With advancements in aircraft technology, battery, and autonomy, the utilization of aircraft services for various urban applications has garnered significant attention. This dissertation investigates the methodologies, feasibility, and advantages of UAV services in two key areas: urban transportation and infrastructure systems. In the context of urban transportation, the potential benefits of Urban Air Mobility (UAM) services are explored as a solution to reduce user's travel time and alleviate congested ground transportation systems. By employing electric vertical take-off and landing (eVTOL) aircraft to transport passengers through the air, UAM can reduce ground-level congestion and vehicle miles traveled (VMT). A comprehensive framework is developed to evaluate the impact of UAM on VMT and travel time, encompassing modules for vertiport design, passenger assignment, vehicle movement simulation, and UAM allocation. A case study conducted in the San Francisco Bay Area demonstrates the potential of multi-modal UAM systems in not only decreasing UAM users' travel time but also improving ground-level average travel speeds, reducing total VMT, and reducing travel times for specific ground users. However, it should be noted that the establishment of vertiport infrastructure with a large number of UAM passengers can influence congestion on local roads and entail significant investment. Vertiports for UAM also outperform the SF BAY area public transit mode, BART, in land use efficiency. UAM service and vertiports offer significantly higher passenger density and passenger miles traveled density, mainly due to their landless airlinks.

Apart from transporting passengers, aircraft at lower altitudes, e.g., small UAVs, can also play a crucial role in digital asset management by inspecting and monitoring civil infrastructures. Aging or disasters such as earthquakes and hurricanes can impact the functionality of structures like bridges, buildings, and tunnels. Leveraging autonomous UAV and Machine Learning (ML) technology, a 3D damage detection, localization, and quantification framework is proposed using UAV-collected images. An ML-based structural damage detector is trained with hundreds of images, and the 3D reconstruction technique, Structure from Motion (SfM), is utilized to convert 2D images into a 3D model. By integrating 2D damage detection with the 3D model using a pinhole camera model, 3D damage localization and quantification are achieved. The feasibility of this framework is validated in a real-world test bed, demonstrating the potential of UAV-collected data for structural health inspection.

Despite advances in post-processing UAV-collected data for damage detection, realizing an autonomous data collection process remains challenging, particularly in scenarios where infrastructure has been affected by disasters and undergoes shape changes. To address this challenge, an autonomous inspection platform is developed, empowering UAVs to explore environments with limited prior information autonomously. The platform employs a nested control loop, comprising a global planner responsible for actively directing the UAV to collect information and a local planner dedicated to collision avoidance. Leveraging a Simultaneous Localization and Mapping (SLAM) algorithm that reconstructs the environment map, the global planner can make decisions and navigate the UAV to scan unknown regions. The local planner, which runs with a relatively higher frequency, takes the latest measurement and computes exact waypoints to avoid collision. Beginning without a prior environment map, the UAV dynamically scans the environment, navigates collision-free, and stops when covering the observable space completely. Validation in a Regional Scale Autonomous Swarm Damage Assessment (RSASDA) simulator built by us effectively demonstrates the platform's capabilities in autonomous infrastructure inspection.

Overall, this dissertation delves into the realm of UAV services and their transformative impact on urban transportation and infrastructure systems. By uncovering the potential of UAM in reducing VMT and showcasing the effectiveness of UAVs in structural health inspection, it sheds light on the wide-ranging benefits that aircraft can bring to metropolitan regions. Moreover, this research offers frameworks and platforms that pave the way for the practical implementation of these services, tackling critical challenges such as regional-scale multi-modal UAM simulation, congestion analysis, facility location, structural damage detection, and autonomous UAV navigation. As we continue to explore the possibilities, it becomes evident that aircraft have the potential to revolutionize urban areas, opening up new horizons for applications beyond transportation and infrastructure, such as emergency response, environmental monitoring, and delivery services. The future of urban areas holds immense promise as the integration of aircraft becomes more prevalent, ushering in a new era of efficiency, connectivity, and sustainability.