Modelling Demand-Based Runway Reconfigurations

Modelling Demand-Based Runway Reconfigurations: A Symbolic AI Approach

van den Berg, Jos (TU Delft Aerospace Engineering)

Santos, Bruno F. (mentor)
Sharpanskykh, Alexei (mentor)
Tielrooij, M. (mentor)
Stroosma, O. (mentor)

Delft University of Technology

Aerospace Engineering

2020-03-06

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.

Agent-Based Modellig

http://resolver.tudelft.nl/uuid:32f18eae-8056-40bc-b12e-85dbdd7fbb9f

Student theses

master thesis

© 2020 Jos van den Berg