The anticipated growth in air traffic is expected to increase demand on air traffic control capacity beyond its current limits, motivating the development of autonomous conflict detection and resolution (CD\&R) methods for self-separating airspace. While both state-based and intent-based conflict resolution methods have been studied extensively, a direct comparison between the two approaches under identical conditions is lacking. This paper addresses that gap by comparing the intent-based A3 CD\&R model to the state-based Modified Voltage Potential (MVP) resolution method. Performance is evaluated through verification scenarios and random traffic simulations at three density levels, using simulations in the BlueSky ATM simulator. Results show that the A3 method achieves substantially fewer conflicts and losses of separation than MVP across all density levels, with a markedly lower domino effect parameter indicating better airspace stability. However, when losses of separation do occur under the A3 model, the intrusions are larger than those observed with MVP. Regarding efficiency, MVP incurs large flight time increase due to speed-based resolutions, whereas the A3 model resolutions result in minimal flight time increases at the cost of larger route deviations. These findings demonstrate that intent information provides meaningful advantages for conflict resolution performance.

Departure management at major airports relies on Target Off-Block Time (TOBT), a human-declared readiness estimate that is prone to inaccuracy. Sensor-derived predictions from computer-vision turnaround monitoring offer a complementary signal, but their higher update frequency and distinct error profile risk destabilising the departure sequence.

This study evaluates whether Predicted End of Ground handling Time (PEGT) predictions can improve departure sequencing at Amsterdam Airport Schiphol without sacrificing schedule stability. A characterisation of operational PEGT data shows that PEGT becomes more accurate than TOBT within approximately 27 minutes of departure, but produces nearly twice as many updates and exhibits pessimistic bias in the final minutes before off-block. These properties motivate the design of selective acceptance filters.

Using a reconstructed rule-based Departure Manager and counterfactual replay of 21,152 departures across 31 operating days (August 2024), 230 configurations of five conjunctive, interpretable acceptance filters were evaluated via Latin hypercube sampling. Results show that unrestricted PEGT adoption reduces vacated slots by 22.6% but increases late resequencing by 18.6%, confirming that improved accuracy alone does not guarantee operational improvement.

However, selective filtering, predominantly through suppression of frequent and late-stage updates, identifies a regime of 55 configurations (24% of those tested) that simultaneously improve all five metrics relative to the TOBT-only baseline: resequencing (-0.6%), late resequencing (-6.6%), vacated slots (-13.3%), TSAT delay (-1.6%), and on-time performance (+0.2%). These configurations improve both the TOBT-only and naive unrestricted-PEGT baselines on every tested metric, demonstrating that composite use of TOBT and selectively filtered PEGT can transcend the baseline stability–slot adherence trade-off.

The results are based on one month of nominal operations at Amsterdam Airport Schiphol; generalisation to disrupted conditions and other departure management architectures requires further investigation.

European hub airports face persistent demand-capacity imbalances generating 22.4 million minutes of delays in 2024, costing €2.8 billion. Traditional reactive approaches prove inefficient for long-haul transatlantic flights. Moreover, long-haul flights have historically been granted exemptions from ground delay programs, meaning short-haul flights disproportionately absorb delays during capacity constraints. This research develops an uncertainty-aware cruise speed control strategy for managing transatlantic arrivals to Amsterdam Schiphol Airport. The methodology decomposes arrival time uncertainty using Johnson distributions conditioned on flight status and temporal horizon, continuously monitoring probability that demand at Initial Approach Fixes exceeds capacity. One-time strategic speed adjustments trigger only when exceedance probability surpasses a predetermined threshold of 70 \%. Fast-time simulation of 30 days using BlueSky and EUROCONTROL trajectory data validates the approach. Across 79 interventions, the strategy achieved 35 \% capacity exceedance certainty reductions on average. These required only 8.72 minutes average delay through conservative Mach reductions of 0.01-0.05. The approach offers a practical, environmentally beneficial pathway for long-range air traffic flow management at capacity-constrained hubs.

There is increasing interest in deploying autonomous air vehicles or drones in urban environments for missions such as package delivery to emergency medical transport. These missions have the potential to ease ground congestion and reduce greenhouse gas emissions in cities.

Operating in an urban environment poses challenges to air vehicles that are distinct from traditional air traffic management. Mainly, drones will need to avoid both dynamic (other drones) and static (buildings and city infrastructure) obstacles during flight. Additionally, the expected densities will be orders of magnitude larger than what is currently seen in conventional airspace.

However, this thesis limits the analysis to constrained airspace, where drones operate in urban areas between tall buildings and/or other infrastructure. This means that drones are restricted to fly along a constrained network that is above the existing street network or any other pre-defined network with a fixed route topology. In constrained airspace, drones can no longer fly directly to their destination and have points of convergence at the intersections of the network.

This thesis focuses on addressing challenges and risks of high-density air operations in constrained urban environments via two research goals. Thesis goal 1 analyses how airspace designs and rules affect the safety and efficiency of the urban airspace at varying traffic density. Thesis goal 2 develops and evaluates a method for analysing the operational feasibility of urban air missions considering local wind conditions.

Traffic flows are highly dynamic. Traffic densities vary locally throughout the day, indicating potential for idle descents outside of night hours. Alternatively, fixed-FPA descents can be flown with lower uncertainty. This thesis presents the development of novel per-flight criteria for executing idle and fixed-FPA descents from cruise to the Initial Approach Fix (IAF), using associated trajectory uncertainties. The influence of the criteria on the number of successful idle descents is assessed throughout a 24-hour operation. Trajectory uncertainty models were identified from simulations under various wind conditions. Using conflict probabilities, nominal aircraft spacing, and IAF arrival times, descent criteria sets were developed. The number of successful idle descents was evaluated for various reference scenarios with execution restrictions in time frames, developed criteria sets, and trajectory uncertainties. The results show conditional inverse relationships between the trajectory uncertainty, reference scenario and criteria strictness, and the number of successful idle descents. Scenarios with stricter time restrictions contain fewer successful idle descents. This also holds for stricter criteria, provided that aircraft spacing does not conceal the effect of the different criteria. Similarly, higher trajectory uncertainty reduces the number of successful idle descents, provided that, additionally, the criteria set was not too strict to conceal the effect. At least 50% of the aircraft descending outside of peak hours could complete an idle descent, regardless of scenario, criteria set, and uncertainty set. Including peak hours, this changes to 40% of all descending aircraft. The difference between high and low uncertainty remained below 2% of all flights for all explicitly developed criteria and scenarios. The theoretical maximum is found when all aircraft fly idle descents in the allocated time frames. This is 76% outside peak hours and 68% overall. This research provides a foundation for assigning idle descents and demonstrates their potential by allowing them outside night hours.

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…

This thesis investigates future air traffic growth projections for an en-route environment and its impact on airspace complexity. The study compares Free Route Airspace, FRA, with the traditional ATS Routes Network, assessing airspace efficiency through various complexity metrics, including flight interactions, air traffic controller workload, and traffic patterns. These complexity metrics present the dependent variables of the study. The research builds a simulation model where the independent variables are the size of airspace, the type of demand, and the operational environment. With help of BlueSky ATM Simulator, the simulation model shows how independent variables can affect the complexity metrics. Additionally to this, the study evaluates the environmental impact by measuring CO2 emissions, which are found to be significantly lower under the FRA due to more direct routing. Also, the findings of this research suggest that FRA offers more efficient traffic distribution, particularly in larger airspace areas. In smaller airspace areas, the controller workload increases due to less predictable flight paths and the more flight interactions under Free Route airspace environment. The study concludes that FRA is advantageous for larger airspace areas, enhancing efficiency and sustainability, but it also introduces challenges in managing air traffic, particularly in smaller or highly concentrated airspace environments.

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 usage of drones in urban environments is expected to grow rapidly in the coming decades. To ensure the safe operations of drones, conflict detection and resolution are vital. Currently, a lot of research has gone into state-based CD&R, which has proven effective in unconstrained airspace but suffers from a large number of false positive conflicts in constrained airspace. The use of intent in constrained CD&R has the potential to reduce the number of false positive conflicts and improve the safety of drone operations significantly. In this paper, an intent-based detection and resolution method for orthogonal constrained very low-level urban airspace is presented and evaluated against a state-based method. The intent-based method calculates the future position along the trajectory at a time interval of 3 seconds for each aircraft, and conflicts are then detected by comparing these positions. The conflicts are solved utilizing a rule-based algorithm. The results show that the intent-based method has a much lower false positive rate for all traffic densities, as well as a higher average detection time before conflict for larger look-ahead times compared to the state-based method. The resolution of the state-based method, however, shows better performance with fewer losses of separation occurrences. With improvements, the intent-based method's low false positive rate, combined with the use of a larger look-ahead time, allows conflicts to be detected more reliably and earlier than the state-based method, thereby facilitating earlier conflict resolution and enhancing safety.

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.

Event cameras are novel bio-inspired sensors that capture motion dynamics with much higher temporal resolution than traditional cameras, since pixels react asynchronously to brightness changes. They are therefore better suited for tasks involving motion such as motion segmentation. However, training event-based networks still represents a difficult challenge, as obtaining ground truth is very expensive and error-prone. In this article, we introduce EV-LayerSegNet, the first self-supervised CNN for event-based motion segmentation. Inspired by a layered representation of the scene dynamics, we show that it is possible to learn affine optical flow and segmentation masks separately, and use them to deblur the input events. The deblurring quality is then measured and used as self-supervised learning loss.

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.

Testing novel concepts in the allocation and use of aircraft is commonly done in air traffic simulators. Current day simulators rely on proprietary models and often propose questionable solutions. This paper uses BlueSky as simulator, in cooperation with performance models OpenAP and BADA. BlueSky aims to provide the user with full freedom to simulate any scenario. Improving BlueSky and adding functionality further aids in this goal. By expanding logic of the simulator, the user should attain more functionality without compromising the user-friendliness of BlueSky. This paper treats the implementation of thrust setting and flight path angle in BlueSky. This enables continuous descent operations to become part of the possibilities within BlueSky. The challenge was to prepare a set of ADS-B data using Fuzzy logic to validate the new model. Its robustness was then be demonstrated using a large number of flights.

In an effort to increase the operational efficiency in the North Atlantic region, the benefits of a decentralized, wind-optimal routing structure are researched. Such a routing structure allows for direct routing by optimizing trajectories on the individual level. Implementing a tactical conflict detection and resolution method eliminates the capacity limits imposed by air traffic control by shifting control to the flight deck. The benefits in terms of safety, capacity, and efficiency are assessed by comparing this routing structure to the current routing structure by simulating a year of real flight data. Furthermore, it is researched if such a routing structure remains viable for forecasted traffic levels by creating a future direct routing scenario with the use of dummy flights. Trajectory optimization is performed for the part of the trajectory above 10,000 ft in a decoupled manner with the ordered upwind algorithm for the horizontal domain and the base of aircraft data performance model for the vertical domain. Conflicts are solved on the tactical level with the modified voltage potential method in the horizontal domain. On average, a 5.1% fuel reduction, or 1.9% time reduction, is established for the new routing structure. A total of 18 loss of separations with an intrusion severity above 1% occur, of which the most severe intrusion still assures 734 ft vertical and/or 3.67 NM horizontal separation. The routing structure appears to be robust for future traffic levels as the airspace density scales linearly with the amount of aircraft and the conflict to loss of separation ratio remains constant with only three loss of separations slightly exceeding the 1% limit.

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.

This MSc. thesis presents the extensive research conducted on designing neural networks capable of generating reactionary dance motions. The research was conducted in collaboration with Stichting Another kind of Blue (AKOB) and CompactCopters UG (CC). It is set within the industry context of the ’AI-man’ project, developed by AKOB and CC; a novel dance show intended to create a truly interactive duet between man and machine. It is the follow-up to the successful ’Airman’ project; a dance show utilizing a swarm of 12 drones to perform with a human dancer on stage. Airman enabled the drone swarm to represent a human figure and copy the improvised dance motions of the human dancer, recorded via a motion capture (MoCap) system in real-time. The AI-man project aims to take this to the next level, by placing an AI motion controller in between the live MoCap recording and the desired humanoid pose passed to the drone swarm. By doing so, the system should be responsive to the human dancer’s motion and generate reactionary dance motions to created a shared improvised duet. Long-term motion generation is a very challenging problem and currently actively researched by various AI researchers. For reference, many researchers consider the prediction or generation of motion longer than one second, or even just 500 ms, ’long-term’ [Ghosh et al., 2018; Gui et al., 2018a; Tang et al., 2017]. In contrast to this, the interactive segment in the Airman show was about one minute long. During the initial literature study, no comparable research was found attempting to generate motion in reaction to full-body human input. To facilitate the research, a custom MoCap dataset was acquired containing over 9 hours of improvised dance motions between two dancers. In comparison to current publicly available datasets, the acquired dataset appears to be second longest MoCap dataset to date.To achieve the desired objective, two models were created:1) An initial regression model, successfully mimicking the human motion in the training dataset, but very unstable in a real-time environment.2) A generative model, intended to generate a variety of reactionary motions in real-time, in reaction to an arbitrary motion input. For the generative model, a novel neural network architecture, for the generation of long-term reactionary motions, is proposed: The ’Differential Recurrent Attention GAN’ (DRA-GAN). Utilizing the training methodology of ’Generative Adversarial Networks’ (GANs) [Goodfellow et al., 2014], in combination with design elements from ’Recurrent Neural Networks’ (RNNs) [Recurrent units: LSTM [Hochreiter and Schmidhuber, 1997] & GRU [Cho et al., 2014]], attention mechanisms [Bahdanau et al., 2015], differential equations and ’Principal Component Analysis’ (PCA) [Jolliffe, 2002].The proposed model showcases promising training progression, but has so far failed to generate true longterm output. This is because the training halts in a common failure mode for GANs; ’mode collapse’ [Liu and Tuzel, 2016]. While the model has so far not succeeded in generating the desired results, it cannot be concluded that the model is unable to generate the desired results. This is because, the full potential of the model has not yet been explored, e.g. by means of ’Bayesian Hyperparameter Optimization’ (BHPO).The framework to achieve BHPO was developed, but the extremely long training times (≈ 2 weeks), on the limited hardware available, have prevented the evaluation of a sufficient number of model variations. To facilitate further research, an extensive list of methodologies was compiled that could potentially resolve the current problems of the model.

The traffic density of small aerial vehicles operating within urban environments is expected to increase significantly in the near future. This urban environment is highly constrained due to being limited to the low-altitude airspace directly above the existing road network. To increase understanding of the factors influencing the capacity of urban airspace, multiple empirical studies have been performed using fast-time simulations. However, the empirical nature of these simulations hampers the extrapolation of their results beyond the specific conditions that have been tested. At the same time, the emergent behaviour of aircraft in constrained urban airspace, such as queueing and local hotspots, yields the existing analytical models for general airspace invalid. In this paper, we derive and validate an analytical model approximating the relationship between the mean flow rate and mean density in a two-dimensional orthogonal grid network airspace, expressed in the so-called Macroscopic Fundamental Diagram. This analytical model is based on road transportation queueing theory and can explain the different interactions occurring in constrained urban airspace compared to general airspace. Notable findings show that the entire airspace can become unstable when the maximum capacity of a single intersection only is reached. Furthermore, the maximum airspace density is found to be unaffected by cruise speed. The results demonstrate how the derived analytical model for the Macroscopic Fundamental Diagram provides an increased understanding of the factors that influence capacity of constrained urban airspace, thereby offering an effective tool for urban airspace design applications. Moreover, this model lays the groundwork for the derivation of more expanded models, including the altitude dimension and non-orthogonal or non-four-way intersections, that can further improve comprehension of constrained urban airspace.