<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.

Urban Air mobility (UAM) promises reduced congestion on roads, reduced travel times and stronger overall efficiency in densely populated areas. However several challenges arise when wanting to implement UAM such as safety and social acceptance. The aim of this paper is to gain valuable insights how to implement safe and socially accepted UAM into society. Current regulations are discussed as well as X, Y and Z volumes in U-space, flight separations with ellipsoidal safety buffers, high speed corridors, landing separation at vertiports and airspace partition with voronoi diagrams are proposed and discussed. Social acceptance is addressed with some of the most prominent concerns e.g. safety, privacy and noise. Examples are set in Stockholm, Sweden, resulting in a maximum airspace occupation of 1 % which means 210 UAS (Unmanned Aircraft Systems) on each flight level. Sensitive areas and people with privacy concerns should have the option to opt-out of having their properties under the flight paths of UAM-vehicles. Concerns with UAM from the public has to be taken into great consideration for a successful implementation of UAM.

In decentralised multi-agent reinforcement learning communication can be used as a measure to increase coordination among the agents. At the same time, the essence of message exchange and its contribution to successful goal achievement can only be established with the domain knowledge of a given environment. This thesis focuses on understanding the impact of communication on a decentralised multi-agent system. To achieve this, communication is employed and studied in the context of Urban Air Mobility, in particular- to the vertiport terminal area control problem. A proposed in this work experimental framework, that promotes different information exchange protocols, allows to investigate if and how the agents leverage their communication capabilities. Acquired simulation results show that in the terminal area of a vertiport the aircrafts, controlled in a decentralised way, are capable of proper self-organisation, similar to the structured technique formulated in [Bertram and Wei(2020)]. A study of their communication mechanisms indicates that through different protocols the agents learn to signal their intent to enter a vertiport regardless of environment settings.

<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>