utility UAM could possibly offer. The studies presented in this dissertation primarily focus on analyzing urban travelers' current behavior and estimating the potential UAM demand for different trip purposes in multiple U.S. urban areas. The system planners would need to know the individual or combined effect of various parameters in the system, such as cost of UAM, network size of UAM, etc., on UAM potential. Therefore, sensitivity analyses with respect to UAM demand are performed against various framework parameters. Capacity constraints are not initially considered for potential demand estimation. However, like any other transportation mode, UAM could suffer from capacity issues that can cause operational delays. A simulation study is dedicated to model UAM operations at a vertiport and estimating factors affecting vertiport capacity. After observing the demand potential for certain optimistic scenarios, we realized the possibility of a large number of low-flying vehicles, which could cause annoyance and environmental impacts. Therefore, the following study focuses on developing a noise estimation framework from a full-day of UAM operations and estimating a highly annoyed population in the Bay Area and Dallas-Fort Worth Region. In our studies, modeling restricted airspaces (due to commercial operations at large airports) was always a critical part of the analysis. The urban airspaces are already quite congested in some urban areas, and we assumed that UAM would not operate in the restricted airspaces. The last study in this dissertation focuses on quantifying the impact of different levels of airspace restrictions on UAM demand potential in New York. It would help system planners gauge the level of integration required between the UAM and National Airspace System (NAS).

The first part of a study of passenger flight experience of Urban Air Mobility was completed. This first part included the design of different Urban Air Mobility vehicle models, in which the passenger flight experience would be quantitatively measured. A first version of a simulator setup, in which the measurements were performed, was also developed. Three concept vehicle models, a single main rotor, a side-by-side rotor and a quadrotor, were designed in the conceptual design software NDARC. The vehicles were electrically propelled with battery technology based on future technology predictions and were designed for autonomous flight with one passenger. The emissions of the vehicles were analyzed and compared with an existing turboshaft helicopter. The interface between NDARC and the flight dynamics analysis and control system software FlightCODE, which was used to create control systems to the NDARC models,  was developed to fit the vehicle configurations considered. The simulator setup was created with a VR headset, the flight simulation software X-Plane, an external autopilot software and stress sensors. Trial runs with the simulator setup were performed and gave important data for the continued development. Planned upgrades of the simulation station were presented and the continuation of the study was discussed.

<p>As Urban Air Mobility (UAM) is developed and brought into fruition via electric vertical takeoff and landing (eVTOL) vehicles, contingencies associated with this new distributed electric propulsion technology in metropolitan areas must be considered. On the state of knowledge on contingencies for eVTOL vehicles, these can be Epistemological Risks or Ontological Risks. Epistemological Risks include known-knowns (probabilistic risks) and known-unknowns (gaps in knowledge). Ontological Risks include, unknown-knowns (hidden knowledge), unknown-unknowns (fog of ignorance). As UAM operations at large scale do not have as much historical accidents data as General Aviation or Commercial Aviation, it is challenging to estimate its accident failure rate per 100,000 flight hours. While battery thermal runaway, battery energy uncertainty, software issues, and common mode power failures are some failure cases listed in this thesis, it is the undiscovered contingency (i.e., unknown-unknown) or unprepared contingency (i.e., unknown-known), along with other external factors, that can lead to an accident. UAM is expected to operate at 1500 feet AGL and at high frequencies over dense metropolitan areas. In an in-flight emergency at these altitudes, any startle response experienced by on-board or remote pilots can lead to longer response times. This study aims to create a framework for contingency planning and risk mitigation using a Reachable Ground Footprint model for eVTOL aircraft under 100% power failure scenarios in-flight. This framework utilizes all existing, public aerodrome infrastructures in metropolitan areas as potential contingency landing sites. Metrics such as Contingency Landing Assurance Percentage and Cruise Altitude Floor requirement are introduced to quantitatively measuring the safety of any UAM trip and provide recommendations on safe cruising altitudes. A demonstration case in the Chicago Metropolitan Area between DuPage Regional Airport and John H. Stroger Hospital Helipad is shown and discussed. Furthermore, aggregate analysis of 434 UAM trips in Chicago Metropolitan Area between Regional Airports, between Regional and Heliports, and between Heliports is performed, along with sensitivity studies involving wind and turn control restrictions. The results discuss variations in Cruise Altitude Floor, Flight Time, and Energy Consumption of these trips using an eVTOL vehicle.</p>

Rising concerns related to the effects of traffic congestion have led to the search for alternative transportation solutions. Advances in battery technology have resulted in an increase of electric vehicles (EVs), which serve to reduce the impact of many of the negative consequences of congestion, including pollution and the cost of wasted fuel. Furthermore, the energy-efficiency and quiet operation of electric motors have made feasible concepts such as Urban Air Mobility (UAM), in which electric aircraft transport passengers in dense urban areas prone to severe traffic slowdowns. Electrified transportation may be the solution needed to combat urban gridlock, but many logistical questions related to the design and operation of the resultant transportation networks remain to be answered. This research begins by examining the near-term effects of EV charging networks. Stationary plug-in methods have been the traditional approach to recharge electric ground vehicles; however, dynamic charging technologies that can charge vehicles while they are in motion have recently been introduced that have the potential to eliminate the inconvenience of long charging wait times and the high cost of large batteries. Using an agent-based model verified with traffic data, different network designs incorporating these dynamic chargers are evaluated based on the predicted benefit to EV drivers. A genetic optimization is designed to optimally locate the chargers. Heavily-used highways are found to be much more effective than arterial roads as locations for these chargers, even when installation cost is taken into consideration. This work also explores the potential long-term effects of electrified transportation on urban congestion by examining the implementation of a UAM system. Interdependencies between potential electric air vehicle ranges and speeds are explored in conjunction with desired network structure and size in three different regions of the United States. A method is developed to take all these considerations into account, thus allowing for the creation of a network optimized for UAM operations when vehicle or topological constraints are present. Because the optimization problem is NP-hard, five heuristic algorithms are developed to find potential solutions with acceptable computation times, and are found to be within 10% of the optimal value for the test cases explored. The results from this exploration are used in a second agent-based transportation model that analyzes operational parameters associated with UAM networks, such as service strategy and dispatch frequency, in addition to the considerations associated with network design. General trends between the effectiveness of UAM networks and the various factors explored are identified and presented.

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