To verify the aircraft position provided by Automatic Dependent Surveillance-Broadcast (ADS-B)
transponders, multilateration (MLAT) technique incorporates time difference of arrival (TDOA) measurements at multiple ground-based receivers to estimate the corresponding distances between those and the aircraft. This approach requires precise time synchronization among receivers that can not always be guaranteed. Alternatively, received signal strength (RSS) measurements can be utilized to derive these distances. In this paper, crowdsourced RSS measurements from 43 receivers were used to construct parameterized signal propagation models that capture the relationship between RSS and distance. The quality of these models
was evaluated by examination of model parameter and estimated distance errors in both 2D and 3D. The results show that at most 26.3% of available RSS measurements could be represented by the models given the cut-off criteria for model parameter errors. Moreover, the models with higher parameter errors demonstrated poor ability to capture RSS measurements at greater distances. The localization errors in MLAT with TDOA were compared to MLAT with RSS where the later resulted in more accurate position estimation in cases where the receiver clocks were not synchronized. However, MLAT with TDOA generally produced significantly more accurate position estimation given the reliable timestamps of signal arrival. The assessment of localization accuracy using crowdsourced data resulted in root mean square errors of 118.1 meters in MLAT with TDOA and 9858.6 meters in MLAT with RSS in 2D, representing the best results obtained.

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 emissions of the transport sector inside the EU-27 have risen by 33 % between 1990 and 2019. A modal shift from unsustainable transport towards more environmentally friendly transport modes can be taken as one solution to mitigate the overall emission of the transport sector. In this paper, multiple open-source models and databases are utilized to compare travel emissions and time of air travel and various ground transport options, including car, bus, and rail. Compared with previous research that relies on closed-sourced or hand-collected data to extract public ground transportation routes information, this paper utilizes the openly accessible General Transit Feed Specification(GTFS) database, facilitating the calculation at a large scale inside EU-27. 820 pairs of routes between popular 41 city centers inside EU-27 are selected for comparison. The results consistently demonstrate that air travel always produces higher emissions per passenger than rail and bus travel for all routes. Emissions from cars are significantly influenced by occupancy rates and the type of vehicle fuel. The emissions from a single person in a petrol/diesel car can exceed those from air travel. However, if four people travel in a hybrid electric or electric vehicle, the per-passenger emission can be similar to rail. Among all public transport, the rail is the most competitive one to replace air travel by offering passengers similar travel time and reducing emissions. The trade-off factor between emissions and time is also investigated on its effect on the passenger route choice decision. In addition, this paper offers insights into the development of emission models and provides recommendations for various stakeholders.

Air Traffic Control (ATC) is tasked with ensuring safe separation between aircraft in a given Controlled Traffic Region (CTR). To achieve this an Air Traffic Controller (ATCo) verbally gives clearances using over the air communication. These clearances are kept track of by the ATCo using so-called ‘flight-strips’, which in modern systems are often digital. The allocation of an ATCo’s time is an important factor in the achievable traffic density within a CTA, which makes ATC an interesting domain to use Automatic Speech Recognition (ASR) models to allow a computer system to ‘listen in’ to the conversation of the ATCo. Although previous research has been done to create such models, few of these result in open available models or domain specific corpora for the creation of such a model. This study will therefore use two open in-domain and one out-of-domain corpora to create such a model and in this process identify domain specific challenges and how these challenges can, in certain cases, be mitigated.

Passenger transportation in Europe is often duplicated using modes of transportation which are environmentally inefficient. Quantifying the carbon dioxide emission inefficiencies of flights versus transit is beneficial to understand the potential savings of a modal shift. In this paper, we analyze the emissions in Europe from multi-stop flights using flight data from March 2019. The excess emissions are quantified by comparing each multi-stop flight with an intermodal journey that does not exceed 60 minutes of extra travel time. We find that on average, transfer passengers using intermodality can reduce their journey’s total
(segment) well-to-wheel and life-cycle assessment emissions by 33% (80%) and 30% (72%), respectively. 840 thousand (19 % of total) transfer passengers starting or ending their journey in Europe can skip the feeder flight while saving an average of 28 minutes of door-to-door travel time. For air travellers taking intra-European multi-stop flights, 157 thousand transfer passengers (10% of the total) do not have to even enter an airport. Further insights regarding the European mobility vision are made, with recommendations for various stakeholders.

The application of automatic speech recognition in the air traffic control domain has been researched extensively. However, its primary application remains in the training and simulation of air traffic controllers. This is due to the insufficient performance of automatic speech recognition in specific environments, such as air traffic control, where strong performance and safety requirements are paramount. This study demonstrates how a large-scale, weakly supervised automatic speech recognition model, Whisper, could meet these performance requirements and establish a new approach to air traffic control communication. Fine-tuning Whisper in the air traffic control domain resulted in a word error rate of 13.5% on the ATCO2 dataset and 1.17% on the ATCOSIM dataset. Furthermore, the study reveals that fine-tuning with region-specific data can enhance performance by up to 60% in real-world scenarios.

Wind estimation plays an important role in many aspects of our world, both for nowcasting and forecasting. This is also true for aircraft flight routes; where wind field knowledge can contribute to flight planning and safety. Aircraft can relay flight information back to ground-based receivers, from which local wind measurements can be derived. Such measurements can be used in interpolation or a meteo-particle model to produce estimates elsewhere; yet those means do not take into account wind dynamics. A physically inspired neural network approach is applied in this work. Flight paths are derived from the aircraft measurements, and used to simulate training data from ECMWF ERA5 reanalysis data. Next, actual aircraft measurements serve as input, with the goal of predicting an entire wind field over the Netherlands and surrounding areas at cruise altitudes. The network can be leveraged by introducing physical losses, which are found to smooth the predicted flows. The network is able to predict flow fields on both simulated and real measurements, given sufficient input data. It improves over the existing methods of relying on 6-hour forecasts or using the meteo-particle model. Magnitude error of wind is reduced to 2.85 m/s, coming from 3.88 m/s for 6-hour GFS forecasts and 4.78 m/s for the Meteo-Particle model. Directional, physically inspired networks have an error of 11.2 degrees, compared to 14.4 and 17.3 degrees for 6-hour GFS forecasts and Meteo-Particle model, respectively. However, both error metrics fluctuate significantly depending on whether flow is uniform or of divergent nature at a particular day. The network is fit for nowcasting and future work can be done for longer-term forecasting.

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).  

The consumer drone sector is expected to grow rapidly in the coming decades. In Europe alone, it is predicted that in excess of seven million such machines will be flying by 2050. This poses a risk of conflict in dense airspaces, with both aircraft and other drones. Such a growing market provides a need to make drones visible to ATC and other airspace users. While several passive surveillance methods exist, such as primary drone radar, a cooperative surveillance system would provide more data to airspace users and other drones to allow for features such as automatic separation. An Automatic Dependent Surveillance system concept is presented in this paper, allowing the drone to broadcast information about itself without external input. This is akin to ADS-B, from which the system inherits its format for the time being.
The study's main contents are threefold. The first consists of recommendations made on the basis of literature. Then, a simulation approach to examine system capacity and related constraints through a sensitivity study is done. Finally, a hardware proof-of-concept, consisting of inexpensive and simple off-the-shelf components is built and tested.
Overall, the paper demonstrates that such a system is indeed feasible. Through the literature, it was found that direct integration of the system with current ADS-B on the 1090 MHz frequency is possible, but may cause performance degradation for existing aircraft. Therefore, the carrier frequency and code allocation are changed. The simulation and capacity study shows that the system works in high-density scenarios (in excess of one drone per square kilometer), but will require additional work on hardware, format and modulation techniques to enable this.
Finally, the hardware demonstrator shows that an inexpensive COTS implementation with a range of approximately 200 meters is possible, on hardware drawing less than five Watts of power.

Urbanization is a worldwide trend that drives an immense increase in air traffic demand. The worldwide aviation network makes global business possible which generates economic growth, creates jobs and facilitates international tourism and trade. Many of the world's transport hubs like The Greater London Area have allocated this demand over various airports, which forms a multi-airport region (MAR). Airport expansion plans and infrastructural investments in multi-airport regions, therefore, depend not only on the total level of air traffic growth but on its allocation. Therefore, the goal of this research is to develop an analysis framework for the market dynamics driving airport activity levels, focusing on multi-airport regions, and analyse how this provides a base for strategic decisions in the region. This goal was split into two sub-goals. First, understand the evolution of airport’s market shares based on the allocation of air traffic passengers amongst airports in a chosen MAR. Second, understand the market dynamics of passenger integrated transport systems in a MAR. Forecasting its underlying determinants can provide estimates for the future transport system in a MAR, which accommodate and facilitate smooth future operations of used logistical components. Greater London was chosen as a multi-airport region.

Part one consists of a quantitative model that can compute annual air traffic demand for a multi-airport region, and the allocation of this demand over airports within, for the period 2010 - 2050. Aggregate air traffic demand is based on UK's GDP projections and the allocation model was based on the relative performance of airports in terms of their accessibility, airfares and connectivity. A multivariate regression model was applied correlating historical (2010 - 2019) airport performance to their respective market shares. The results show regional air traffic demand is likely to grow 82\% over the next 30 years. Based on 60 observations, the model can predict its market shares with an R-squared of 0,952.

Part two progresses the quantitative model to generate a forecasting framework for the next 30 years for various important aviation-related variables in the region. This concludes that for London Gatwick, City and Stansted airport the projected growth in air traffic demand will not form any capacity problems, but for London Heathrow, Luton and Southend airport, it will. A major factor influencing this is the rising environmental awareness and increasing costs for carbon emission abatement. London Heathrow Airport, being UK's most important airport, together with the UK government has identified several developments to facilitate the expected growth in air traffic demand.

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.

The aviation industry faces the challenge of reducing its climate eects. This paper aims to determine whether optimizing flight trajectories can reduce the environmental impact of aircraft during the climb and descent phases. It addresses the research gap in sustainable flight trajectory optimization by combining state-of-the-art research on optimization techniques with research on aviation’s climate eects. A genetic algorithm is created for the climb and descent phases to minimize the impact of gaseous emissions; the objective function entails the climate and air quality costs of the emission of carbon dioxide, carbon monoxide, nitrogen oxides, sulfur oxides, and water vapor. OpenAP is used to evaluate the emissions of aircraft. The optimization includes variable mass and speed, wind, departure and arrival procedures, and airspace constraints. The optimization considers both the vertical and horizontal flight paths simultaneously. The model was tested for more than 22,000 flights in June 2018 at Amsterdam Schiphol Airport. It has been found that an average reduction in environmental costs of 8.6% and 18.6% is achievable for departing and arriving flights, respectively. The reduction depends on the aircraft type, weather conditions, and whether the aircraft departs or arrives. The open-source optimization model can be used for further research, for instance, evaluating the reduction possibilities at dierent locations. The research shows that the aviation industry can reduce its environmental impact considerably by optimizing departing and arriving trac trajectories.

Accurate 4D trajectory predictions are required for the implementation of Trajectory Based Operations. In addition, decentralized, free routing can make medium- to long-term flight trajectories more difficult to predict. Novel trajectory prediction techniques are needed, independent of waypoint-to-waypoint navigation and air traffic control operator behaviour. This research aims to improve the accuracy of medium- to long-term 4D flight trajectory predictions by incorporating a model that encompasses the dynamics of the air traffic situation. Data-driven techniques are well-suited to trajectory prediction purposes as high-fidelity air traffic and environmental data are widely available. A statistical analysis is first conducted to select the most suitable air traffic dynamics features for trajectory prediction purposes. The selected air traffic dynamics features are then translated to a spatiotemporal map. This paper proposes a composite, deep neural network to predict individual trajectories, merging a LSTM network with a 2D Convolutional LSTM based network.

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.

Automatic Dependent Surveillance – Broadcast (ADS-B) is an operational enhancement as part of next-generation air transportation systems in Air Traffic Control. It enables aircraft and airport vehicles to periodically broadcast the information from their on-board equipment, like their identification, GPS location, velocity, and intent. Compared to classical radar surveillance, the service implementation has increased the renewal time, reduced costs, and increased safety and accuracy already. Nowadays, Mode S 1090 Extended Squitter is the most predominant adopted technology ADS-B service implementation.
However, the ATC system has not been developed with security in mind and is vulnerable to a number of different radio frequency attacks by malicious parties. ADS-B is planned for long-term use but lacks the minimal and necessary inherent security mechanisms.
This study suggests a possible and cost-effective solution that improves the security and integrity of raw ADS-B signals by designing a tool which can verify and validate the low-level signal. In this study, in order to mitigate the threat of maliciously injected signals, a method is proposed where two variables of direction of arrival are independently determined using a multi-channel coherent receiver. First, a calculated angle using signal decoding and trigonometry and secondly, an estimated angle using phase relationships and spatial correlation. Finally, an integrity verification method has been proposed and successfully applied.

Aviation has a negative impact on the environment. As the number of flights is expected to increase, the total environmental impact of aviation will continue to worsen. New solutions are required to mitigate these effects. Aircraft Trajectory Optimization For Environmental Impact (TOFEI) can help reduce the impact of aircraft pollutants while also facilitating an increase in the number of flights. Whereas previous studies have shown that TOFEI can be effective in mitigating the footprint of aviation, their scope is limited. An applicable model should include wind, variable Mach number and an appropriate environmental impact metric. This paper presents the Trajectory Optimizer for Environmental Purposes (TOEP), which bridges the existing gap. The approach comprises two models, a genetic algorithm and a direct collocation model. The former finds climate-optimal trajectories using a meta-heuristic search technique, while the latter solves the problem using optimal control in CasADi. As direct collocation provides faster and more accurate results, the genetic algorithm is used to verify the direct collocation model. The algorithm uses the open aircraft performance model OpenAP. Results show that the environmental impact of flights could be reduced by as much as 6.6% when fully optimizing for this metric. The impact differs significantly between short haul flights and long haul flights, with 1.2% environmental cost reduction for the former and 2.7% for the latter. Pareto fronts are utilized to investigate the relationship between operating cost and the environmental cost.

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 fastgrowing Unmanned Aircraft scene accounts for an increasing number of new users who should have access to the airspace as any other full-fledged airspace user. Simultaneously, in order to maintain the currently achieved levels of performance in ATM, Unmanned Vehicles ought to be compliant with existingaviation regulations as well as with specially tailored requirements and standards. A subset of those requirements should address flight performance, which can be very diverse among Unmanned Aircraft. At the present time, there is a lack of research on this particular aspect of Unmanned Aircraft Systems integration in ATM, as solutions to other type of technological and regulatory issues are being pursued. By means of fasttime simulations of non-segregated RPAS operations in a given airspace, this project studies how Unmanned Operations impact ATM system performance in terms of Safety, Efficiency and Capacity and makes a suggestion on where the line could be drawn regarding admissible RPAS Flight Performance for TMA.