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).

The demand for air mobility services will depend on the safety of these operations but also on the transportation time savings in congested urban areas. An adequate air space structure is therefore essential to achieve both objectives. Corridors, the most extended solution proposed nowadays, can meet the safety requirements necessary for air taxi operations, but they are rigid (point-to-point solutions) and would increase delays. As an alternative, this paper presents the airspace structure proposed in the SESAR AMU-LED Project, based on layers to assure both safety and efficiency of air taxi operations. In this proposal, small UAS will fly in the bottom part, called the Very Low Level, whereas air taxis will fly in the upper part. The paper applies a collision risk model to determine the minimum required safety buffer between both layers to assure the necessary safety levels. The results obtained show that a buffer of 10 m between them would meet the required safety levels for air taxi operations.

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.

The expected number of drone operations in the coming decades, together with the fact that most of them will take place in very-low-level airspace, will lead to a density of drone flights much greater than that of conventional manned aviation. In this context, the number of conflicts (i.e., 4D convergence of drone trajectories below the safe separation minima) will be much more frequent than in manned aviation and, therefore, conventional air traffic management methods or even the specific proposed mechanisms for drone traffic management are unlikely to be able to solve them safely. This paper considers a set of simulated drone trajectories in a high-density urban environment to analyze the applicability of machine learning regression and classification techniques to detect conflicts among such trajectory times in advance of their occurrence in order to provide new methods to manage the expected drone traffic density safely and efficiently. This would not be possible with current drone traffic management solutions. The obtained results suggest that the Random Forest, Artificial Neural Networks and Logistic Regression algorithms could detect nearly all near-collisions up to 10 s before they occur, and the first two algorithms could also detect a significant number of near-collisions more than 60 s earlier.