This study presents an approach to incorporate optimisation in the strategic conflict resolution service for unmanned aircraft systems (UAS) traffic management. A conventional approach in line with the First-Come, First-Served (FCFS) principle is introduced, following the generation of two types of flight plans (i.e., linear and area operations) with uncertainty buffers further taken into account. This approach is based on iteratively checking the availability of the shared airspace volumes. Next, an optimisation model is formulated, using the same common airspace representation, aiming at minimising the overall delay and deviation to the equivalent FCFS solution (i.e., fairness concern), subject to operational constraints including a time-based separation minima. Some potential implementations are also envisioned for the optimisation model under plausible operational scenarios. Finally, simulation experiments are performed where five case studies are designed, including FCFS and optimisation, as well as their hybrid and batch uses depending on the flight plan submission time. Sensitivity analysis is conducted to assess the impact of some specific model assumptions. Results suggest that, compared to FCFS, a notable delay reduction can be achieved with optimisation incorporated, which is due to the FCFS prioritisation scheme that is often not efficient.

This article presents an integrated approach for on-demand dynamic capacity management (DCM) service to be offered in U-space. The approach involves three main threads, including flight planning (demand), airspace configuration (capacity), and demand-capacity balancing (DCB). The flight planning thread produces unmanned aerial systems (UAS) trajectories for each flight that together reflect the estimated traffic demand. The airspace configuration thread defines the fundamental airspace structure and proposes dynamic adjustment schemes that determine the capacity distribution. It also enables the flight planning to reschedule alternative trajectory options to route away from possible congested areas. The last DCB thread takes the previous inputs and then computes for the optimal slot allocation and trajectory selection, as well as the optimal airspace configuration. Simulation case studies have been performed through mimicking an envisioned U-space operating scenario. Results suggest that the integrated approach can achieve the best outcome in almost all the key performance areas than any other cases, where only partial functions are realized.

The emerging field of Advanced Air Mobility (AAM) holds great promise for revolutionizing transportation by enabling the efficient, safe, and sustainable movement of people and goods in urban and regional environments. AAM encompasses a wide range of electric vertical take-off and landing (eVTOL) aircraft and infrastructure that support their operations. In this work, we first present a new airspace structure by considering different layers for standard-performing vehicles (SPVs) and high-performing vehicles (HPVs), new AAM services for accommodating such a structure, and a holistic contingency management concept for a safe and efficient traffic environment. We then identify the requirements and development process of a testing and simulation infrastructure for AAM demonstrations, which specifically aim to explore the decentralized architecture of the proposed concept and its use cases. To demonstrate the full capability of AAM, we develop an infrastructure that includes advanced U-space services, real and simulated platforms that are suitable for future AAM use cases such as air cargo delivery and air taxi operations, and a co-simulation environment that allows all of the AAM elements to interact with each other in harmony. The considered infrastructure is envisioned to be used in AAM integration-related efforts, especially those focusing on U-space service deployment over a complex traffic environment and those analyzing the interaction between the operator, the U-space service provider (USSP), and the air traffic controller (ATC).

Advanced Air Mobility (AAM) is a concept that is expected to transform the current air transportation system and provide more flexibility, agility, and accessibility by extending the operations to urban environments. This study focuses on flight test, integration, and analysis considerations for the feasibility of the future AAM concept and showcases the outputs of the Air Mobility Urban-Large Experimental Demonstration (AMU-LED) project demonstrations at Cranfield University. The purpose of the Cranfield demonstrations is to explore the integrated decentralized architecture of the AAM concept with layered airspace structure through various use cases within a co-simulation environment consisting of real and simulated standard-performing vehicle (SPV) and high-performing vehicle (HPV) flights, manned, and general aviation flights. Throughout the real and simulated flights, advanced U-space services are demonstrated and contingency management activities, including emergency operations and landing, are tested within the developed co-simulation environment. Moreover, flight tests are verified and validated through key performance indicator analysis, along with a social acceptance study. Future recommendations on relevant industrial and regulative activities are provided.

This paper builds upon previous work in Dynamic Capacity Management (DCM) by extending the implementation from a centralised to a decentralised approach, focusing on coordination among multiple Urban Air Mobility actors. The original model incorporates a set of initiatives such as slot allocation, rerouting, and airspace re-configuration, which can be customised to different model variants. In this study, a decomposed optimisation model is formulated using the column generation method such that the original model can be broken down into numerous (flight- and airspace-specific) subproblems, along with a master problem dealing with the coupling (capacity) constraints. The solution algorithm is then designed to pursue the global optimum. A coordination scheme from an operational perspective is devised to align the decentralised architecture, decomposed model, and solution algorithm. Numerical experiments are conducted, and the results indicate that, regardless of the model variant used, the decentralised approach produces a solution close to that of the centralised model, with both outperforming the semi-centralised approach. This study demonstrates the conceptual feasibility of implementing a DCM service within a decentralised architecture, achieving a near-global optimum similar to what is typically expected from a centralised implementation.