This study presents a high-fidelity digital twin (DT) framework designed to evaluate and improve vertiport operations for Advanced Air Mobility (AAM). By integrating Unreal Engine, AirSim, and Cesium, the framework enables real-time simulation of Unmanned Aerial Vehicles (UAVs), including unmanned electric vertical take-off and landing (eVTOL) operations under nominal and disrupted conditions, such as adverse weather and engine failures. The DT supports interactive visualisation and risk-free analysis of decision-making protocols, vertiport layouts, and UAV handling strategies across multi-scenarios. To validate system realism, mixed-reality experiments involving physical UAVs, acting as surrogates for eVTOL platforms, demonstrate consistency between simulations and real-world flight behaviours. These UAV-based tests confirm the applicability of the DT environment to AAM. Intelligent algorithms detect Final Approach and Take-Off (FATO) areas and adjust flight paths for seamless take-off and landing. Live environmental data are incorporated for dynamic risk assessment and operational adjustment. A structured capacity evaluation method is proposed, modelling constraints including turnaround time, infrastructure limits, charging requirements, and emergency delays. Mitigation strategies, such as ultra-fast charging and reconfiguring the layout, are introduced to restore throughput. This DT provides a scalable, drone-integrated, and data-driven foundation for vertiport optimisation and regulatory planning, supporting safe and resilient integration into the AAM ecosystem.

This paper discusses the development of an adaptive Deep Reinforcement Learning solver, designed for centralised onground Tactical Conflict Resolution and applied within diverse in-cruise configurations of urban airspace. Our approach utilises a Multi-agent Reinforcement Learning algorithm with a shared policy framework, employing a Proximal Policy Optimisation baseline. The solver aims to ensure tactical conflict resolution through centralised decision-making and distributed instructions via speed calibration and flight level assignment. Each operation adheres to a designated flight plan and Operational Volume tolerances. Our methodology first enhances the solver's adaptability by interpreting a broad spectrum of in-cruise conflict configurations. This is achieved through a cautious observation and action space design, a normalisation strategy, and a Recurrent Neural Network that generalises conflict resolution for any number of intruders. We also apply existing standards from Airborne Collision Avoidance Systems to evaluate the safety performance of our Machine Learning-based solver, addressing the gap in Machine Learning model evaluation as a conventional system. We explore eight case studies of varying difficulty to investigate the safety impact of specific conflict configurations, from single to multiple intersections, considering shared routes or individual operation volumes, among other factors. Finally, we develop a ninth case study that maps all possible configurations under cruise conditions. We explore different trained models and assess their compatibility with other case studies, demonstrating the solver's adaptability in performing Tactical Conflict Resolution tasks for operations in urban airspace.

This paper presents a modular suite of collaborative Unmanned Aircraft System Traffic Management (UTM) services designed to enhance urban airspace operations. The framework integrates various pre-flight and in-flight services within a semi-autonomous simulation environment that emulates UTM service provision for urban airspace operations. It adopts a holistic approach that fosters intelligent algorithm design, robust serviceability, and strong collaboration among different services, enabling comprehensive simulation and assessment of their automated interactions and overall system performance. Building on the Air Mobility Urban - Large Experimental Demonstrations (AMU-LED) project, this study advances UTM service maturity with over 1,600 simulation flight hours across three traffic density levels within a synthetic urban airspace. The analyses assess the impact of specific UTM services on Key Performance Areas such as safety, path efficiency, and operational workload in a 115 NM${}^{2}$ airspace, accommodating up to 150 operations, including 40 simultaneous operations.