As a result of the current evolution in the ATM system, a fundamental shift in the ATC work domain is foreseen from ad-hoc tactical to more strategic, 4D (i.e., space and time) trajectory management. Both the SESAR programme in Europe and the NextGen programme in the US envision a central role for the human operator, to be aided by high-level automated decision support tools. In an attempt to prototype such support tools, a 4D trajectory management interface was previously designed and experimentally validated. To improve on the maturity of this concept, effects of wind and trajectory uncertainty were integrated into the interface in this present work. Using six professional controllers, the redesigned interface was evaluated in a realistic 4D inbound traffic peak scenario within the Dutch airspace, where small control spaces require a mixed tactical-strategic form of ATC. Results indicated that operators were able to successfully combine conflict and arrival time management in a highly complex traffic scenario despite the added display complexity, although further research will be needed to confirm these findings in a statistically relevant context.

This paper addresses the airline centred Arrival Sequencing and Scheduling problem aimed at the smart distribution of arrival delays, considering the explicit preferences from users. We consider the scenario in which actions are executed solely in the en-route phase with the available leeway present in the current ATM system. The arrival process at the destination centre alongside equity rules such as ”First-Come, First-Served” remain untouched. A Mixed-Integer Linear Programming approach is presented in order to evaluate the fleet-wide impact of speed changes by individual aircraft in order to come to a global (airline specific) optimum. The approach presented is evaluated using operational data in the form of a case study of a large European hub-style carrier. Case study results indicate the ability to decrease delay related cost by over 15% through the more efficient distribution of delay times between aircraft. Overall aircraft timeliness in the case study for both the controlled airline as well as competing airlines shows a slight improvement of several seconds of average delay per aircraft. In addition, a number of variations to the base model are presented, investigating a possible trade-off between model priorities.

The introduction of Trajectory Based Operations (TBO) is set to change the operation on all levels of Air Traffic Control (ATC), including aerodrome control. Here, adherence to a planned four-dimensional trajectory is important to achieve a stable operation. At the same time, this concept provides possibilities for support tools to be used in ATC, by making use of new data and information. This research presents the Take-off Timing Support Tool (TTST) for aerodrome control, and more specifically runway control, at Amsterdam Airport Schiphol. It is incorporated into the already existing Electronic Flight Strip System and features a time axis on which flight strips can be placed. By dragging departures along the time axis a planning and sequence can be constructed, while taking into account the provided solution space to prevent conflicts. An experiment conducted with the TTST showed that professional air traffic controllers used the information from the tool in their decision-making, leading to a decrease in conflict count, and it was demonstrated that a stable planning could be established. Therefore, the TTST is a suitable platform to support TBO in aerodrome control and assists air traffic controllers with time-based control. No significant difference in departure interval was present. The workload did increase moderately.

The introduction of Trajectory Based Operations (TBO) is set to change the operation on all levels of Air Traffic Control (ATC), including aerodrome control. Here, adherence to a planned four-dimensional trajectory is important to achieve a stable operation. At the same time, this concept provides possibilities for support tools to be used in ATC, by making use of new data and information. This research presents the Take-off Timing Support Tool (TTST) for aerodrome control, and more specifically runway control, at Amsterdam Airport Schiphol. It is incorporated into the already existing Electronic Flight Strip System and features a time axis on which flight strips can be placed. By dragging departures along the time axis a planning and sequence can be constructed, while taking into account the provided solution space to prevent conflicts. An experiment conducted with the TTST showed that professional air traffic controllers used the information from the tool in their decision-making, leading to a decrease in conflict count, and it was demonstrated that a stable planning could be established. Therefore, the TTST is a suitable platform to support TBO in aerodrome control and assists air traffic controllers with time-based control. No significant difference in departure interval was present. The workload did increase moderately.

On final approach, an Approach (APP) Air Traffic Controller (ATCo) is responsible for keeping sufficient separation between aircraft lining up on the ILS. The current industry standard is to separate these aircraft with a minimum distance, called Distance-Based Separation or DBS. European regulation requires all European airports to implement separation based on time (Time-Based Separation or TBS) before 2024. Due to this implementation, effectively changing the APP ATC task from a geometrical to a time-based problem, and because of further complications such as the European re-categorisation of aircraft types, experts fear that the theoretical gains attainable by using TBS will not be fully realised. In this research, a display tool concept to aid APP ATCos in realising the full potential of TBS, the Ideal Turn-In Point (ITIP) display is designed and evaluated with respect to the current state of the art. The ITIP display assists controllers in selecting optimal approach strategies starting from the moment aircraft enter the Terminal Control Area. The display aims to assist the operator by showing the possibilities and restrictions in the system rather than giving (restricting) advisories. In an initial proof-of-concept experiment, comparing the ITIP display to the current industry state of the art display, promising results were found; the ITIP display was shown to maintain safety and increase efficiency, whilst maintaining controller workload. The current industry state of the art display is a tool designed by the National Air Traffic Services, currently operational at London Heathrow Airport.

In the past decades, air traffic growth has resulted in increasingly complex situations. In order to guarantee safety and efficiency is maintained, new automation tools and interfaces must be developed. In collaboration with the Netherlands Air Navigation Service Provider, the Inbound Traffic Support System interface was developed. This display reveals the constraints of a merging task for an area controller within the South Sector of Amsterdam Airport Schiphol. The interface aims to visualize the possible solutions of a merging task, such that the impact of decisions can be foreseen. By showing the affordances of the work domain, the display keeps the Air Traffic Controller as the active decision maker rather than issuing advisories. Two professional area controllers examined the proposed display and expressed interest in the additional elements, but also voiced concerns about display clutter. In an experiment the effects of the Inbound Traffic Support System interface were examined for semi-professional participants. When using the display, participants could better estimate whether an aircraft was going to adhere to the altitude restriction. A decreasing trend in the number of control commands for difficult scenarios was found. Possibly, because participants had a better understanding of the situation, and fewer control adjustments had to made. Moreover, a difference in performance between the participants was observed. Some participants made use of the tools as was intended, whereas others seemed at times to be rather overwhelmed with the information. It appears that by revealing the solution space, the interface forces the user to think about a control strategy at an earlier stage.

As aviation recovers to pre-pandemic levels large amounts of Air Traffic Flow Management delay also return, as the traffic at airports and in airspaces reaches their old levels. These delays have a yearly cost of around 500 million euros in the European Civil Aviation Conference area and are detrimental to the experience of passengers. This paper proposes a change in the interaction between the A-CDM process at Schiphol and EUROCONTROL's Network Manager, varying at what point the slot improvement process for regulated flights is stopped; providing more transparency in the network with this change. The resulting change in ATFM delay is investigated by modelling these two systems and freezing the slots at a set time, using historical input data to accurately model the turnaround process. The results generated using the described methods are inconclusive. More data and an additional implementation of using the CTOTs calculated by the model in the simulated turnaround are needed in order to make statistically sound conclusions on whether changing the T-DPI-s horizon has any effect. Nevertheless, this paper still presents insights and a novel framework for continued research into the subject.

The expected growth of civil air traffic and the inclusion of advanced systems in the Royal Netherlands Air Force result in more demanding airspace requirements across all users, making this a scarce resource. To optimise its usage, military airspaces in Amsterdam Flight Information Region are used as Flexible Use of Airspace (FUA), which no longer considers airspace as entirely ‘civil’ or ‘military’, but as a continuum to be allocated temporarily according to user requirements. Given its importance for civil-military cooperation, FUA is at the core of the Dutch Airspace Redesign Programme, considering both a reorganisation of FUA structures and the plannability policies they are reserved with. In order to inform these decisions, this study analyses the effect of FUA availability and plannability on the fuel efficiency of civil commercial traffic. Historical traffic data from the Eurocontrol R&D data archive is sampled for the month of March 2019 and used in three experiments. On the one hand, Experiment 1 investigates FUA availability by considering flights losing route efficiency due to FUA sectors and comparing them with Great Circle Route alternatives and similar flights historically transiting them. On the other hand, Experiment 2 considers flights making use of FUA sectors during times when these have been delegated to civil use to assess the effects of carrying a surplus fuel due to an insufficient airspace plannability. By proposing new plannability policies, the hypothetical reduction in fuel consumption as a result of not taking the surplus fuel is assessed. Lastly, Experiment 3 combines the benefits found in Experiment 1 with the plannability policies of Experiment 2 to determine the fuel benefits resulting from a tactical rerouting enabled by the new plannability concepts. A total of 1,548 simulations have been performed in the open source air traffic simulator BlueSky to compute the fuel efficiency metrics. The results suggest that making both the Alpha and Delta sectors completely available would result in a yearly reduction in fuel consumption of 70,198 and 100,022 tonnes, respectively; 8,908 and 13,301 of which would be saved solely by adopting a new plannability policy (corresponding to 28,060 and 41,898 tonnes of CO2). Finally, not carrying a surplus fuel due to this concept would contribute to an extra 270 and 394 tonnes of fuel consumption being reduced in 2019 (851 and 1,241 tonnes of CO2).  

During the COVID-19 pandemic people living close to the airport got accustomed to less flights, and therefore less noise disturbance. Now the amount of traffic is increasing again and so is the noise disturbance. For this research specifically the optimizing task of distributing of arriving aircraft over the IAF is addressed, as often the shortest transition routes from an IAF towards the runway go over densely populated areas, but flying via another IAF results in longer flying times and thus more CO2 emissions. A tool that is able to quantitatively make a trade-off between noise and emission is required to provide a basis for the distribution of aircraft over the IAF. In this research the feasibility and effects of using an expanded version of the aircraft landing problem to create the IAF Selection Optimization tool is studied, including the effects of different settings for the tool. As a case study Amsterdam Airport Schiphol is used, as it is a busy airport that lies close (11km) to the city center of Amsterdam. In this paper first the methodology of the IAF Selection Optimization tool is explained, and afterwards the working of the tool is discussed by running various scenarios for three different days from 2019 at AAS. For all scenarios an optimal and feasible solution was found by the tool, based on the input variables selected for each scenario. It could therefore be concluded that the IAF Selection Optimization tool provides a means to optimally distribute aircraft based over the IAF based on a quantitative trade-off.

This research investigates the role of time prediction accuracy in optimizing Continuous Descent Operations (CDO) within the aviation sector, with a specific focus on assessing the additional benefits brought forth by the integration of Air-Ground Datalink technologies. Continuous Descent Operations, characterized by uninterrupted and efficient descent profiles, hold promise for reducing fuel consumption, emissions, noise, and overall operational costs. However, the extent to which accurate time predictions contribute to the success of CDO remains a critical yet understudied aspect.

The airspace in western Europe is one of the most complex airspaces globally. With the air space already operating near maximum capacity, innovations are needed to increase airspace efficiency. This research aims to design and test a debunching concept for inbound air traffic to increase the efficiency of the arrival traffic stream. A probabilistic debunching concept is created for the Initial Approach Fix using Probability Density Functions fitted on the errors in predicted arrival times provided by the EUROCONTROL Enhanced Tactical Flow Management System Flight Data messages (EFD). A bunching probability is detected when two or more aircraft have a probability of arriving at the Initial Approach fix simultaneously or within the required Wake Category Separation. A debuncher is created using a Constrained Genetic Algorithm that decreases the bunching probability by imposing en-route delay on arriving air traffic. The results show that the bunching probability increases with shorter prediction horizons as the uncertainty in the arrival estimates decreases. It is shown that with the probabilistic debunching method, it is possible to decrease the delay in the Arrival Manager by imposing delay en-route. However, at prediction ranges before 40 minutes before arrival at the IAF, the decrease in the AMAN delay is not consistent, indicating that the trajectory prediction uncertainty at this range is too high. Furthermore, the decrease in the AMAN delay is often lower than the imposed delay by the debuncher, showing that the decrease in AMAN delay comes at the cost of extra delay en-route. It is concluded that when the uncertainty in the trajectory predictions is decreased, the effectiveness of the debuncher increases and the effect on the Arrival Manager is improved, indicating improved arrival efficiency.

In recent years Amsterdam Airport Schiphol (AAS) has been the airport with the highest Airport Air Traffic Flow Management (ATFM) delay in Europe. EUROCONTROL delays inbound flights heading towards AAS on departure, due to reasons at or around AAS. There are two main reasons for Airport ATFM delay at AAS, Weather and Aerodrome Capacity. This study aims to find a better understanding of the operational conditions that cause ATFM Aerodrome Capacity delay, such that it can be addressed properly. This is done by applying a Bayesian Network (BN) to AAS data, that includes variables from the operational conditions at AAS. The BN shows the relationship between variables and is based on conditional probabilities, so it can show the stochastic behavior of the airport. A baseline model is created using expert knowledge and through Structure Learning (SL), additional relationships between variables are identified which result in a model that best represents the operational data. The results from the BN show that an increased chance of ATFM Aerodrome Capacity delay most often occurs in the first inbound peak of the day when the cumulative delay is still low. In these conditions, the percentage of capacity used according to the schedules is often between 75% and 100%. However, at the actual time of operation the percentage of capacity used is often above 100%, indicating that the available capacity is exceeded. Interestingly, the conditions that increase the chance of having ATFM Aerodrome Capacity delay do not increase the chance of arriving too late. Having an ATFM Aerodrome Capacity delay of 20 to 30 minutes, showed only a 35% chance of arriving with more than 15 minutes of delay. Moreover, there is a discrepancy in the strategic planning of the landing slots, and the actual arriving traffic. Additionally, airlines apply schedule buffers, that could nullify the ATFM Aerodrome Capacity delay, but are unknown to other operators in the air transportation system. More information should be shared between operators to ensure all interests are met and a safe and efficient operation can be realized.

Taxi time predictions are used by air traffic controllers to optimally release aircraft from the gate such that efficiency losses due to queuing are minimized, while runway capacity is maintained. More accurate taxi times can therefore result in improved airport surface operations and reduce air traffic controller workload. This article proposes a methodology to develop taxi time predictor and applies this methodology to Schiphol airport. The methodology combines novel data-driven predictors with different improvements and extensive performance evaluation. One such improvement involves using recent taxi time prediction errors to improve upcoming taxi time predictions. During evaluation, this article extends conventional analysis by analyzing different prediction horizons and performance metrics. Applying the methodology at Schiphol airport resulted in a predictor that increased the fraction of flights with a taxi time error of less than two minutes from 64.41% to 67.91% compared to the currently operational manual decision tree predictor.