Alumni

The future concept for arrivals at Amsterdam Schiphol, as for many airports, is to progressively implement Continuous Descent Operations (CDO). For this, it is already known that a high degree of predictability of the arrival trajectories is needed. With new Air Ground Datalink (AGDL) technology emerging, specifically ADS-C, these possibilities are becoming within reach. However, it is unclear to which extent the integration of this AGDL provided information will enhance Trajectory Predictor performance. Moreover, the sensitivity of the managed arrival process to the predictability of the trajectories is unclear. Having a better insight in this dependency enables the further design of the technical concept by providing target performance levels. In turn, it also provides direction and input to the business case for equipage by airlines for trajectory sharing as well as ground system trajectory prediction performance. To establish a useful measurement for value added by improved predictability of the success rate, that is, the percentage of CDO’s that can be executed without ATC intervention, is envisioned.

The assertion and updating of the Target Off-Block Time, also known as TOBT, during the handling process of aircraft affects the capacity of an airport where airport collaborative decision making (A-CDM) is used, such as at Schiphol. The research question concerns an analysis that shows the effect of both how different parties deal with the TOBT proposition and updating and the influence of information sharing on Schiphol’s supply. The assignment includes a data analysis and the challenge of creating a model to show the effect on the supply of Schiphol.

Graduated: February 2024

At present, the primary stakeholders of the Knowledge and Development Centre (KDC),  who are Royal Schiphol Group (RSG), The Royal Dutch Airlines (KLM), and the Air Traffic Control the Netherlands (LVNL), individually prepare for the upcoming day’s operations. They rely on their respective sources of information for this process.

It’s evident that the lack of effective communication and not sharing information creates barriers to the airport’s efficient and safe operation. Current research aims to determine the pertinent and current information from partner organizations that is presently being utilized, as well as what needs to be shared. This is aimed to enhance the operational planning process for all stakeholders involved, with the aim of providing Schiphol APOC with a well-defined framework for data usage and management and improving the accuracy of traffic predictions and minimizing delays.“

Graduated: January 2024

The goal of this project is to investigate and analyze the operational challenges and requirements associated with the implementation of Tactical Demand Tailoring (TDT) in the European aviation industry. TDT is a concept that involves planned inflight re-routing to optimize air traffic flow, enhance efficiency, and respond to changing conditions. The assignment aims to investigate various aspects related to TDT, including its feasibility, the parties involved, legislative considerations, and the necessary information and communication systems.”

Graduated: January 2024

The concept of SWIM, System wide information Management is a system that covers a complete change in how information is managed along its full cycle across the whole European ATM system. SWIM is expected to be an important driver for the new updated standards, information and working methods. KLM flight dispatch is 24/7 operation to ensure KLM has a safe and efficient fleet operation. Flight dispatchers prepare the whole flight and with that ensures the crew has all the required information at all time. It has been recognized that global interoperability and standardization are essential to improve the aviation business. SWIM is a system that can contribute to the interoperability of flight dispatch and for this reason the research will be to investigate if there is any available information/data on SWIM that can improve the flight dispatch operation where possible.

Graduated: January 2024

Air traffic sector demand and capacity balancing is an important process to enable safe and efficient flight execution. In current operations, demand and capacity are determined based on schedules and flight plans. In reality, disruptions to flights create a different situation that may not have been anticipated by the Air Navigation Service Provider. This can ultimately cause unnecessary network regulations. This research aims to improve air traffic sector demand forecasting, by exploring machine learning-based trajectory prediction. In light of the Trajectory Based Operations concept that is developed within ATM research, a trajectory-based approach is taken to improve demand forecasts. Using available flight status messages from the Eurocontrol Network Manager, and actual recorded trajectories, a transformer neural network was built that could generatively predict flight trajectories. This model could accurately generate trajectories, outperforming the flight plan and other neural network approaches by a large margin. For demand prediction, the introduction of improved trajectories may provide only marginal improvements.

Graduated: November 2023

After a lull in traffic due to the COVID-19 pandemic, Schiphol and the surrounding airspace is again reaching peak capacity during its operations, leading to Network Manager (NM) regulations. When a departing flight is regulated its sequencing process by the Pre-Departure Sequencer is different from non-regulated flights, possibly leading to delays stacking and an even later Calculated Take-Off Time being issued by NM. These feedback effects are currently not well understood and some parameters in the process are not well explored in regards to finding an optimum, either local or global. In this research the A-CDM EHAM process and related systems will be modeled in order to reach new insights into the effects of varying communication time horizons and the effects of the varying data streams on the total departure capacity of Schiphol.

Graduated: November 2023

Traffic entering AMS-FIR usually presents itself at the FIR-entry points according to their filed flight plan.

AMS-FIR has a relatively high density and complexity of traffic. Therefore, rerouting arriving aircraft inside this airspace to make an approach to a runway, other than the one most conveniently oriented with respect to their FIR-entry point, is considered undesirable. This research will provide insight into a proposed operational tool which could help LVNL improve its operation. This tool will be referred to as Tactical Demand Tailoring (TDT) in this report. It would allow LVNL to redirect the flights arriving to Schiphol tactically, after their departure, but before these aircraft enters AMS-FIR. This extends the influence LVNL has over Schiphol arrivals to outside of its area of operations.

Graduated: June 2023

At Schiphol airport all ground traffic is handled by ground controllers, who are all working on the traffic within a specific geographical sector. There are four main sectors, but one controller can also handle multiple different sectors. This means that the number of active controller will fluctuate during the day. There is however little insight in what the workload of a specific sector will be within the nearby future. Next to this there is also little insight in how the workload of the Schiphol ground controllers effects the capacity of the taxi system as a whole. Since the taxi system capacity has a large influence on the capacity for Schiphol as a whole, more insight within the parameter of ground controller workload could be of significant use, as it can be used in deciding on what number of controller will need to be active.  This research will try to create a tool in which the upcoming workload can be estimated using the planned operational data.

Graduated: July 2023

Aircraft noise is a significant problem for communities surrounding airports. Accurate prediction models are needed to estimate noise levels from aircraft operations. In this research, the accuracy of the sonAIR aircraft noise model is evaluated in predicting noise levels around Schiphol airport by comparison to measurement data from NOMOS and the current best practice modelling approach Doc29. Results show a significant but consistent underestimation of noise levels by sonAIR, mainly due to a generalisation of emission models. This research contributes to better aircraft noise predictions, which will have implications ultimately leading to a better quality of life for communities affected by aircraft noise.

Graduated: May 2023