Abstract—In the coming decades, drones are expected to operate within urban areas at high volumes, and if implemented suc- cessfully, applications such as infrastructure inspection, medical supply and parcel delivery can be improved by the technology. This poses a challenge: how are these drones to be guided in this highly-constrained airspace? Many existing projects have approached the problem from different angles: some place more importance on the Tactical Layer and thus resolving conflicts in flight, while other research focuses on the Strategic Layer with scheduling or airspace design. While analysis is done on a complete system, with all separation management layers implemented, work remains to be done regarding quantifying how these layers interact, and what positive characteristics of these interactions can be utilised to make the system more efficient, safe, and robust to uncertainties. This paper proposes a framework on which this analysis can be performed. Firstly, lay- ers are investigated independently. A feedback system is proposed, where layer outputs are measured, as is the resulting system performance. For instance, an initial hypothesis is that reducing airspace complexity in the Strategic layer, while accounting for uncertainty, will lead to better overall system performance. This can help with minimising flight times and improving overall safety. Also, manoeuvres performed by the Tactical (in-flight) layer should take this complexity metric into account. The feedback loop approach also proposes that the complexity be fed back to the central planner, and that the Strategic (Pre-Flight) layer should be able to take system status into account when performing planning. @en

Emissions and contrails are key factors in aviationinduced climate change, often presenting conflicting objectives in flight trajectory optimization. Previous research typically lacks in optimizer efficiency or in addressing these trade-offs, with limited use of extensive meteorological and flight data. In this paper, a fully open non-linear optimal control flight optimization approach is designed for contrail avoidance and emission reduction, with high computational efficiency. This is achieved by leveraging the most recent trajectory optimizer, OpenAP.TOP, and atmospheric data handling tool, fastmeteo. We present a new compound grid-based objective function that considers both contrails and emissions and introduce four different metrics for evaluating the performance. A total of four months’ worth of data, containing around half a million flights, are gathered from OpenSky for analyses. We show that high levels of contrail mitigation can be achieved, without significantly increasing flight time, distance, or emissions. @en

Abstract—The integration of unmanned aerial systems (UASs) into existing airspace is imminent as the concept is maturing. U- Space is a concept in which UASs are allowed to fly alongside general aviation aircraft, requiring all aircraft in the airspace to transmit navigation data using radio and network identification. Nevertheless, a consensus is required for the separation in case of conflict amidst the limitations of communication, navigation, and surveillance (CNS) systems. Update rate and probability depict the uncertainty in the communication systems, while spread around the ground truth in position describes the navigation accuracy. Then, BlueSky, an open-source simulator, produces thousands of conflict detection and resolutions to evaluate the CNS limitation on the intrusion prevention rate and loss of separation severity. This paper concludes that higher update rate, probability, and position accuracy lead to safer conflict resolution, and selecting 50 meters as the radius of the protected zone results in a 25-meter intrusion in the worst case. @en

Conventional Air Traffic Control is still predominantly being done by human Air Traffic Controllers, however, as the traffic density increases, the workload of the controllers increases as well. Especially for the area of unmanned aviation, driven by the rise in drones, having human controllers might become unfeasible. One of the methods that is currently being investigated for replacing the conflict resolution task of Air Traffic Control is Reinforcement Learning. As violation of the required separation margins, also called an intrusion, is an event of relatively low frequency, using Reinforcement Learning for this task comes with difficulties that can potentially be attributed to data imbalance. This paper artificially increased the traffic density during the training phase of the Reinforcement Learning method to investigate what the importance is of a balanced data set on the performance of the Reinforcement Learning method. It was found that as the traffic density increased, the Reinforcement Learning methods started to outperform the analytical methods. Beyond this it was found that methods trained at higher traffic densities, but tested at lower traffic densities, outperformed the methods trained at that specific density. This indicates that it might be better to always ensure that the training scenarios are more complex than anticipated during the execution phase, even if that results in unrealistic scenarios.

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Weather obstacles in the airspace can interfere with an aircraft’s flight plan. Pilots, assisted by air traffic controllers (ATCs), perform avoidance maneuvers that can be optimized. This paper addresses the generation of alternative aircraft trajectories to resolve unexpected events. The authors propose a solution based on the RRG algorithm, K-means clustering, and Dynamic Time Warping (DTW) similarity metric to address the problem. The mixed algorithm succeeds in generating a set of paths with diversity in an obstacle constrained airspace between Paris-Toulouse and London-Toulouse airports. This tool could help to reduce the workload of pilots and ATCs when such a situation arises.

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Current advancements in machine learning have provided new architectures, such as encoder-decoder transformers, for automatic speech recognition. For generic speech recognition, very high accuracies are already achievable. However, in air traffic control, automatic speech recognition models traditionally rely on domain-specific models constructed from limited training data. This study introduces this newly developed transformer model for air traffic control and provides a set of fully open automatic speech recognition models with high accuracies. This paper demonstrates how a large-scale, weakly supervised automatic speech recognition model, Whisper, is fine-tuned with various air traffic control datasets to improve model performance. We also evaluated the performance of different sizes of Whisper models. In the end, it was possible to achieve word error rates of 13.5% on the ATCO2 dataset and 1.17% on the ATCOSIM dataset with a random split (or 3.88% with speaker split). The study also reveals that finetuning with region-specific data can enhance performance by up to 60% in real-world scenarios. Finally, we have open-sourced the code base and the models for future research. @en

Air traffic sector demand and capacity balancing are necessary for safe and efficient flight execution. Demand and capacity are determined in current operations based on schedules and flight plans. This research aims to improve air traffic demand forecasting by exploring machine learning-based trajectory prediction, specifically the newly emerged transformer-based neural network models. The predicted trajectories are considered to improve demand forecasts for Air Traffic Control in the Netherlands. We successfully built a transformer neural network using available traffic messages from the EuroControl B2B connection and actual trajectories obtained from the OpenSky ADS-B repository. A new lost function is specifically designed to improve this prediction model’s performance. This trajectory predictor could accurately generate trajectories, outperforming the flight plan and other neural network approaches by a good margin. For demand prediction, introducing improved trajectories provided small gains that could lead to more stable predictions. @en

Urban air mobility can be a potential solution for urban congestion, and high-level concepts of operations (e.g., UTM, U-space) have been developed with the help of large-scale simulations of multi-agent systems. However, one aspect that should be researched more is the effect of wind on the safety and efficiency of missions in an urban environment. While studies that analyse the potential effect of wind on U-space operations exist, they mostly use constant wind fields, or highly simplified wind models. The study at hand investigates whether medium-fidelity CFD models can be used to predict and match recorded wind data in the city centre of The Hague. Six locations with distinct urban features were chosen, and wind measurements were recorded on two separate days. Using the rooftop wind properties obtained during the measurement sessions, computational fluid dynamics (CFD) simulations were performed within a large urban model of the city. Results indicate that there are large discrepancies between the simulated and measured values. Some wind phenomena observed within the measured wind data were also replicated by the CFD model. Thus, based on the results presented in this work, future research should focus on improving computer city models and wind measurement methods to ensure the development of concepts of operations that maximise the safety and efficiency of future U-space operations. @en

Many transportation networks have complex infrastructures (road, rail, airspace, etc.). The quality of service in air transportation depends on weather conditions. Technical failures of the aircraft, bad weather conditions, strike of the company’s staff cause delays and disrupt traffic. How can the robustness of such networks be improved? Improving the robustness of air transportation would reduce the cascading delays between airports and improve the passenger journey. Many studies have been done to find critical links and nodes, but not so many analyze the paths. In this paper, we propose a new method to measure network robustness based on alternative paths. Besides improving the robustness of the French (respectively Turkish Airlines and European) low-cost flight network by 19% (respectively 16% and 6.6%), the method attempts to show the relevance of analyzing the network vulnerability from a path-based approach.

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The use of small aircraft for a wide range of missions in urban airspace is expected to increase in the future. In Europe, efforts have been invested into developing a unified system, called U-space, to manage aircraft in dense very-low-level urban airspace. The Metropolis II project aimed to research what degree of centralisation an air traffic management system should use in such airspace. The paper at hand is a follow-up, and investigates improvements that can be brought to the tactical conflict prevention, detection, and resolution module of such a system in order to harmonise these components with an organic high-density U-space environment. The proposed improvements are: the prioritisation of vertical conflict prevention in intersections, the use of intent in detecting and resolving conflicts, and the use of heading-based manoeuvres in open airspace. Results show that the use of intent information in the conflict detection process, as well as the implementation of suitable tactical prevention procedures, can greatly increase airspace safety. Furthermore, the experiments revealed that the effectiveness of conflict resolution algorithms is highly dependent on the airspace rules and structure. This reiterates the potential for increasing the safety and efficiency of operations within constrained airspace if the tactical separation modules are unified with the other components of air traffic management systems for U-space. @en

The interest in urban air mobility as a potential solution for urban congestion is steadily growing. Air operations in urban areas can present added complexity as compared with traditional air traffic management. As a result, it is necessary to test and develop novel airspace designs and rules. As airspace in urban areas is a scarce resource, creating structures and rules that effectively utilise the airspace is an important challenge. This work specifically focuses on layered airspace design in urban operations constrained to fly between the existing buildings. Two design parameters of airspace design are investigated with two sub-experiments. Sub-experiment 1 investigates layer function assignment by comparing concepts from previous research with different layer assignment distributions. Sub-experiment 2 investigates the flight rules of vertical distribution of traffic within the airspace, to determine whether this is best achieved in a static (pre-allocated) or dynamic manner. Both sub-experiments analyse the overall system safety, route duration, and route distance under increasing traffic demand. Results reveal that the importance of cruising airspace is apparent at high densities. Results also shows that the safest layer allocation flight rule depends on the traffic density. At lower densities dynamic rules help to spread traffic locally. However, when the airspace is saturated it is safer to pre-allocate flight heights if achieved uniformly.@en

Unmanned Aircraft Systems (UAS) Traffic Management (UTM) is an active research subject as its proposed applications are increasing. UTM aims to enable a variety of UAS operations, including package delivery, infrastructure inspection, and emergency missions. That creates the need for extensive research on how to incorporate such traffic, as conventional methods and operations used in Air Traffic Management (ATM) are not suitable for constrained urban airspace. This paper proposes and compares several traffic capacity balancing methods developed for a UTM system designed to be used in highly dense, very low-level urban airspace. Three types of location-based dynamic traffic capacity management techniques are tested: street-based, grid-based, and cluster-based. The proposed systems are tested by simulating traffic within mixed (constrained and open) urban airspace based on the city of Vienna at five different traffic densities. Results show that using local, area-based clustering for capacity balancing within a UTM system improves safety, efficiency, and capacity metrics, especially when simulated or historical traffic data are used.

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Following the Paris Accords, the aviation industry aims to become climate neutral by 2050. In this line, electric vehicles that tow aircraft during taxiing are a promising emerging technology to reduce emissions at airports. This paper proposes an end-to-end optimization framework for electric towing vehicles (ETVs) dispatchment at large airports. We integrate the routing of the ETVs in the taxiway system where minimum separation distances are ensured at all times, with the assignment of these ETVs to aircraft towing tasks and scheduling ETV battery recharging. For ETV recharging, we consider a preemptive charging policy where the charging times depend on the residual state-of-charge of the battery. We illustrate our model for one day of operations at a large European airport. The results show that the 913 arriving and departing flights can be towed with 38 ETVs, with battery charging distributed throughout the day. The fleet size is shown to increase approximately linear with the number of flights in the schedule. We also propose a greedy dispatchment of the ETVs, which is shown to achieve an optimality gap of 6% with respect to the number of required vehicles and with 22% with respect to the maximum delay during towing. We also show that both algorithms can be leveraged to account for flight delays using a rolling horizon approach, and that over 95% of the flights can be reallocated if delays occur. Overall, we propose a roadmap for ETV management at large airports, considering realistic ETV specifications (battery capabilities, kinematic properties) and requirements for aircraft collision avoidance during towing.

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The number of unmanned aircraft operating in the airspace is expected to grow exponentially during the next decades. This will likely lead to traffic densities that are higher than those currently observed in civil and general aviation, and might require both a different airspace structure compared to conventional aviation, as well as different conflict resolution methods. One of the main disadvantages of analytical conflict resolution methods, in high-traffic density scenarios, is that they can cause instabilities of the airspace due to a domino effect of secondary conflicts. Therefore, many studies have also investigated other methods of conflict resolution, such as Deep Reinforcement Learning, which have shown positive results, but tend to be hard to explain due to their black-box nature. This paper investigates if it is possible to explain the behaviour of a Soft Actor-Critic model, trained for resolving vertical conflicts in a layered urban airspace, by interpreting the policy through a heat map of the selected actions. It was found that the model actively changes its policy depending on the degrees of freedom and has a tendency to adopt preventive behaviour on top of conflict resolution. This behaviour can be directly linked to a decrease in secondary conflicts when compared to analytical methods and can potentially be incorporated into these methods to improve them while maintaining explainability.@en

Contrails contribute to global warming by trapping outgoing terrestrial radiation, exerting an immediate warming influence on the climate. The climatic impact of contrails is potentially comparable to that of aviation’s carbon emissions. This underlines the importance of minimizing contrail formation to mitigate the climate effects of aviation, both now and in the future. The evaluation of contrails demands more precise data on the location and altitude where they form. Remote sensing imagery enables the identification of their location. Nevertheless, determining the altitude of the contrail remains problematic, complicating the identification of the source flight. This study introduces a novel method that enables researchers to determine the altitude of a contrail solely using Landsat data by analysing shadows cast by contrails. Through validation against ADS-B data from OpenSky, we demonstrate that such a technique can achieve the accuracy of a few hundreds of meters, which is suitable for incorporation into a climate-optimized routing system. Finally, a ResUNet segmentation model is also presented, which can identify contrails and their shadows in Landsat imagery. These results constitute a step forward for more accurate contrail dataset and models. @en

Taxiing aircraft using electric towing vehicles (ETVs) is expected to significantly contribute to the objective of climate-neutral aviation by 2050. This study reviews existing work on operational aspects of electric towing of aircraft, and discusses management solutions. We first discuss the varying electric taxi systems currently under development, and their implementation progress at airports. We outline the current specifications of ETVs and the procedures needed to perform electric taxiing movements. We next discuss the management needs for implementing ETVs at an airport, by reviewing existing mathematical models for ETV fleet management: dedicated vehicle routing models, ETV to flight assignment models, fleet sizing models and battery charging optimisation models. Last, we identify remaining research challenges. For instance, a main challenge is to increase the robustness of ETV routing and towing scheduling against disruptions due to flight delay. This paper summarizes the main research directions needed to support large-scale ETV implementation in the next few decades.@en

As global flight volume rises, the aviation industry is facing increasing climate challenges. One major factor is the impact of contrails, which trap outgoing terrestrial radiation and counteract emission reduction benefits from emission-optimized flight routes. Our study quantifies contrail-forming flights globally and assesses altitude adjustments necessary to avoid these regions. Using the Integrated Global Radiosonde Archive and global flight data from 2021-2022, we highlight several contrail-prone regions with high air traffic volumes and high potential for contrail-formation. We propose an operational strategy in altitude diversion, which can halve the amount of persistent contrails. Further, we analyse the additional carbon emissions caused by the altitude diversions and safety risks in terms of potential new conflicts. Our findings provide actionable strategies for policymakers to balance climate mitigation and operational challenges in aviation.

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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.

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One of the biggest challenges facing the aerospace industry today is its sustainability. As the number of flights is expected to rise globally, aviation’s climate impact will continue to increase. Current research has extensively addressed the rerouting of aircraft through wind-optimization in order to minimize fuel burn and emissions. Such optimization is currently implemented for flight planning. Although this strategy is optimized for fuel burn and emissions, it does not necessarily minimize the overall climate impact. Navigating optimally through wind fields could mean flying through regions with a higher climate impact, where warming contrails are formed. This can occur when contrails trap outgoing terrestrial radiation and so contribute to global warming. This warming contrail creation could potentially forfeit the climate gain of the reduced emissions from the wind-optimized route. In order to implement such a climate-optimized routing model, knowledge about the atmospheric conditions under which contrails form is required. One existing theorem is the Schmidt-Appleman Criterion, which uses the air temperature, relative humidity and ambient air pressure to determine whether contrail formation is possible. In addition, the ice-supersaturation criterion model indicates contrail persistence. In this paper, multiple open data sources are used to examine the use of this established criterion, to evaluate the appropriateness of these data sources for future use in a climate-optimized routing model. Based on the obtained results, we show that, with these data sources, the combination of Schmidt-Appleman and the ice-supersaturation criterion can produce a more reliable determination of contrail formation. The results can be used for an improved unified and data-driven model for the purposes of climate-optimized routing. @en

The Metropolis II project aimed to study the impact of centralised separation management for urban aerial mobility. Three concepts were developed in this study: a fully centralised, strategically separated concept, a hybrid concept featuring cen- tralised strategic separation and distributed tactical separation, and a fully distributed tactical concept. A comparative simu- lation study was performed, using traffic scenarios based on predicted demand in an urban airspace in the city of Vienna. Simulations were performed with varying traffic densities and situations. Results show that the purely strategic and purely tactical strategies perform comparably in terms of safety, and that further improvements can be achieved with a combination of those strategies.@en