Urban air mobility (UAM) is presented as a potential solution to urban congestion. By utilising aerial vehicles for tasks like parcel delivery, public transport, and surveillance, pressure on traditional ground-based transportation infrastructure can be alleviated. This is particularly important with the rise of e-commerce and the increasing demand for fast and efficient delivery methods. UAM has the potential to revolutionise urban travel, offering faster commutes and enhancing surveillance capabilities for improved traffic management and emergency response.

The U-space concept, developed within the European Union, provides a framework for the safe integration of drones and small unmanned aircraft systems (sUAS) into urban airspace. It focuses on establishing services, regulations, and procedures to manage UAM operations effectively. An important component of this concept is Type Zu airspace, designated for high-density urban operations. This airspace requires strict regulations and safety-critical services like dynamic capacity management, conflict resolution, and continuous monitoring to ensure safe and efficient U-space operations.

Conflict detection and resolution (CD&R) of air traffic is required to ensure the safety of such operations, and VLL urban airspace presents unique challenges compared to conventional air traffic management. Buildings and other obstacles restrict aircraft movement, making manoeuvring and conflict avoidance more difficult. Unpredictable urban wind patterns further complicate flight planning and trajectory prediction. These factors, combined with the inherent complexity of urban environments, necessitate the development of robust CD&R algorithms and rules specifically tailored to the challenges of VLL urban airspace.

The core research objective of this dissertation is to identify and develop effective CD&R algorithms and rules for safe and efficient UAM operations in VLL urban airspace. This involves evaluating the limitations of existing CD&R methods, designing new algorithms that address the specific challenges of urban environments, and defining clear rules and procedures for aircraft navigation and conflict resolution…
@en

With its 500.000 flights per year, Schiphol Airport is one of the busiest airports in Europe. Efficient runway use is vital for a smooth day-to-day operation. This paper investigates the new runway scheduler at Schiphol Airport, also referred to as the Departure Manager (DMAN). The new DMAN is built for a better utilisation of the outbound capacity with a higher predictability. This study aims to verify whether positive behaviour regarding earlier Target Off-Block Time (TOBT) updates leads to the desired results in capacity, predictability and reduced delay, based on historical data. An experimental model was used to simulate a new Departure Manager (DMAN), which uses a set of priority rules to assign flights to the runway slot in which they depart. The simulation did not find an overall significant result for all cases, runways and months, due to the averages influenced by the size of the data. However, a positive trend can be seen in the presented results, indicating that earlier TOBT updates lead to a better runway schedule. One cannot schedule better than on time, but late TOBT updates accumulate like a snowball effect throughout the planning.

The use of drones in combination with a delivery truck can have a significant impact in improving the efficiency of last-mile delivery. Drones can be dispatched to customers from the truck, allowing the truck to continue delivering packages at the same time. This approach gives rise to the widely researched Traveling Salesman Problem with multiple Drones (TSP-mD). Numerous heuristic models have been developed to solve the problem in a near-optimal manner. However, these optimization strategies do not account for disruptions, which are common in delivery networks and can negatively impact their performance. While existing literature usually considers static models, a more dynamic approach could address these disruptions by adapting to real-time circumstances. To explore this, a dynamic method is developed in this paper for solving the TSP-mD. Its efficiency is compared to an existing static heuristic model from the literature. The comparison is performed in the BlueSky Open Air Traffic simulator, in which disruptions are introduced, such as truck delays and drone speed variations. Experiments in this environment demonstrate that the existing algorithm consistently achieves shorter mission completion times across all uncertainty settings. However, the newly developed method shows a significant improvement in performance under uncertain conditions. Therefore, the use of global optimization for the TSP-mD should be reconsidered.

This paper explores the impact of Automatic Dependent Surveillance - Contract (ADS-C) on air traffic control (ATC) procedures, focusing on operational efficiency and safety margins in lower airspace. Through 64 simulation configurations, the study evaluates how varying airspace density, separation buffer size, and vertical error in ADS-C data influence operational metrics, such as fuel burn, track miles, and flight time. The simulations utilize synthetic ADS-C data with a 100% equipage rate, providing insights into how ADS-C can be applied to manage intersecting flight trajectories. Results indicate that separation buffer size is the most influential factor. Smaller buffers lead to significant reductions in fuel burn, track miles, and flight time compared to the baseline, though this comes at the expense of increased conflict risks. Airspace density demonstrated trends where higher densities showed the greatest fuel savings but more conflicts, highlighting a trade-off between operational efficiency and safety. These findings support the role of ADS-C in increasing predictability and improving trajectory management, both of which are key to Trajectory-Based Operations (TBO). By improving the accuracy of aircraft intent and trajectory data, ADS-C can optimize flight paths and enable more efficient air traffic management. However, carefully considering separation buffers and airspace density is essential to balance efficiency with safety.

The expected growth of civil air traffic and the inclusion of advanced systems in the Royal Netherlands Air Force result in more demanding airspace requirements across all users, making this a scarce resource. To optimise its usage, military airspaces in Amsterdam Flight Information Region are used as Flexible Use of Airspace (FUA), which no longer considers airspace as entirely ‘civil’ or ‘military’, but as a continuum to be allocated temporarily according to user requirements. Given its importance for civil-military cooperation, FUA is at the core of the Dutch Airspace Redesign Programme, considering both a reorganisation of FUA structures and the plannability policies they are reserved with. In order to inform these decisions, this study analyses the effect of FUA availability and plannability on the fuel efficiency of civil commercial traffic. Historical traffic data from the Eurocontrol R&D data archive is sampled for the month of March 2019 and used in three experiments. On the one hand, Experiment 1 investigates FUA availability by considering flights losing route efficiency due to FUA sectors and comparing them with Great Circle Route alternatives and similar flights historically transiting them. On the other hand, Experiment 2 considers flights making use of FUA sectors during times when these have been delegated to civil use to assess the effects of carrying a surplus fuel due to an insufficient airspace plannability. By proposing new plannability policies, the hypothetical reduction in fuel consumption as a result of not taking the surplus fuel is assessed. Lastly, Experiment 3 combines the benefits found in Experiment 1 with the plannability policies of Experiment 2 to determine the fuel benefits resulting from a tactical rerouting enabled by the new plannability concepts. A total of 1,548 simulations have been performed in the open source air traffic simulator BlueSky to compute the fuel efficiency metrics. The results suggest that making both the Alpha and Delta sectors completely available would result in a yearly reduction in fuel consumption of 70,198 and 100,022 tonnes, respectively; 8,908 and 13,301 of which would be saved solely by adopting a new plannability policy (corresponding to 28,060 and 41,898 tonnes of CO2). Finally, not carrying a surplus fuel due to this concept would contribute to an extra 270 and 394 tonnes of fuel consumption being reduced in 2019 (851 and 1,241 tonnes of CO2).  

In order to enable the safe and efficient integration of Unmanned Aerial Vehicles into very low level airspace, modern day research focuses on the development of new traffic services and procedures. One of these is the geovectoring protocol, which aims to reduce traffic complexity by setting limits on the allowed ground speed, course, and vertical speed. A geovector can be used to increase the capacity of an airspace by lowering the conflict rate. However, problems with priorities emerge when performing conflict resolution maneuvers in geovector airspace, as the limits are ignored in this process. A powerful conflict resolution algorithm is the Modified Voltage Potential (MVP). This research proposes an extension to the MVP ruleset, based on Velocity Obstacle theory. Making use of an alternative conflict resolution maneuver which respects the geovector, five resolution strategies are defined with different priority settings for the separate limits. The performance of these strategies is compared to pure MVP on geovector, safety, and stability measures, making use of fast-time simulations in a corridor airspace. All resolution strategies show improvements on the ability to perform conflict resolution maneuvers within the geovector limits, albeit at the expense of safety and stability. It is recommended to further investigate the performance of the geovector resolution strategies for other types of airspace, to verify whether the observed reduction in conflict rate from the geovectors can be reinforced by the resolution strategies.

To facilitate an increase in air traffic volume and to allow for more flexibility in the flight paths of aircraft, an abundance of decentralized conflict resolution (CR) algorithms have been developed. The efficiency of such algorithms often deteriorates when employed in high traffic densities. Several methods have tried to prioritize certain conflicts to alleviate part of the problems introduced at high traffic densities. However, manually establishing rules for prioritizing intruders is a difficult task due to the complex traffic patterns that emerge in multi-actor conflicts. Reinforcement Learning (RL) has demonstrated its ability to synthesize strategies while approximating the system dynamics. This research shows how RL can be employed to improve conflict prioritization in multi-actor conflicts. We employ the Proximal Policy Optimization algorithm with an actor-critic network. The RL model decides on intruder selection based on the local observations of an aircraft. It was trained on a limited number of conflict geometries in which it was able to significantly reduce the number of intrusions. A conflict prioritization strategy was then formulated based on the decisions taken by the RL model during training. We show that the efficacy of a conflict resolution algorithm that adopts a global solution, the solution space diagram (SSD) in this research, can be improved when utilizing this conflict prioritization strategy. Finally, these results were compared to the performance of a pairwise CR method, the Modified Voltage Potential (MVP). Even though MVP resulted in a smaller number of intrusions compared to SSD with conflict prioritization, the prioritization strategy did reduce the gap between the two CR methods.

For future operations of unmanned aviation, even higher traffic densities than previously seen in manned aviation are expected. Previous work has shown that a vertically layered airspace design performs best at improving safety metrics such as the total number of conflicts and Losses of Separation (LoSs). Furthermore, it has been shown that machine learning techniques are capable of selecting heading ranges for the vertically stacked layers in non-uniform traffic scenarios, in order to reduce the number of conflicts and LoSs compared to uniform structures. These works, however, set structures in an ‘empty’ airspace and do not take into account the necessary vertical deviations to get from one structure to the next. In this work reinforcement learning (RL) agents are used to select layer heading ranges, while taking into account the previous airspace structure. During this dynamic structuring, several challenges arise. First, it is not clear how to reduce the number of vertical conflicts when aircraft move into a new airspace structure. Second, specific structures should be selected that reduce the necessary vertical deviations from the old structure, while still minimising the cruising conflicts for the new traffic distribution. The present work is divided into three experiments. Experiment I focused on analysing the number of conflicts and LoSs that aircraft suffer during vertically moving towards their layer in the new structure. Experiment II tested whether a RL agent is capable of setting an aircraft structure in function of the expected future traffic scenario. Experiment III aimed to show the capability of a RL agent to select airspace structures, while taking into account the previous airspace structures, in order to decrease the number of vertical conflicts. The results of the research show that RL methods are capable of defining airspace structures appropriate for a given traffic scenario. For dynamic reconfiguration, it proved challenging to simulate traffic scenarios that cause an agent to select different structures to prevent the occurrence of vertical conflicts. Under the experimental conditions employed, analytical methods of structure selection performed better in terms of safety.

The maximum workload for air traffic control officers is a key constraint on the capacity of the airspace system. In the free flight concept air traffic control is removed and aircraft themselves take over its responsibilities. The Modified Voltage Potential (MVP) algorithm is an implementation of this concept. However, its use in the presence of airspace restrictions has not been studied extensively. Five new concepts with additional steering rules added to the MVP using a steering rule hierarchy are tested in a constrained airspace using fast-time simulations. One method adds a relative-position-based velocity averaging rule and the four others add absolute-position-based geovectoring rules. The experiment shows that three of the concepts using geovectoring perform better than the baseline MVP method whereas the velocity averaging concept increases the system workload. Further research is suggested to better understand the interaction effects between the different rules in the steering behavior hierarchy.

In an effort to increase airspace capacity, new methods for the decentralization of air traffic control have been a topic for research since the early nineties. The Modified Voltage Potential is a method to provide decentralized conflict detection and resolution. Through this method, aircraft can fly direct routes while resolving conflicts in a distributed manner. Though successfully implemented in various studies, its behavior in conflicts where aircraft are flying near parallel tracks has raised concern. It was found that problems can arise during the recovery after the conflict resolution, the phase where an aircraft regards the conflict as resolved and reverts to its desired velocity. To improve the Modified Voltage Potential's behavior during recovery, a modification is proposed. Through the use of two criteria, aircraft evaluate whether a conflict is resolved and if they can revert to their initial or desired velocity. Through analysis of a state-based solution space and an experiment, this study finds that the proposed recovery method significantly improves the stability, efficiency, and safety of the MVP method.

This research presents the derivation, implementation and safety assessment of a velocity obstacle- based conflict resolution method to be used by UAVs flying within a horizontally restricted airspace by a geofence under the presence of wind. Two parameters indicating the safety of the applied conflict resolution method have been measured, i.e., the Intrusion Prevention Rate (IPR) and the Violation Prevention Rate of the Geofence (VPRG). Three coordination rule-sets have been implemented i.e., 1) geometric optimum (OPT), 2) geometric optimum from target heading (DEST) and 3) only change in heading (HDG). These rule-sets have been assessed during a safety assessment. It was concluded that the OPT rule-set performed best in terms of the IPR and the DEST rule-set performed best in terms of the VPRG under windy and wind calm conditions. The HDG rule-set performed worst in terms of both safety parameters. It was noted that both safety parameters are the lowest when conflicts occur close the geofence under windy conditions for all implemented rule-sets.

The resilience of the Air Traffic Management (ATM) system to disturbances is required to maintain high operation performance. Before it can be improved, the resilience of the ATM system must be quantified. The measurement of resilience requires knowledge of a system reference state. This thesis proposes a novel methodology to detect disruptions without a pre-specified reference state and to quantify airspace resilience to disturbances. The method utilises residual-based anomaly detection to model a reference state based on historical values and detect deviations from it. The method has been tested in assessing the resilience of arrival time (airspace state)to high winds (disturbance) in 9 airports worldwide for a year. The results have shown that the method is capable of detecting disruptions as well as airports experiencing high wind conditions tend to be more resilient to them.

In the upcoming years, en route airspace capacity will be limited by air traffic controller workload, requiring the introduction of automation to assist controllers with conflict detection and resolution. However, acceptance is considered to be one of the main obstacles in the introduction of novel automation. Individual-sensitive automation has been proposed to increase acceptance by adapting to different controller strategies. This research evaluates how personalized automation for air traffic control can be achieved using convolutional neural networks. A human-in-the-loop experiment is devised to generate datasets consisting of conflict resolution commands with corresponding velocity obstacle images as learning feature. Results show that the trained models can reasonably predict command type, direction and directional value. Furthermore, a correlation is found between a controller consistency metric and achieved prediction performance. Finally, the individual-sensitive models performed significantly better than general group-based models, confirming the strategy heterogeneity of the population, which is a critical assumption for personalized automation.

A large number of stakeholders exist in the modern air traffic management ecosystem. Air transportation studies benefit from collaboration and the sharing of knowledge and findings between these different players. However, not all parties have equal access to information. Due to the lack of open-source tools and models, it is not always possible to undertake comparative studies and to repeat experiments. The barriers to accessing proprietary tools and models create major limitations in the field of air traffic management research. This dissertation investigates the methods necessary to construct an aircraft performance model based on open data, which can be used freely and redistributed without restrictions. The primary data source presented in this dissertation is aircraft surveillance data that can be intercepted openly with little to no restriction in most regions of the world. The eleven chapters in this dissertation follow the sequence of open data, open models, and performance estimations. This order corresponds to the three main parts of the dissertation. In the first part of the dissertation, open surveillance data is explored. Methods are developed to decode and process this data. Extraction of information is also made possible thanks to machine learning algorithms. The second part of the dissertation examines the main components of the open aircraft performance model. Models related to kinematics, thrust, drag polar, fuel flow, and weather are investigated. The third part of the dissertation looks into the possibility of using surveillance data to estimate aircraft performance parameters, for example, aircraft turn performance, aircraft mass, and thrust settings, for individual flights. With the goal of making future air traffic management studies more transparent, comparable, and reproducible, the models and tools proposed in this dissertation are fully open. The final aircraft performance model, OpenAP, proposed in this dissertation has proven to be an efficient open alternative to current closed-source models.@en

Continuous Climb Operations aim at improving the efficiency of a flight's climb phase by reducing the number of level-offs. The associated fuel-benefits help airlines and airports to comply with future emission demands, rising fuel prices and modernizing airspace requirements. Previously, most studies in this field only considered the effect of removing unintended level-off sections requested by Air Traffic Control (ATC). In this study, the effect of all current climb inefficiencies have been studied for Schiphol Airport in the Netherlands. By comparing historic radar tracks and on-board measured flight data with simulated optimized trajectories, potential fuel benefits have been calculated for three scenarios. It was found that on average, 39.9 kg of fuel could be saved per flight with minimum time loss compared to the original departure when abandoning the 250 knots speed restriction below FL100. As a result, 19.2 million kg fuel and 60.8 million kg CO₂ emissions could be reduced annually at Schiphol given the current traffic density.

In an effort to accommodate the increasing air traffic demand, research is conducted to increase future airspace capacity. In addition to the growing demand, new forms of air transport are entering the market. Although a distributed control strategy seems unavoidable to cope with the introduction of Unmanned Aerial Vehicles and Personal Air Vehicles, research on how this distributed control should be organized has not yet reached consensus. Prior research has demonstrated that structuring airspace into layers results in a lower conflict rate compared to an unstructured airspace. The principles of aircraft dispersion and relative velocity reduction in the layers have a positive effect on airspace capacity, and these principles are further explored in this research. In a large-scale simulation experiment with a mix of different Personal Air Vehicles, the value of defining lateral zones in a departure/arrival area was investigated. By comparing the zones concept with an unstructured airspace concept, it was found that structuring the airspace can act as a conflict prevention mechanism. The added value of the zones becomes more apparent at higher traffic densities. Different zone configurations were evaluated and compared. Simulation results show that a configuration with a higher number of zones results in the lowest overall number of conflicts, at the cost of a slight efficiency loss.

Decentralized airspace concepts have been proposed to increase the capacity of airspace. Previous research has showed that decentralized airspace concepts show great improvements in capacity, such as the Layers concept, where height rules are implemented, and unstructured airspace, where there are no procedural constraints. One aspect that determines capacity is safety. Measuring the number of instantaneous conflicts can be used as an intrinsic safety metric for new airspace design. Conflict counts can be measured by doing experiments which can be time consuming, or by using mathematical models. However, these models are derived using certain assumptions about the traffic. The ideal traffic settings for the models may not always be realistic in practice. This research attempts to improve the models and validate how accurate the models are with varying traffic scenarios, so that the conflict count models may be used for more realistic traffic scenarios.

Currently, Air Traffic Control is responsible for the separation of air traffic in our limited airspace. To facilitate the continuing growth of air traffic, an ambitious plan is to give the responsibility of the separation task to the pilots of the aircraft. The behavior of existing conflict resolution methods within this decentralized approach is not completely understood in multi-aircraft conflict scenarios. This research proposes the usage of the Solution Space Diagram, constructed from velocity obstacles, as an automated, horizontal conflict resolution method. With the aid of fast-time simulations, the performance in terms of safety, stability and efficiency of the method using the Solution Space Diagram proved to be worse than an existing method due to coordination issues and the diagram being filled up rapidly under high aircraft densities.