Current students

Amine Nari (MSc) – Modeling Delay Propagation for LVNL’s Decision Support Tool (DST)

Air traffic flow management depends on accurate demand prediction, yet a major source of error comes from knock-on delays, which are primary delays that propagate to subsequent flights. These cascading effects create significant uncertainty in the European network, affecting both planning and operational decisions. This research focuses on understanding how delays develop and spread over time across flights and connections, using Schiphol Airport as the primary case study. By analysing large-scale delay data, the study aims to identify key patterns and mechanisms driving delay propagation.

The goal is to develop a model that represents how delays evolve throughout the network and can simulate their impact on traffic demand. The model will be integrated into LVNL’s Decision Support Tool (DST) to improve demand prediction and support operational decision making.

Albert Chou (MSc) – Analysis Noise Abatement Procedures Schiphol

Noise Abatement Procedures (NAPs) are specific procedures designed to reduce noise impact in the airport surroundings. At Schiphol Airport, three main NAPs are considered; Noise Abatement Departure Procedures (NADP), Continuous Descent Approaches (CDA), and reduced flap setting descents. These NAPs reduce noise through a reduction in aircraft thrust and drag. However, the implementation of these NAPs is sensitive to aircraft type, aircraft mass, weather, air traffic, and other factors. Therefore, analysing compliance of flight tracks with NAPs is challenging. This research aims to incorporate a data-driven approach to analyse the compliance of flight tracks with NAPs such as NADP, CDA, and reduced flap setting descents through Automatic Dependent Surveillance–Broadcast (ADS-B) and Automatic Dependent Surveillance–Contract (ADS-C) data. By improving NAP detection, this research supports better evaluation of NAP compliance and aircraft noise emissions at Schiphol.

Mitch Beintema (BSc) – Mapping Data Resources to improve Operational Decision Making

The Deep Turnaround (DT) integration project investigates how AI-driven video recognition of aircraft turnaround activities can be securely connected to LVNL’s technical infrastructure. Originally developed at Amsterdam Airport Schiphol, DT uses multi-camera monitoring and historical datasets to capture, interpret, and distribute real-time information on A-CDM milestones and turnaround events. By providing a verified stream of operational data, DT has the potential to improve sequencing, resource planning, and situational awareness across airport stakeholders. At present, however, DT functions largely in isolation, with limited sharing of milestone data between the Knowledge Development Center (KDC) partners. This research examines how interoperability can be achieved by addressing technological requirements, regulatory compliance frameworks, and organizational barriers. The ultimate goal is to enable collaborative decision-making and reduce fragmented situational awareness. In doing so, the study aims to unlock efficiency gains, support more sustainable operations, and contribute directly to SESAR objectives for a predictable, integrated European air traffic management network.

Coen Schinkel (BSc) – Artificial Intelligence Applications for Air Traffic Management

The integration of artificial intelligence (AI) in air traffic management (ATM) is expected to play a key role in improving efficiency, prediction, and safety. International programs such as SESAR and NextGen outline recurring domains where AI could add value, including trajectory prediction, conflict detection, speech recognition, anomaly detection, and human–machine teaming. For LVNL, however, it is not yet clear which of these applications are both relevant and feasible in daily operations.

My thesis focuses on identifying and evaluating AI applications that can realistically be adopted within the Dutch ATM context in the short term. The research begins by reviewing international frameworks and academic studies to determine which applications are sufficiently mature to be considered within the coming five years. These applications are then linked to LVNL’s organizational functions and assessed against three feasibility dimensions: operational fit, compliance with safety and cybersecurity requirements, and acceptance by air traffic controllers. The outcome of the study will be a prioritized set of AI opportunities that combine potential benefits with practical feasibility. In this way, the research provides LVNL with a concrete basis for deciding which AI applications to explore further, supporting both operational efficiency and the continued modernization of Dutch air traffic management.

Kewin Duda (BSc) – Identifying Formal and Informal Approach Design Criteria

Air traffic management is complex and requires tight coordination between procedures, controllers, and flight crews. Instrument approach procedures aim to ensure predictability and smooth transitions to landing, with stabilised approaches at their core—where aircraft follow a defined path at the correct speed and configuration. This reduces workload and supports efficient sequencing.

Despite these procedures, deviations still occur. These can be Non-Compliant Approaches (NCA), where procedural rules aren’t fully met, or Non-Stabilised Approaches (NSA), where trajectory or configuration can’t be maintained. NCAs often lead to NSAs, increasing workload and reducing predictability.
This study investigates whether the final approach routes to runways 18R and 18C at Schiphol allow aircraft to meet published requirements at the Final Approach Fix. Using historical trajectory data, it explores whether atypical approaches result from route design limitations or operational factors. The goal is to identify root causes and offer insights to improve safety and efficiency.

Yoari Karelsz (MSc) – Managing Idle Descent Trajectory Uncertainties at Schiphol

Idle descents are continuous descent operations flown at almost zero thrust. These descents are more fuel efficient and cause less noise compared to normal powered descents. However, air traffic control cannot intervene during such descents, or the additional efficiency will be lost. Change in wind, pilot inputs and other factors, normally compensated with thrust, cause these descents to be less predictable. Therefore, an additional spacing buffer of two till four minutes is required to safely fly idle descents. This has a large impact on the landing capacity of Schiphol, hence only nighttime operations incorporate idle descents when possible. This research aims to provide insight into the trajectory uncertainties of idle descents by analysing flight recorder data. Quantifying and reducing the trajectory uncertainties of idle descents can potentially reduce the required spacing buffer and help with flying more idle descents.

Niels Prins (MSc) – Trajectory-Based Operations in Dutch medium-altitude airspace under mixed ADS-C equipage conditions

Trajectory-Based Operations (TBO) aim to improve the predictability, efficiency, and safety of air traffic management through the use of intent-based trajectory information. With the growing availability of Automatic Dependent Surveillance–Contract (ADS-C) technology, TBO is becoming increasingly feasible. This thesis focuses on developing a trajectory management framework for the Dutch Flight Information Region (FIR) within the FL100–FL260 range, managed by Amsterdam Area Control Centre. Designed for mixed equipage conditions, it will accommodate both ADS-C-equipped and conventional aircraft, managing inbound and outbound traffic while integrating a conflict detection and resolution module. Operating on a time horizon of minutes to tens of minutes, the framework enables continuous trajectory planning and strategic conflict resolution, while retaining tactical intervention capability. Tested with real-world Dutch traffic scenarios, and potentially extended to lower altitude sectors, the framework represents a pragmatic, evolutionary step towards TBO, enhancing planning capabilities while preserving the flexibility required in current operations.

Julia Huigen (MSc) – Environmental performance of TBO implementation evolutions at Mainport Schiphol

The aviation industry contributes significantly to global greenhouse gas emissions, and its environmental impact is expected to grow as air traffic continues to increase. As a result, both Airlines and Air Navigation Service Providers (ANSP) are required to reduce emissions. Numerous studies have shown the benefits of Continuous Climb Operations (CCO) and Continuous Descent Operations (CDO) in reducing fuel consumption and emissions. However, these studies often focus on simplified situations and do not fully consider the real challenges of busy and complex airspace using real-world data. This research aims to provide practical insights into the potential of TBO in the Dutch airspace. This thesis will investigate how vertical flight performance can be improved by quantifying, visualising, and mitigating inefficiencies through the implementation of TBO, particularly in the climb and descent phases. Airline data will be utilised as a necessary input to understand real-world operations and identify areas where performance can be improved.

 

Eric van Pijlen (MSc) – Improving thrust and weight estimation for a Doc. 29 noise model by using ACMS-data to more accurately predict noise levels at Amsterdam Airport Schiphol

To regulate and mitigate noise impact of an airport on the surrounding communities, aircraft noise models are used to assess the impact. For these models, flights are modelled based on standard procedures of consecutive steps of speed and altitude changes and their corresponding delivered thrust. Under these procedures, however, lie a number of assumptions that could significantly alter the actual flown trajectory and thus noise pollution. Using real world data including data from the Aircraft Condition and Monitoring System (ACMS) and measurement data from the Noise Monitoring System (NOMOS), a comparison can be made between the estimated and real flight profiles.

 

 

Jens Bremer (MSc) – Improving the departure manager (DMAN) at Schiphol through PEGT Integration and optimization of the 10-minute bin mechanism

Schiphol’s current Departure Manager (DMAN) system, built around Target Off-Block Times (TOBT) and fixed 10-minute bins, offers operational stability but lacks the granularity needed to efficiently sequence departures in a high-density environment. This research investigates the integration of the Predicted End of Ground Handling Time (PEGT) into the DMAN. This new PEGT is a machine learning-based estimate developed by Schiphol Aviation Solutions’ Deep Turnaround project. PEGT leverages image-based machine learning algorithms to monitor over 70 unique turnaround events in real-time, generating predictive insights from over 150,000 historical turnarounds. These predictions enable early delay detection (up to 40 minutes in advance), supporting more precise TOBT estimates and improved gate and runway slot usage. Building on previous work and aligned with SESAR’s integrated DMAN-AMAN vision, this study also questions the effectiveness of rigid 10-minute departure bins. Using historical PEGT & TOBT data together with simulation models inspired by traffic flow constraints, this project evaluates dynamic bin sizing and delay damping mechanisms. The goal is to find a balance between operational stability and responsiveness in pre-departure sequencing. By combining turnaround predictions with more flexible pre-departure scheduling, this research aims to improve DMAN’s ability to manage departure flows efficiently, reduce last-minute gate conflicts, and enhance overall airport throughput at complex hub airports like Schiphol.

Lisa Blom (MSc) – Dynamic application of idle descents and off-idle geometric descents in the CTA

Many TBO techniques for Trajectory Management for arrivals involve either speed management by ATC or time management by the aircraft. However, in high density operations, both speed management and time management exhibit too many uncertainties, making them incompatible with high-density arrival operations. In previous research, the benefits of an alternative method of managing trajectories in a speed managed environment, have been demonstrated. In this alternative method, geometric descents are employed. Using these fixed angle descents, many uncertainties are eliminated. However, since such descents are flown off-idle, a different balance between capacity and flight efficiency is created. On the other hand, opportunities exist to allow aircraft to fly idle descents, when higher uncertainties can be permitted due to specific operational circumstances. This study will research the uncertainties associated with idle descents and off-idle geometric descents within the CTA related to EHAM. A model demonstrating the dynamic application of both descent techniques in a 24H operation will be developed based on determined criteria, and compared to static applications of the descent techniques.

 

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.

 

 

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.

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.