Current students

Thomas Groothoff (MSc) – Effects of a decision support tool for the planner operating in multiple airport regions

Currently the workload of the executive operating in sector 3 is relatively high compared to other sectors within the Dutch airspace. An explanation one could give for the relative high workload is the fact that the diversity of the traffic passing through the sector is high, the traffic consists out of inbound and outbound traffic for EHAM, regional traffic from EHRD and EHEH and finally transit flights passing over the sector. In order to make such sectors more manageable for the executive a decision support tool for the planner for decision support in such sectors will be developed. Due to the higher fidelity of information available in the future through ADS-C data, new possibilities with regards to planning become possible. The tool that will be developed will make use of this ADS-C data in order to provide new insights to the planner, which allow for better planning that might reduce the complexity of the airspace. Due to this reduced complexity of the airspace, the workload of the executive will be reduced.

 

Lilien Madi (MSc) – Flight performance

Despite regulatory efforts at harmonizing and enhancing ATM performance, ANSPs still utilize indicators based on extra distance and time flown, such as Horizontal Flight Efficiency (HFE) to periodically report on their performance. However, additional distance and time flown may not necessarily correlate with increased fuel consumption particularly if the flight operates under more favourable conditions for fuel burn, such as optimal wind conditions, speed, and altitude. In certain cases, there is a negative correlation between horizontal efficiency and total fuel efficiency. Nonetheless, fuel-based metrics are not enforced as their complexity remain a limiting factor in their implementation. Given these metrics offer a more accurate representation of a flight’s environmental efficiency, this study will focus on fuel-based performance indicators and will utilize an open-source aircraft performance model (OpenAP) to reconstruct historical and flight plan trajectories, generate reference optimal trajectories and calculate each flight’s fuel consumption. The discrepancy in the fuel consumption of this set of trajectories will allow the identification of strategic, tactical, horizontal and vertical efficiencies in LVNL’s airspace.

Suze Garstman (BSc) – Implementing FF-ICE release 1

The concept of FF-ICE is to reduce the limitations of the current flight plan 2012 in order to support the future environment as detailed in the global ATM operational concept. Which concept is to support future ATM operations. Through the EU implementing rule 118/2021 phase 1, a.o. the introduction and use of a new flight plan will be mandatory 2025, December 31st. To reach this goal it is necessary to investigate if and how these new flight plan alterations can and will be supported by LVNL and her stakeholders.  Today’s flight plan presents the ATCO and other stakeholders the initial intend of a flight. No modifications during the flight are possible. However, FF-ICE phase 1 is designed to manage this problem specially towards Trajectory Based Operations. This new flight plan will be introduced to provide richer route/trajectory descriptions to support the efficient use of air space worldwide. This research aims to identify the alterations already in place or needed within LVNL and her stakeholders to comply with EU implementing rule 118/2012.

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.

Ahmed Kubba (MSc) – Integration of Uncertainty Quantification in Extended Arrival Management and Long-Range Air Traffic Flow Management for Transatlantic Flights

The field of Air Traffic Management (ATM) is evolving to meet the growing complexities of air travel, yet traditional systems like Air Traffic Flow Management (ATFM) and Arrival Management (AMAN) still rely heavily on deterministic inputs. This reliance leads to inefficiencies, especially in long-haul operations such as transatlantic flights, where uncertainties in weather, demand, and capacity often disrupt planning. Despite recent advances in machine learning and delay prediction models, integrating uncertainty quantification into ATM systems remains underexplored, limiting their adaptability in dynamic environments. This research seeks to address these challenges by integrating uncertainty quantification into an Extended AMAN and LR-ATFM framework, with a focus on transatlantic operations. The goal is to develop a dynamic speed management system that adjusts flight speeds based on real-time uncertainty predictions. By enhancing predictability and optimizing sequencing, the approach aims to reduce fuel consumption, minimize delays, and improve overall efficiency. This is especially relevant as the aviation industry faces increasing pressures to manage growing air traffic sustainably and reduce carbon emissions.

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.

 

 

 

 

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.