Current students

Vincent van Dijk (BSc) – Enhancing predictability and efficiency in the aircraft towing process through improved real-time information sharing

In complex and dynamic environments, real-time data sharing is more important than ever. It’s no surprise that Eurocontrol’s Airport Collaborative Decision Making (ACDM) system, which focuses on improving the efficiency and resilience of airport operations by optimizing resource use and enhancing air traffic predictability through real-time data sharing, is yielding positive results. For both inbound and outbound flights, this system has minimized delays caused by miscommunication or a lack of transparency among stakeholders. However, when it comes to towing operations, which share many operational similarities with flight movements, the same improvements haven’t been realized. In towing operations, it’s still common for there to be poor information exchange between stakeholders, making the process unpredictable and inefficient. This research aims to improve the predictability and efficiency of aircraft towing through enhanced real-time information sharing, potentially reducing disruptions, congestion, and APU runtime, while optimizing tow truck use and overall operational performance.

Matthijs Slobbe (MSc) – ETA predictions based on accurate weather

The weather is something that impacts everyone on a daily basis and much research has been done to improve weather forecasting in the past. This is also the case in the aviation industry, using traditional data sources as well as aircraft measurements. Previous research in this domain has developed the Meteo-Particle (MP) model which constructs a wind field based on data collected by aircraft and UAVs. Research has also been done with new physically inspired machine-learning approaches to create wind fields. This research, with the ultimate goal being to reduce uncertainty in aircraft estimated time of arrival (ETA), intends to approach the problem of creating accurate 3D wind fields with a diffusion neural network, filling in the gaps where there is no aircraft data available. Previous models struggle with non-uniform wind fields, this new approach presents an opportunity for better reconstruction of the wind fields under these conditions.

Teun Vleming (MSc) – Effects of a decision support tool on merging ILS and EoR traffic in approach control

Established on RNP AR APCH (EoR) is a navigation technique built upon Required Navigation Performance Authorization Required approaches, which use self-monitoring capabilities to achieve a high navigation accuracy. This allows aircraft to be established on complex (curved) approach paths and be released from standard radar separation requirements, which brings benefits in terms of reduced level segments, more predictable ground tracks and reduced track miles. Since not all operators at Schiphol Airport are equipped for EoR, the air traffic controller will have to handle a mix of traffic. Evaluations from previous implementations of EoR highly recommend offering a support tool to the approach controller. This research focuses on designing and evaluating a Decision Support Tool (DST) to enable merging EoR traffic with vectored ILS traffic on final approach for a single runway. The design will follow principles from Ecological Interface Design to create an effective and accepted tool. A simulation will be used to evaluate the DST in terms of controller workload, traffic capacity and ability to robustly handle different traffic mixes.

 

Vera Buis (MSc) – improving fog forecasts for Amsterdam Airport Schiphol using machine learning algorithms

LVNL uses a Decision Support Tool (DST) to manage and mitigate delays up to four hours ahead. Accurate weather forecasts play a crucial role in accurately predicting the airport’s capacity. Specifically low visibility conditions due to fog severely limit the capacity due to the large separation between aircraft that is needed to ensure safe operations. Fog can arise and dissipate within a matter of minutes. Besides that, it can occur on very small spatial scales, as small as a couple hundred metres. Ordinary weather forecasting tools are often incapable of capturing this very small-scale and short-lived nature of fog. Using machine learning algorithms to forecast fog poses a promising solution to this issue. Using observations on the airfield, visibility forecasts can be made for multiple areas at the airport. A small-scale, accurate fog forecast greatly increases the capability of air traffic controllers to anticipate lower capacities, and therefore timely mitigate delays.

 

Jorn van Beek (MSc) – Evaluation of Arrival Manager Horizon Extension in a Trajectory Management context

The Arrival Manager (AMAN) system is used to provide regulation on aircraft entering the Terminal Maneuvering Area (TMA). New regulations require the AMAN to freeze the sequence further ahead than the current 14 minutes with the aim of reducing fuel burn and decreasing controller workload. The increased horizon combined with uncertainty on arriving aircraft cause these aircraft to “pop-up”. This causes sequence errors which increase workload and fuel burn. These effects are more pronounced when using the AMAN in a Trajectory Based Operation (TBO) environment. Previously, research on the horizon extension and pop-up impact and solutions has been performed, although in idealized scenarios without a realistic solution. This research aims to model the Arrival Manager in high fidelity to support decisions on the Arrival Manager design, and to develop and test realistic solutions to decrease the impact of the pop-up traffic when extending the AMAN horizon towards TBO operations.

 

Tex Ruskamp (MSc) – Reducing uncertainty for Flow Management of arriving traffic at Schiphol before departure

At LVNL a Decision Support System (DST) is used to support ACC Supervisors and Flow Managers (FMP) in their decisions to issue flow regulations to the Network Manager of Eurocontrol in Brussels. The horizon at which flow regulations are typically issued is three to four hours before arrival at Amsterdam Schiphol Airport. However, at this time horizon, a significant portion of the arriving traffic is still on the ground at the so-called out-stations. Previous research has shown that a significant portion of the uncertainty in the predicted traffic demand is originated in the pre-departure phase. This research aims to develop a machine learning model that makes an improved estimation of the Take Off Time.

 

 

Thijs Scheffers (MSc) – Effects of increased trajectory predictability by ATS Datalink on air traffic management operations in lower airspace

The latest generation of Air-to-Ground Datalink (AGDL), known as Air Traffic Services B2 (ATS B2) is now being introduced into European airspace. As mandated by the European Union (EU), effective from 31 December 2027, aircraft receiving their first airworthiness certification on or after this date must be capable of downlinking and processing ADS-C Extended Projected Profile (EPP) data, as part of ATS B2. An important element of this AGDL implementation is the availability of detailed trajectory information with flight intent. This application leads to improved predictability, as it allows for more accurate predictions of an aircraft’s intentions and destination. Increased predictability enables improvements in key areas, such as safety, flight efficiency, and environmental impact. The aim of this research is to determine the impact of this improved predictability on the design of air traffic control procedures in lower airspace around Schiphol Airport.

 

Sander Poelstra (MSc) – Optimizing taxiway maintenance planning using ground control workload limits

Currently, the taxiway maintenance planning at Amsterdam Airport Schiphol (AAS) is determined based on technical necessity, and often not based on the impact on ground operations. Including the impact on operations is then done at a later stage, resulting in maintenance projects being pushed through because it is not operationally feasible. An important operational effect that depends on maintenance planning is the impact on the workload of ground controllers. This workload should not become too high due to taxiway maintenance, otherwise safety and ground capacity at the airport will deteriorate. It is therefore essential to study the relationship between the closure of taxiways due to maintenance and the workload of ground controllers in order to test the feasibility of maintenance plans. In this thesis project, this relationship is studied and it is clarified when and for how long taxiways can be closed for maintenance at AAS such that the workload of ground controllers remains within acceptable limits.

Alexandru Măgdălinoiu (MSc) – Supporting executive inbound flight sequencing: improving Expected Approach Time adherence

An important bottleneck in managing inbound traffic capacity is the limited size of the Terminal Manoeuvring Area, which should not become crowded by aircraft arriving from the Control Area. This is done by arrival metering, a process entailing sending aircraft through the Initial Approach Fix (IAF) at pre-established Expected Approach Times (EAT). Currently, area controllers are required to deliver aircraft at the IAF at their EAT ± 120 seconds. Reducing this window in the near future would allow for less buffer times between aircraft to increase capacity, potentially allowing implementation of time-based separation at the IAF as an initial step towards trajectory based operations. This research aims to produce a visual decision support tool to aid the aircraft sequencing process and achieving EAT adherence at the IAF, that is compatible with the currently in use or upcoming systems used in the Amsterdam Area Control Center. To avoid significant changes to the modus operandi of controllers, the aim is to show the user the operational constraints and focus on expediting traffic. An experiment was conducted with eight professional controllers to compare the newly developed tool with a rendition of the current interface. The results are positive, indicating less deviation from the EATs in all participants and overall lower participant workload as a result of using the proposed decision support tool.