Future concepts for Air Traffic Management (ATM) foresee—and even mandate—an increase in the distance from the destination airport at which the ATM system should start planning the arrival sequence and arrival time of inbound flights. By increasing the horizon beyond one hour before arrival, it becomes possible to timely adjust the flight path of the inbound aircraft to meet the planned time. These timely adjustments tend to result in a more efficient flight profile, reducing fuel burn and emissions....@en

Amsterdam Airport Schiphol (AAS) saw a record number of passengers during the summer of 2018, with an average of 220,000 passengers per day[61]. Over recent years, the airport has recorded an increase in passenger numbers every year. Projections of the International Air Transportation Association show a significant increase in demand for air travel worldwide in every of the projection scenarios[38]. It is therefore highly likely that the demand for flying from AAS will continue to increase. This poses a problem because the airport is surrounded by densely populated municipalities. Apart from passenger numbers, the airport also broke records in terms of complaints from the surrounding community[53]. To mitigate nuisance, the airport is assigned a fixed quota in terms of environmental pollution and external safety. This leads to the airport being restricted to a certain amount of flights per year and the operations being highly constrained by noise abatement procedures. A major factor in these noise abatement procedures is the "Fourth runway rule". This rule is aimed at minimising operations with four runways in use simultaneously, by mandating that under normal circumstances no more than 40 flights per day may be handled using the "fourth runway"[3]. As a result, it is required to operate predominantly using configurations with three active runways throughout the day. Facilitating the alternating pattern of inbound and outbound peaks at AAS, using only three active runways, requires switching between using a second runway for inbound traffic and using a second runway for outbound traffic. These type of reconfigurations are referred to as demand-based runway reconfigurations. The "Fourth runway rule" combined with varying wind conditions at AAS, results in the airport seeing 18 runway reconfigurations per day on average[54]. According to investigations by the Dutch council for safety(OVV), the high amount of runway reconfigurations poses elevated safety risks[55]. Apart from these elevated safety risks, runway reconfigurations are considered disruptive events that can lead to substantial capacity reductions for the airport system [4]. Considering these facts, it is critically important to establish an adequate understanding of the phenomenon thereby enabling the estimation of the effects and generating the potential for mitigating these effects. The research described in this report is aimed at developing this understanding by exploring the dynamics, decisionmaking processes, quantitative aspects, and qualitative aspects related to the runway reconfiguration phenomenon. For an airport like AAS, with a complex runway structure, performing a safe and efficient runway reconfiguration is no trivial task. This task is in reality performed through an interaction between two highly skilled and experienced air traffic control operators, that bear responsibility for mutually exclusive control areas of the inboundoutbound system. Investigating this complex socio-technical phenomenon is highly challenging due to the many interactions, uncertainties, and qualitative aspects involved. Especially the aspect of human intelligence involved in this phenomenon is hard to capture and fully understand.

Decentralised air traffic control shifts decision making to a local level, allowing decentralised controllers to focus on solving local conflicts. Since decentralised controllers have accurate local information at their disposal, they can adapt quicker and more efficiently to changing conditions. Consequently, it is suggested that the dynamics of an airport’s ground operation are better managed when using decentralised control. To establish an understanding of the mechanisms of decentralised control, an agent-based simulation model is designed and applied to the actual ground operation at Amsterdam Airport Schiphol. By assessing the emergent behaviour of decentralised control to the actual ground operation managed by centralised control, insights are acquired in terms of the routing strategies, needs, and characteristics of decentralised control.

Air traffic control uses Arrival Managers (AMANs) to schedule an inbound stream of aircraft. As these systems use predicted arrival times to optimize the planning for capacity and flight efficiency, the accuracy of these predictions is an important parameter in arrival planning. While prediction capabilities have improved, and are likely to improve more, it is unlikely that prediction error will disappear altogether. Especially in future scenarios with longer planning horizons, techniques will have to be found to support planning in the presence of prediction uncertainty. To enable working with uncertainty on a predicted arrival time, that uncertainty needs to be predicted itself. This paper proposes and tests a method to predict arrival time uncertainty based on historic prediction accuracy using currently available arrival time estimates.

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