The mass of an aircraft is crucial for performance-related studies, such as predicting flight trajectories and analyzing flight emissions. In these studies, the flight trajectories are often reconstructed using a point-mass aircraft performance model combined with flight profiles from surveillance data and take-off mass information. However, airlines do not usually disclose take-off mass information, considering its sensitive nature. Thus, aircraft masses often need to be assumed or estimated. This paper presents a simple and computationally effective approach for estimating take-off mass using only open data and models. We explore the strong correlation between take-off mass, flight distance, cruise altitude, and partially, the airspeed during the cruise. The main idea is to generate fuel-optimal trajectories with known masses and distances, and then compare them with actual flight data. The optimal trajectories are generated using the open aircraft performance and optimization library. By assuming that actual flights follow quasi-fuel-optimal trajectories, the take-off mass of a flight can be estimated based on simple regression models trained on the optimal trajectory dataset. This open-loop take-off mass estimation approach requires no proprietary information from aircraft manufacturers or airlines. We verified the model with an anonymized dataset containing actual A320 flights with known take-off mass. Our two- and three-feature multi-linear models yield mean absolute percentage errors of 5.95 % and 4.89 %, respectively. This study is another step forward in open science and a contribution to the aircraft trajectory studies.

Very-low-level (VLL) urban air operations have been extensively investigated as a solution for mitigating congestion in cities. However, the manner in which the management of such traffic should be performed is still actively investigated. One important component of such a system is the conflict detection and resolution (CD&R), mainly composed of the strategic and tactical CD&R module. While many approaches towards these have been studied, insufficient analysis has been conducted on their compatibility when functioning within a unified, hybrid system. Additionally, their robustness to operational uncertainties such as wind and departure delays is often overlooked. In this work, we investigate the performance of strategic planing methods when combined with tactical CD&R and subjected to a wide range of traffic demand levels and uncertainty conditions. Simulations indicate that the performance of the strategic deconfliction module is highly sensitive to the presence of wind and delay. This decline in performance is partially mitigated by the tactical deconfliction module. Thus, the results suggest that increased use of tactical CD&R could lessen the required level of detail of strategic deconfliction methods, leading to improved compatibility between the two modules.

Reinforcement learning (RL) is a method that has been studied extensively for the task of conflict-resolution and separation management within air traffic control, offering advantages over analytical methods. One key challenge associated with RL for this task is the construction of the input vector. Because the number of agents in the airspace varies, methods that can handle dynamic number of agents are required. Various methods exist, for example, selecting a fixed number of aircraft, or using methods such as recurrent neural networks or attention to encode the information. Multiple studies have shown promising results using these encoder methods, however, studies comparing these methods are limited and the results remain inconclusive on which method works better. To address this issue, this paper compares different input encoding methods: three different attention methods – scaled dot-product, additive and context aware attention – and long short-term memory (LSTM) with three different sorting strategies. These methods are used as input encoders for different models trained with the Soft Actor–Critic algorithm for separation management in high traffic density scenarios. It is found that additive attention is the most effective at increasing the total safety and maximizing path efficiency, outperforming the commonly used scaled dot-product attention and LSTM. Additionally, it is shown that the order of the input sequence significantly impacts the performance of the LSTM based input encoder. This is in contrast with the attention methods, which are sequence-independent and therefore do not suffer from biases introduced by the order of the input sequence.

U-space/UTM operations are considered an integral part of the future development of cities, with applications ranging from package delivery to urban air mobility. However, this new complex environment also poses challenges for the conflict detection and resolution (CD&R) process, especially if aircraft will have to navigate above the existing street network due to privacy and obstacle constraints. The research at hand aims to investigate how information about the environment and other aircraft can be used to improve the performance of CD&R methods in constrained urban airspace. For this, three algorithms are developed and tested, each with different levels of information availability: the first solely uses current state information for conflict solving, the second includes additional information about the urban environment within the CD&R process, and the last also incorporates trajectory intent data to solve conflicts. These methods are tested within simulations of urban air traffic scenarios at various demand and wind levels to determine their safety and efficiency performance. Results show the use of street geometry information benefits the resolution process greatly, increasing the safety level while minimally affecting efficiency. Intent information is shown to not be critical for achieving this.

Contrail optimization offers an efficient and cost-effective way for aviation to immediately reduce its climate impact. Open-source optimization, wherein the contrail and emission effects are balanced based on meteorological open data, has been presented in previous work. However, prior research overlooks the importance of using forecasting data, as opposed to post-processed reanalysis data. For contrail optimization to be implementable, forecasting data needs to be available at a sufficient quality in the flight planning stage in order to perform the optimization. In this paper, a fully open non-linear optimal control flight optimization is implemented and applied using both forecasting and reanalysis data. A total of 120 days (175.440 flights) of flight data from OpenSky are used in the analysis. We show that forecasts with larger lookahead times (up to 12 hours) are equally effective when compared to more recent forecasts (1 hour lookahead time) for contrail optimization, with equally high accuracy. However, when compared to more accurate post-processed reanalysis data, there are considerable differences in predicted contrails formed. This research shows there is still a long way to go before we can actually implement contrail optimal flight planning.

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.

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.

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.

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.

U-Space drone operations are expected to be a driver for further urban development, especially through use cases such as medical and commercial parcel delivery. In particular, package delivery using small drones shows great promise, with e-commerce giants such as Amazon deploying limited-scale drone delivery trials in rural areas. As the technology matures, large-scale operations will take place in constrained urban areas, leading to high airborne traffic densities. It is necessary to develop a robust automated separation management system that actively ensures safe separation of drones both in the air and on the ground. This paper focuses on the Strategic element, more specifically on pre-departure planning. The aim of this is to reduce the chance of conflicts around vertiports, where spatial and environmental constraints make tactical resolutions difficult. This work focuses on two scenarios: a single pad for both takeoffs and landings (in a spatially constrained urban area) and 4 takeoff-landing pad pairs (for a distribution center). Several methods are compared for this takeoff sequencing task, coupled with a conflict detection algorithm: A First-Come First-Served method that applied delay to conflicting flights, a Mixed-Integer Programming approach, a Genetic Algorithm, Particle Swarm Algorithm and Simulated Annealing were used. For a single-pad approach, first-come first-served works best in terms of computation time and total deployment time (or makespan). For the multi-pad approach however, changing the flight sequence through metaheurisitic methods and mixed-integer linear programming show a reduction in total deployment time.

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.

As urban ground transportation congestion increases, there is growing interest in urban air transportation, such as delivery drones and air taxis. However, managing air traffic in densely populated urban areas poses significant challenges, which require effective flight planning, separation management, and airspace design. This paper investigates dynamic capacity balancing methods to manage air traffic in constrained urban airspace, where drones must fly above the existing road network. Specifically, it compares the effectiveness of labelling high-complexity zones using historical data versus real-time aggregate flow data. The results indicate that while both approaches reduce airspace intrusions and improve safety, the best approach depends on traffic demand levels. At lower demand levels, using historical data yields better safety outcomes, whereas using real-time data is more effective at higher demand levels due to its flexibility. At their best, both methods increase the travel distance by less than 6% while reducing airspace intrusions by 30% compared to a case without dynamic capacity balancing.

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.

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.

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.

<p>There are several efforts to explore employing drones to replace ground transportation in cities. However, this would mean that the expected traffic densities would be significantly higher than existing air traffic management. A decentralised system for traffic management may be necessary in this future because (1) not all airspace actors will want to freely share data, (2) the uncertainty of missions due to wind or other factors could make a previous plan inoperable, and (3) the ad hoc nature of urban missions makes them difficult to plan in advance. This work focuses on the challenges of drone operations within constrained urban airspace. We define constrained airspace as a virtual network overlaid on the physical environment, where tall buildings and urban infrastructure dictate the allowed routes. Drones are restricted to flying within this virtual network, either above the existing street network or along other predetermined segments. A dynamic and decentralised traffic management method is presented. The method uses current aggregate flow data to identify and alter the cost of travelling through high-density clusters. The goal is to reduce local traffic density and complexity by encouraging alternate routes. Three different clustering strategies are presented that look at the current position of aircraft and recent safety events. The dynamic traffic management method is first illustrated with two simple example scenarios. Then an experiment is conducted with different traffic demand levels within the city of Rotterdam. It was observed that when using traffic complexity indicators, the method is able to reduce safety events by 30 percent while only increasing the distance travelled by 6 percent.</p><p> </p>

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.

With the rapid increase in the use of Unmanned Aerial Systems (UAS) for commercial applications such as medical and parcel delivery, the need for safe airborne separation in airspace has become critical. This paper examines the impact of position uncertainty on autonomous separation methods within U-Space, a European Union initiative for managing drone traffic. The study focuses on evaluating various conflict resolution algorithms—specifically, Modified Voltage Potential (MVP) and Velocity Obstacle (VO) variations—under conditions of navigational uncertainty. Through Monte Carlo simulations using the BlueSky ATM simulator, position uncertainty stemming from Global Navigation Satellite Systems (GNSS) errors is modelled and analysed. The research compares the effectiveness of different conflict resolution strategies in preventing conflicts between UAS, measuring intrusion prevention rates and the closest point of approach during encounters. The results indicate that MVP provides superior performance in handling positional uncertainty, offering more robust conflict resolution capabilities than VO-based methods especially at shallow angles conflict situation. These findings are critical for ensuring the safe integration of UAS into increasingly congested airspace environments, guiding future developments in U-Space operations.

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.

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.