The Influence of Traffic Structure on Airspace Capacity

Best paper award for the Network Management trackInternational audienceAirspace structure can be used as a procedural mechanism for a priori separation and organization of en-route air traffic. Although many studies have explored novel structuring methods to increase en-route airspace capacity, the relationship between the level of structuring of traffic and airspace capacity is not well established. To better understand the influence of traffic structure on airspace capacity, in this research, four airspace concepts, representing discrete points along the dimension of structure, were compared using large-scale simulation experiments. By subjecting the concepts to multiple traffic demand scenarios, the structure-capacity relationship was inferred from the effect of traffic demand variations on safety, efficiency and stability metrics. These simulations were performed within the context of a future personal aerial transportation system, and considered both nominal and non-nominal conditions. Simulation results suggest that the structuring of traffic must take into account the expected traffic demand pattern to be beneficial in terms of capacity. Furthermore, for the heterogeneous, or uniformly distributed, traffic demand patterns considered in this work, a decentralized layered airspace concept, in which each altitude band limited horizontal travel to within a predefined heading range, led to the best balance of all the metrics considered

The Efficacy of Operational Bird Strike Prevention

Involving air traffic controllers and pilots into the bird strike prevention process is considered an essential step to increase aviation and avian safety. Prior to implementing operational measures such as real-time warning systems, it is vital to evaluate their feasibility. This paper studies the efficacy of a bird strike advisory system for air traffic control. In addition to the potential safety benefit, the possible impact on airport operations is analyzed. To this end, a previously developed collision avoidance algorithm underlying the system was tested in fast-time Monte Carlo simulations involving various air traffic and bird densities to obtain representative conclusions for different operational conditions. The results demonstrate the strong safety potential of operational bird strike prevention in case of precise bird movement prediction. Unless airports operate close to their capacity limits while bird abundance is high, the induced delays remain tolerable. Prioritization of hazardous strikes involving large individuals as well as flocks of birds are expected to support operational feasibility in all conditions

Analysis of Risk-Based Operational Bird Strike Prevention

Bird strike prevention in civil aviation has traditionally focused on the airport perimeter. Since the risk of especially damaging bird strikes outside the airport boundaries is rising, this paper investigates the safety potential of operational bird strike prevention involving pilots and controllers. In such a concept, controllers would be equipped with a bird strike advisory system, allowing them to delay departures which are most vulnerable to the consequences of bird strikes in case of high bird strike risk. An initial study has shown the strong potential of the concept to prevent bird strikes in case of perfect bird movement prediction. This paper takes the research to the next level by taking into account the limited predictability of bird tracks. As such, the collision avoidance algorithm is extended to a bird strike risk algorithm. The risk of bird strikes is calculated for birds expected to cross the extended runway center line and to cause aircraft damage upon impact. By specifically targeting these birds and excluding birds lingering on the runway which are taken care of by the local wildlife control, capacity reductions should be limited, and the implementation remain feasible. The extrapolation of bird tracks is performed by simple linear regression based on the bird positions known at the intended take-off times. To calculate the probability of collision, uncertainties resulting from variability in bird velocity and track are included. The study demonstrates the necessity to limit alerts to potentially damaging strikes with birds crossing the extended runway center line to keep the imposed delays tolerable for airports operating at their capacity limits. It is shown that predicting bird movements based on simple linear regression without considering individual bird behavior is insufficient to achieve a safety-effect. Hence, in-depth studies of multi-year bird data to develop bird behavior models and reliable predictions are recommended for future research. This is expected to facilitate the implementation of a bird strike advisory system satisfying both safety and capacity aspects

Standards for UAS - Acceptable Means of Compliance for Low Risk SORA Operations

Unmanned aircraft systems (UAS) have been in civil use since several years. A new risk-based approach was developed by the Joint Authorities for Rulemaking of Unmanned Systems (JARUS) which relies on the so-called Specific Operations Risk Assessment (SORA) for the medium risk category. Following this process, operational authorization is based on the assessment of the safety of the operation and not solely on the safety of the aircraft design. To comply with the resulting mitigations it is necessary to convince authorities using Acceptable Means of Compliance (AMC). The goal of the European research project AW-Drones is to identify and assess existing standards as a possible AMC for the existing and upcoming regulations. The research in AW-Drones is performed by an international consortium of both, industry and research agencies. Additional stakeholders support the project, i.e. EASA and also other groups of experts, committees and Standard Development Organisations (SDOs). In this paper, the approach and methodology of the project is described, including the current state of work. The results of the data collection step and the assessment will be discussed. The used criteria will be shown and the proposed impact on the SORA process discussed. An Outlook will detail on remaining tasks and the dissemination of the results in a public database, nicknamed the metastandard

Standards für UAV - Nachweismöglichkeiten für die Umsetzung des SORA-Prozesses im Bereich niedriger Risikoklassen

Der Betrieb unbemannter Fluggeräte (UAS) erfolgte lange Zeit außerhalb der geltenden Regulierungen, wie sie aus der bemannten Luftfahrt seit etlichen Jahren bekannt und europäisch harmonisiert sind. Durch die kommerziell getriebene Entwicklung von UAS, die auch immer mehr Einsatzmöglichkeiten eröffnen, ist der Regulierungsprozess auch hier stärker in den Fokus gerückt. Mit den EU Verordnungen 2019/947 und 2019/945 der Europäischen Kommission ist der von den Joint Authorities for Rulemaking of Unmanned Systems (JARUS) entwickelte Prozess für das Specific Operations Risk Assessment (SORA) als risikobasierter Ansatz zur Beurteilung der Sicherheit des UAS als Leitfaden verankert. Aus diesem Prozess werden missionsabhängige Maßnahmen in Form von Operational Safety Objectives abgeleitet, mit denen die Sicherheit systemseitig nachgewiesen wird. Zusätzlich werden Maßnahmen zur Vermeidung von Risiken im Betrieb (Mitigations) einbezogen. Das Ergebnis des Prozesses sind qualitative Anforderungen an die Mission und das Luftfahrtgerät. Zur Umsetzung der aufgestellten Anforderungen sind, ähnlich dem Vorgehen in der bemannten Luftfahrt, anerkannte Nachweismöglichkeiten, sogenannte Acceptable Means of Compliance (AMC) nötig. Diese Nachweismöglichkeiten bestehen aus unterstützendem Material, meistenteils Standards, die das Vorgehen und die Zielgrößen für Analysen und Tests definieren. Die AMC werden durch die entsprechenden Behörden, im Falle einer Musterzulassung der europäischen Luftfahrt also durch die europäische Luftfahrtbehörde EASA, anerkannt. Im Rahmen des Forschungsprojektes AW-Drones wird das Ziel verfolgt, bestehende Standards als mögliche AMC für die entstehenden Regulierungsprozesse für UAS zu identifizieren und ihrer Eignung nach einzuschätzen. Der Fokus des Projektes liegt in einem schrittweisen Ansatz, der zunächst darauf ausgelegt ist, geeignete AMC für die Risikoklassen SAIL I-IV des SORA Prozesses zu finden. Dieser Ansatz soll später auf Themenbereiche wie U-Space erweitert werden. Der erste Schritt zur Umsetzung der Untersuchungen beginnt mit dem Aufstellen einer Datenbasis UAS relevanter Standards, die zusätzlich nach Themenkomplexen strukturiert ist, um eine grobe Übersicht herzustellen. Anschließend erfolgt eine Bezugserstellung zu den Anforderungen der SORA und als letzter Schritt die eigentliche Analyse der Eignung eines Standards als mögliches AMC anhand verschiedener, gewichteter Kriterien. Daraus werden zum einen potentielle AMC, zum anderen Lücken in unterstützenden Standards identifiziert

OpenAP: An open-source aircraft performance model for air transportation studies and simulations

Air traffic simulations serve as common practice to evaluate different concepts and methods for air transportation studies. The aircraft performance model is a key element that supports these simulation-based studies. It is also an important component for simulation-independent studies, such as air traffic optimization and prediction studies. Commonly, contemporary studies have to rely on proprietary aircraft performance models that restrict the redistribution of the data and code. To promote openness and research comparability, an alternative open performance model would be beneficial for the air transportation research community. In this paper, we introduce an open aircraft performance model (OpenAP). It is an open-source model that is based on a number of our previous studies, which were focused on different components of the aircraft performance. The unique characteristic of OpenAP is that it was built upon open aircraft surveillance data and open literature models. The model is composed of four main components, including aircraft and engine properties, kinematic performances, dynamic performances, and utility libraries. Alongside the performance model, we are publishing an open-source toolkit to facilitate the use of this model. The main objective of this paper is to describe each main component, their connections, and how they can be used for simulation and research in practice. Finally, we analyzed the performance of OpenAP by comparing it with an existing performance model and sample flight data.</p

Distributed Conflict Resolution at High Traffic Densities with Reinforcement Learning

Future operations involving drones are expected to result in traffic densities that are orders of magnitude higher than any observed in manned aviation. Current geometric conflict resolution (CR) methods have proven to be very efficient at relatively moderate densities. However, at higher densities, performance is hindered by the unpredictable emergent behaviour from neighbouring aircraft. Reinforcement learning (RL) techniques are often capable of identifying emerging patterns through training in the environment. Although some work has started introducing RL to resolve conflicts and ensure separation between aircraft, it is not clear how to employ these methods with a higher number of aircraft, and whether these can compare to or even surpass the performance of current CR geometric methods. In this work, we employ an RL method for distributed conflict resolution; the method is completely responsible for guaranteeing minimum separation of all aircraft during operation. Two different action formulations are tested: (1) where the RL method controls heading, and speed variation; (2) where the RL method controls heading, speed, and altitude variation. The final safety values are directly compared to a state-of-the-art distributed CR algorithm, the Modified Voltage Potential (MVP) method. Although, overall, the RL method is not as efficient as MVP in reducing the total number of losses of minimum separation, its actions help identify favourable patterns to avoid conflicts. The RL method has a more preventive behaviour, defending in advance against nearby neighbouring aircraft not yet in conflict, and head-on conflicts while intruders are still far away.</p

Beyond Ecological Interface Design: Lessons From Concerns and Misconceptions

The ecological interface design (EID) paradigm was introduced in the process control domain 25 years ago by Kim Vicente and Jens Rasmussen, as a way to help operators cope with system complexity and events unanticipated in the design of automated control systems. Since that time, this perspective has sparked interest in other safety-critical sociotechnical domains where humans cooperate with computerized systems to ensure safe and efficient system behavior. Many of our own, but also other explorations have, however, resulted in several usability concerns and misconceptions about the EID perspective as a viable design approach. This paper discusses some of these concerns and misconceptions, where the final goal is to get past the EID label and to consider the general lessons relative to the demands and opportunities that advanced information technologies offer and complex systems require. This paper concludes with a preliminary outlook for the future of EID, where it is anticipated that the adjective “ecological” will become increasingly redundant, as the focus on supporting “productive thinking” becomes the dominant paradigm for engineering representations

Improving Algorithm Conflict Resolution Manoeuvres with Reinforcement Learning

Future high traffic densities with drone operations are expected to exceed the number of aircraft that current air traffic control procedures can control simultaneously. Despite extensive research on geometric CR methods, at higher densities, their performance is hindered by the unpredictable emergent behaviour from surrounding aircraft. In response, research has shifted its attention to creating automated tools capable of generating conflict resolution (CR) actions adapted to the environment and not limited by man-made rules. Several works employing reinforcement learning (RL) methods for conflict resolution have been published recently. Although proving that they have potential, at their current development, the results of the practical implementation of these methods do not reach their expected theoretical performance. Consequently, RL applications cannot yet match the efficacy of geometric CR methods. Nevertheless, these applications can improve the set of rules that geometrical CR methods use to generate a CR manoeuvre. This work employs an RL method responsible for deciding the parameters that a geometric CR method uses to generate the CR manoeuvre for each conflict situation. The results show that this hybrid approach, combining the strengths of geometric CR and RL methods, reduces the total number of losses of minimum separation. Additionally, the large range of different optimal solutions found by the RL method shows that the rules of geometric CR method must be expanded, catering for different conflict geometries