Current students

Soraya van Beek (MSc)Improving probabilities of poor visibility and ceiling


In order to optimize the current Schiphol operations, an accurate weather forecast is of great importance. The Royal Netherlands Meteorological Institute (KNMI) provides a probabilistic weather forecast for Low Visibility Procedures (LVPs) to Schiphol, which is computed using deterministic weather models. If these LVPs occur due to low visibility or low ceiling, adjusted capacity and flow restriction are needed within airport operations. Inaccurate forecasting can lead to last-minute restrictions or restrictions when not needed. This research will assess the performance of the probabilistic forecast of visibility and ceiling at Schiphol airport. This will be done for the performance of three different weather models: HIRLAM, HARMONIE and ECMWF. Causes for inaccurate forecasting will be identified and suggestions for improvement of the probabilistic weather forecast will be the end result of this research.


Rebekka van der Grift (MSc)Using NOMOS measurements to improve aircraft noise models

The impact of the aircraft industry on the environment becomes more evident every day. Especially for local communities around the airport, the noise nuisance is an important factor which puts a strain on the capacity of Schiphol mainport. This capacity is based on the noise levels around airports, which are calculated with noise models based on key input parameters. The accuracy of these models is thus of great importance for the Schiphol mainport and the local communities. This research aims to develop a dynamic noise model based on real world aircraft noise measurements taken by NOMOS. The measurements will be used to calibrate certain input parameters to minimise any differences between model and measurements. This method helps to keep the model up to date and validated. Using measurements instead of standard input parameters is expected to increase the accuracy of the model, but also increase the trust of local communities in noise modelling.


Gijs Bekkers (BSc)Improving Operational Plan Preparation for Amsterdam Airport Schiphol

Stakeholder preparation for future operation is currently carried out mostly individually, with limited and mostly untimely access to relevant information. Through identifying and mapping plan development for each involved actor, it is possible to find commonalities and moments of information exchange. Data that could be of relevance for others is often kept private, due to lack of insight in handling by others and lack of knowledge on collective benefits of information-sharing. Using feedback from the Airport’s Operation Centre (APOC) on their Airport Operations Plan (AOP), and from LVNL on their OPS plan, together with expert recommendations, poses improvements for desired information sharing within stakeholders. Optimizing the plan-establishment processes and collectively arranging operational preparation yields benefits for all involved stakeholders. To finally ensure proper execution, data-exchange and arrangements should be constantly monitored using chosen performance indicators.


Max Aalberse (MSc)Optimizing the distribution of aircraft over the IAF

Around Schiphol and many other airports the amount of movements allowed is constrained due to the considerable noise pollution from aircraft. For a large part the noise pollution is created by arriving aircraft that are in between the IAF and the RWY. During this period the aircraft are in so called transition. These transition routes are usually already optimized to reduce the noise disturbance to surrounding residents, but due to the positioning of the runways this is not always possible. A different distribution of the aircraft over the IAF could result in less noise disturbance for surrounding residents, but would also increase flight times and fuel usage and therefore an increase in other emissions such as CO2. The goal of this research is to create a parameterized model that optimally distributes the aircraft over the IAF based on a quantitative trade-off between noise disturbance and CO2 emissions. Resulting in a model that is potentially able to reduce noise disturbance around airports while keeping the increase in environmental impact at a minimum.


Edzer Oosterhof (MSc)Analysis and Optimization of Air Traffic Bunching for the Area Control Center

With the current growth in air traffic and the resulting developments in terms of environmental issues and noise abatement, the pressure on the Area Control Center (ACC) is growing. On the one hand the Terminal Control Area (TMA) requires arriving traffic to be handed over accurately sequenced and merged, and on the other hand the ACC tries to minimize the miles flown in its sector. At the tactical level, there are Air Traffic Flow Management (ATFM) measures in place for traffic within Europe. However, no such tactical measures exist for traffic from the North Atlantic Tracks, increasing the probability that bunching occurs in the ACC during peak loads. By tactically predicting bunching in the sector and at the Initial Approach Fixes, a concept for debunching should be devised that focusses on airborne delay consumption and sequencing of traffic in the Upper control Areas (UTA) before it enters the Control Area (ACC), decreasing the pressure on the ACC.


Stephanie Wiechers (MSc) – Visual Interface to Support Improved EAT Adherence at IAF when Holding

As Schiphol is one of the busiest airport in the world, with tight flight schedules and urban areas that lie under arrival routes, adherence to the time planning is very important. When extreme weather conditions cause delays over the entire arriving fleet, holding stacks are installed at the three Initial Approach Fixes (IAFs) around Schiphol. In the current operational environment, little support is offered to the holding stack controller (ACC) to gain an overview of the effects of speed and wind on the turn times and difference between inbound and outbound leg velocity. With increased support, the controller will be able to make decisions based on representative information and with that, deliver inbound aircraft to Approach (APP). The (expected) resulting increased EAT adherence should lead to more orderly traffic in the TMA, improving capacity and workload.


Janjaap Wijnker (BSc)Evaluating the accuracy of information provided by the (D-1) OPS plan

In early 2020 LVNL implemented the OPS plan with the objective to improve the alignment of traffic demand with available capacity. This can be achieved by improving the predictability of the operations and make this transparent and accessible for the stake-holders. Every day the PRE-TACT unit develops an OPS plan for the following operational day. The plan contains two types of information, external factors that might impact the capacity, and recommendations for the most optimal operations. The recommendations are based on the external factors, for example the predicted traffic demand and weather forecast. These two factors contribute to the configuration of runways, and the runway selection determines the required capacity. In order to for LVNL to improve possible deficiencies of the OPS plan, the aim of this research study is to evaluate the accuracy and precision of the predicted traffic demand. The analysis focusses on the difference between predicted and actual traffic demand on a 20 minute resolution. In addition, the implications of factors contributing to the proposed runway configuration will be assessed since the use of different runways could have a significant impact on the capacity.


Christophe Vakaet (MSc) – Taxi Time Prediction with Classical and Auto Machine Learning at Schiphol Airport

Ground control uses the Departure Sequence Planner (DSP) to optimally plan departures within the operational constraints. The DSP uses an estimated Variable Taxi Time (VTT) to calculate an aircraft’s Target Take-Off Time (TTOT). If the VTT is underestimated flights will not make the determined TTOT, while an overestimation requires the air traffic controller to tactically hold an aircraft. These consequences result in delays, capacity losses, additional workload, and uncertainty. This uncertainty inhibits further operational optimizations. The VTT is currently predicted based on the average taxi times for different gate-runway combinations, wake turbulence categories, deicing procedure, and simplified runway configuration. The goal of this project is to improve VTT predictions by employing machine learning techniques and additional data sources such as traffic density, weather, aircraft type, and more.