<p dir="ltr">Urban air mobility (UAM) has the potential to revolutionize the transportation industry, offering fast, convenient, and sustainable travel options for passengers and cargo. The development and operation of UAM networks, however, face significant challenges, including the need for infrastructure investments and the management of grid electricity usage. In this thesis, we present a comprehensive model of UAM network operations based on system-of-systems engineering principles and employ a data-driven simulation framework to analyze the expected performance of a UAM operation. Our approach optimizes the composition of the UAM network, including the number of vehicles, chargers, and sizing of solar microgrids, to minimize total acquisition costs while adhering to operational constraints such as maximum average passenger delay and grid usage for each vertiport. Through the application of our methodology to diverse case studies, we provide valuable insights into the optimal design and integration of on-site microgrids for UAM vertiport networks, highlighting their impact on carbon emissions, operating costs, and grid electricity usage. This research contributes to the development of sustainable and efficient UAM systems, supporting informed decision-making among stakeholders involved in the planning, deployment, and operation of urban air mobility networks.</p>

Background: A continuously growing urban population leads to congested urban areas. As a result, people are wasting time being stuck in traffic. One way of solving this problem is to use the air for moving people. Thus, companies all over the globe are working extensively on approaches for Urban Air Mobility such as air taxis. Purpose: The purpose of this thesis is the identification of key determinants influencing the acceptance of air taxis from a potential user perspective. Thereby, the thesis develops the Urban Air Mobility Acceptance Model (UAMAM) as an extension of the Technology Acceptance Model (TAM). Method: An explanatory online survey was conducted to test the hypotheses in the proposed UAMAM. Data from 321 respondents living in cities larger than one million inhabitants representing the potential target group was collected. Partial Least Squares Structural Equation Modeling (PLS SEM) was used to assess the measurement model in terms of validity and reliability and the structural model in terms of hypotheses testing and strength of relationships between proposed variables. Further, a multigroup analysis has been examined to identify significant differences among groups. Conclusion: The results show that the attitude, which is strongly influenced by the perceived usefulness, as well as subjective norm, travel cost and the personal innovativeness are key determinants affecting the users’ behavioral intention to use air taxis. Further, moderating effects of age on the relation between time saving and behavioral intention as well as on the relation between personal innovativeness and behavioral intention were identified. Additionally, moderating effects of occupational status on the relation between travel cost and behavioral intention were found.

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<div>The National Aeronautics and Space Administration (NASA) has a vision for Advanced Air Mobility (AAM) based on safely introducing aviation services to missions that were previously not served or under-served. Many potential AAM missions lie in metropolitan areas that are beset by various types of uncertainty and potential constraints. Radio interference from other electronic devices can render unreliable communication between flying vehicles to ground operators. Buildings have irregular surfaces that degrade GPS localization performance. Skyscrapers can induce spontaneous turbulence that degrades vehicles' navigational accuracy. However, the potential market demands for aerial passenger-carrying and package delivery services have attracted investments. For example, Google WingX, Amazon Prime Air, and Joby Aviation are well-known companies developing AAM systems and services. If the market visions are realized, how will safety be assessed and maintained with high-density AAM operations?</div><div><br></div><div>While there are multiple technology candidates for realizing high-density AAM operations in urban environments, the means to accomplish the requisite first step of assessing the airspace safety of an integrated AAM eco-system from the candidate technologies is crucial but as yet unclear. This dissertation proposes an entropy-based framework for assessing the airspace safety level for low-altitude airspace in an AAM setting. The framework includes a conceptual model for depicting the information flows between air vehicles and an air traffic authority (ATA) and the use of a probability distribution to represent the traffic state. Subsequently, the framework embeds three airspace-level metrics for assessing airspace safety and uncertainty levels. The traffic safety severity metric quantifies the traffic safety level. The traffic entropy quantifies the uncertainty level of the traffic state distribution. Finally, the temperature is the ratio of the traffic safety severity to the traffic entropy. The temperature is similar to the traffic safety severity but gives a higher weight to the instance with a safe traffic state. </div><div><br></div><div>Simulation studies show that the combined use of the three metrics can evaluate relative airspace safety levels even if the unsafe conditions do not occur. The use cases include using the metrics for real-time airspace safety level monitoring and comparing the design of airspace systems and operational strategies. Additionally, this study demonstrates using a heat map to visualize vehicle-level metrics and assess designs of UAM airspace structures. The contribution of this study includes two parts. First, the temperature metric can heuristically assess a probability function. Based on the definition of the cost function, the temperature metric gives a higher weighting to the instance of the probability function with a lower cost value. This study constructs several triggers for predicting if a near-miss event would happen in the airspace. The temperature-based trigger has a better prediction accuracy than the cost-function-based trigger. Secondly, the temperature can visualize the safety level of an airspace structure with the considerations of the environmental and vehicle state measurement uncertainty. The locations with high-temperature values indicate that the regions are more likely to have endangered vehicles. Although this framework does not provide any means of resolving the unsafe conditions, it can be powerful in the comparison of different airspace design concepts and identify the weaknesses of either airspace design or operational strategies. </div>

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This thesis presents methods to place landing sites for the Urban Air Mobility (UAM) concept. The analysis shows an integrated approach to establish UAM landing site requirements, place landing sites based on predicted demand, and estimate the costs associated with UAM landing sites. This thesis also makes estimates of fares associated with UAM operations. The methods presented are applied to three large urban centers in the United States. The analysis assumes an all-electric, advanced multi-rotor aircraft with autonomous navigational and Vertical Takeoff and Landing (VTOL) capabilities to estimate UAM landing site requirements. The thesis presents the land area requirements of UAM landing sites using Federal Aviation Administration heliport design criteria considering ground-taxi configurations. The analysis performed employs a UAM vehicle with an equivalent Rotor Diameter (RD) of 43 feet. In this thesis, UAM demand is estimated using a mode choice model developed in a companion study (UAM Scenario Analysis). The methodology to locate UAM landing sites includes splitting and consolidation of UAM landing sites considering the Zillow Transaction and Assessment Dataset (ZTRAX) to introduce land-use size and cost constraints. The sites are split using a K-Means clustering method and are consolidated using a simple center of mass approach where the demand of each landing site is analogous to mass. The results presented in this thesis apply to 75 and 200 landing sites in each region and assume passenger Cost-Per-Mile (CPM) of $1.20 and $1.80, respectively. This thesis presents a comparative study on how the availability of land affects the splitting, consolidation, and relocation of UAM landing sites for each region, the number of landing sites, and the cost per passenger-mile. / Master of Science / This thesis aims at the landing sites for the Urban Air Mobility (UAM) concept for commuting passengers in Northern California (17 counties), Southern California (9 counties), and Dallas-Fort Worth (12 counties) region. The aircraft for this service is designed to be an all-electric advanced multi-rotor aircraft with autonomous navigational and Vertical Takeoff and Landing (VTOL) capabilities. The commuting trips considered is focused on passengers traveling to work from home and back. This thesis presents the land area requirements of these landing sites, which are calculated from the Federal Aviation Administration's (FAA) Advisory Circular 150/5390-2C using ground-taxi configuration for a typical representative aircraft of an equivalent rotor diameter (RD) of 43 feet. The landing sites are then split into smaller sites and consolidated into larger sites. This thesis also presents a list of plots of land located within the 0.5 statute-mile boundaries of the landing sites for relocation. This entire analysis is based on the availability of land from the Zillow Transaction and Assessment Dataset (ZTRAX). The results presented in this thesis are for 75 and 200 landing sites set in the study area for a passenger Cost-Per-Mile (CPM) of $1.2 and $1.8, respectively. The results show how the availability of land changes for different CPM for a set of landing sites and affects the splitting, consolidation, and relocation of landing sites for each region. In the end, the thesis presents conclusions and recommendations unique to each region.

<p>The research effort addressed important gaps in the modeling to simulate Urban Air Mobility (UAM) operations and couple optimization analyses for vehicle design, fleet allocations, and operational choices for next generation urban travel. Urban Air Mobility is expected to be a \$1 trillion dollar industry by 2040, but operators and designers have limited models and tools to estimate fleet performance, cost metrics, emissions performance, and profit for a given concept under future concepts of operations. A review of the literature reveals 14 modeling gaps related to infrastructure, operations, airspace, vehicles, and customers. In addition, the UAM industry requires better understanding of how operational choices may impact vehicle design and fleet allocations in a market with significant economic barriers and infrastructure needs. To address those needs, this effort proposed alternatives to address modeling challenges and develop studies to evaluate UAM vehicle concepts and concepts of operations in ways once not possible using the enhanced modeling tools. The research findings revealed that modeling coupled design/fleet and operational choices can affect daily profitability potential by 2-4\times\, for piloted and autonomous operations and affect the fleet size from between 12-50 vehicles across small, medium, and large metropolitan areas. The modeling capability provided by the improvements in UAM operations simulations and accessing vehicle and fleet metrics enables future studies to address UAM in a holistic manner. The increased capability could benefit the UAM community and inform future operations and concepts of operations in preparation for ubiquitous operations.</p>

<p>Unmanned aerial systems (UASs) are at the intersection of robotics and aerospace re-<br> search. Their rise in popularity spurred the growth of interest in urban air mobility (UAM)<br> across the world. UAM promises the next generation of transportation and logistics to be<br> handled by UASs that operate closer to where people live and work. Therefore safety and<br> security of UASs are paramount for UAM operations. Monitoring UAS traffic is especially<br> challenging in urban canyon environments where traditional radar systems used for air traffic<br> control (ATC) are limited by their line of sight (LOS).<br> This thesis explores the design and preliminary results of a target tracking system for<br> urban canyon environments based on a network of camera nodes. A network of stationary<br> camera nodes can be deployed on a large scale to overcome the LOS issue in radar systems<br> as well as cover considerable urban airspace. A camera node consists of a camera sensor, a<br> beacon, a real-time kinematic (RTK) global navigation satellite system (GNSS) receiver, and<br> an edge computing device. By leveraging high-precision RTK GNSS receivers and beacons,<br> an automatic calibration process of the proposed system is devised to simplify the time-<br> consuming and tedious calibration of a traditional camera network present in motion capture<br> (MoCap) systems. Through edge computing devices, the tracking system combines machine<br> learning techniques and motion detection as hybrid measurement modes for potential targets.<br> Then particle filters are used to estimate target tracks in real-time within the airspace from<br> measurements obtained by the camera nodes. Simulation in a 40m×40m×15m tracking<br> volume shows an estimation error within 0.5m when tracking multiple targets. Moreover,<br> a scaled down physical test with off-the-shelf camera hardware is able to achieve tracking<br> error within 0.3m on a micro-UAS in real time.</p>

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.