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.

The UAS (Unmanned Aircraft System) Traffic Management (UTM) System Concept of Operations (ConOps) is the first formal design reference document of the UTM system, ConOps aims to bring Class G Airspace into government regulation. However, it should be noted that there are still some shortcomings in ConOps that require further discussion. For example, there are concerns about operational rights, privacy rights, and the potential interference caused by high-rise buildings in urban core areas. The Lane-based UTM systems could potentially help in solving the above issues. The flight paths of Unmanned Aerial Vehicles (UAVs) in urban areas or other areas will interact with the road network, which can facilitate airspace traffic development. Ground traffic flow simulation is generally conducted on three levels: macroscopic, mesoscopic, and microscopic. Some of the commonly used car traffic flow simulation tools include Vissim, SUMO, and MATSim. However, UAV traffic simulation is mostly at a single level, and all of the current mainstream simulation software for UAV, such as Gazebo, AirSim, and Flight Gear, are microscopic-level analyses of UAV operations, lacking uniform management of drone traffic flow and operations. In addition, these UAV traffic simulation studies do not consider the city traffic and road network. In this context, a lane-based cosimulation UAV traffic simulation method is proposed in this study. The co-simulation architecture will be based on the highfidelity three-dimensional (3D) environment developed in the Unreal Engine, UAV simulation with AirSim, and twodimensional (2D) road network simulation with SUMO. A standardized and universal co-simulation architecture and communication interface to ensure interoperability, compatibility, and synchronization will be developed in this study. The lane-based co-simulation method will effectively leverage the road network simulation capacities to turn complex 3D space planning into simple 2D planning, it could reduce computational load and improve system efficiency. The 3D environment will also enhance the simulation capacities with its unique and high-fidelity simulation capacities. Overall, the proposed co-simulation method will support the Digital Twin development by interfacing several simulation tools, incorporating different communications, and adding realistic visualization, which could create unprecedented opportunities for software tool combinations.

Accurate prediction of drone motion within structured urban air corridors is essential for ensuring safe and efficient operations in Urban Air Mobility (UAM) systems. Although real-world Remote Identification (Remote ID) regulations require drones to broadcast critical flight information such as velocity, access to large-scale, high-quality broadcast data remains limited. To address this, this study leverages a Digital Twin (DT) framework to augment Remote ID spatio-temporal broadcasts, emulating the sensing environment of dense urban airspace. Using Remote ID data, we propose BiDGCNLLM, a hybrid prediction framework that integrates a Bidirectional Graph Convolutional Network (BiGCN) with Dynamic Edge Weighting and a reprogrammed Large Language Model (LLM, Qwen2.5–0.5B) to capture spatial dependencies and temporal patterns in drone speed trajectories. The model forecasts near-future speed variations in surrounding drones, supporting proactive conflict avoidance in constrained air corridors. Results from the AirSUMO co-simulation platform and a DT replica of the Cranfield University campus show that BiDGCNLLM outperforms state-of-the-art time series models in short-term velocity prediction. Compared to Transformer-LSTM, BiDGCNLLM marginally improves the $R^2$ by 11.59%. This study introduces the integration of LLMs into dynamic graph-based drone prediction. It shows the potential of Remote ID broadcasts to enable scalable, real-time airspace safety solutions in UAM.