Current students

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.

 

 

Daan van der Veldt (BSc) – Improving TOBT (Target Off-Block Time) progress by using big data

Target Off-Block Time (TOBT) plays a crucial role in Airport Collaborative Decision Making (A-CDM) as it serves as a key parameter for coordinating and optimizing airport operations. This research focuses on the importance of accurate estimations of TOBT in airport operations management. It highlights the challenges in predicting TOBT due to various factors such as passenger arrival times and ground handling processes. The complexity of these factors necessitates advanced tools capable of dynamically forecasting TOBT to enhance operational efficiency. The research involves a data-driven analysis to understand the primary causes of delays in TOBT. Through this analysis, the aim is to identify patterns and correlations among different variables influencing the accuracy of TOBT estimates. The ultimate goal of the research is to provide insights that could facilitate the development of improved prediction models for TOBT, enabling airports to better plan and manage turnaround processes. Enhanced accuracy in TOBT estimations can lead to smoother flight flow, reduced delays, and improved operational efficiency at airports. This research contributes to further optimizing airport operations and reducing the impact of delays on both airlines and passengers.
Reducing uncertainty for Flow Management of arriving traffic at Schiphol before departure.

 

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.

 

Lars Dijkstra (MSc) – Ground handling planning conformance prediction

Predicting whether, and if so, the extent to which, aircraft ground handling is delayed has become increasingly important in the last decade as the aviation industry aims to improve the punctuality of its flights. Timely identification of aircraft ground handling delays allows the operational partners to update their schedules and reallocate their resources. While obtaining an accurate prediction is important, understanding how a prediction comes about is at minimum equally important. After all, this yields insights into the complex and stochastic aircraft ground handling process system, consisting of many sequential and parallel activities such as fuelling, (de)boarding and baggage (un)loading, and allows the operational partners to establish mitigation and/or contingency measures using knowledge extracted from the model. This research delves into the prediction of scheduled ground handling end time adherence at intervals during an aircraft turnaround at Amsterdam Airport Schiphol (AAS). To accomplish this, the processes and variables at play in the aircraft ground handling process are first identified and assessed. Subsequently, the aircraft ground handling process is modelled using interpretable machine learning.

Alexandru Măgdălinoiu (MSc) – Supporting executive inbound flight sequencing: improving exit constraints and EAT adherence

As the number of flights climbs to and surpasses pre-Covid levels, airspace capacity struggles to keep up with the continuously rising demand. This, alongside decreased flexibility in usage of the current airspace by controllers due to restrictions posed to reduce noise pollution and emissions, leads to the need to optimize flown routes and facilitate the handover between adjacent controlled areas. In the case of Schiphol-inbound flights this is translated into the aircraft sequencing and arrival metering process, which aims to maximize throughput given the limited landing capacity of existing runways. The goal of this research is developing a visual interface which aids Area Control Centre controllers in devising control strategies to follow the Expected Approach Time (EAT) computed by the Arrival Manager (AMAN) more closely and streamline the handover process to the Terminal Manoeuvring Area, while encouraging proactive control choices as an initial step towards Trajectory-Based Operations (TBO) and avoiding increasing the resulting cognitive workload.