LV
L.S.A.B. Vorage
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To ensure continuous operations at airports, operational schedules need to be able to cope with early and late arriving/departing flights. To optimize such schedules for these events, flight delay predictions are necessary. Till now, flight delay has been studied mainly from a binary and regression standpoint. These type of predictions lack a certainty indication, which is needed by decision-makers to optimize a schedule. This research focuses on probabilistic predictions of flight delay on an individual flight level. Flight operation data from Amsterdam Schiphol airport and weather data from METAR is used to train four machine learning models that predict probabilistic flight delay distributions on a prediction horizon of one day. The first two developed models, a Dropout neural network and a Random Forest, predict empirical distributions. The other two models, a Mean Variance Estimator (MVE) and a Mixture Density Network (MDN) model, predict continuous distributions by predicting the parameters of an assumed distribution. The MVE predicts a single normal distribution, while the MDN model predicts a mixture of multiple normal distributions. Two interval-based performance metrics are suggested which assess the probabilistic predictions from different perspectives. It is concluded that arrival delay is best modelled with a MDN model with ten components, while departure delay is best modelled with only three components. Both models outperform a baseline statistical method on all considered interval widths. The MDN models outperform the other machine learning models by predicting more early arrivals and early departures correctly. The proposed models show that they can provide certainty indications of flight delay on an individual flight level, as opposed to binary and regression models. In the future, these certainty indications can assist airport operators with optimizing operational schedules, ensuring continuous operations and making ultimately air travel more pleasant.
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To ensure continuous operations at airports, operational schedules need to be able to cope with early and late arriving/departing flights. To optimize such schedules for these events, flight delay predictions are necessary. Till now, flight delay has been studied mainly from a binary and regression standpoint. These type of predictions lack a certainty indication, which is needed by decision-makers to optimize a schedule. This research focuses on probabilistic predictions of flight delay on an individual flight level. Flight operation data from Amsterdam Schiphol airport and weather data from METAR is used to train four machine learning models that predict probabilistic flight delay distributions on a prediction horizon of one day. The first two developed models, a Dropout neural network and a Random Forest, predict empirical distributions. The other two models, a Mean Variance Estimator (MVE) and a Mixture Density Network (MDN) model, predict continuous distributions by predicting the parameters of an assumed distribution. The MVE predicts a single normal distribution, while the MDN model predicts a mixture of multiple normal distributions. Two interval-based performance metrics are suggested which assess the probabilistic predictions from different perspectives. It is concluded that arrival delay is best modelled with a MDN model with ten components, while departure delay is best modelled with only three components. Both models outperform a baseline statistical method on all considered interval widths. The MDN models outperform the other machine learning models by predicting more early arrivals and early departures correctly. The proposed models show that they can provide certainty indications of flight delay on an individual flight level, as opposed to binary and regression models. In the future, these certainty indications can assist airport operators with optimizing operational schedules, ensuring continuous operations and making ultimately air travel more pleasant.
Bachelor thesis
(2017)
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A. Doutsis, T. van der Gaag, D. Garcia de la Horra, Mohamed Mohamed Gomaa Abdulfattah Tolba, M. Henkel, B.W.J. van de Krol, A.S. Moreno Gonzalez, S. Ravnan, N.C. Ruitenbeek, L.S.A.B. Vorage, B.C. Root, J.B. Maas, S.S. Mestry
Lunar exploration initially started as a race between two superpowers to see who had the highest technical capabilities to send a human onto the Moon’s surface. Until recently, the frequency of Moon missions has been in decline, but in the last couple years there has been an increasing interest in returning. Scientific studies and observations will be performed and will give humanity a better understanding of the solar system and our place in it. As a result there will be a need for a direct communication line between the lunar surface and Earth, such that rovers can be controlled in real-time and immediately relay their data back, independent of their location on the Moon.
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Lunar exploration initially started as a race between two superpowers to see who had the highest technical capabilities to send a human onto the Moon’s surface. Until recently, the frequency of Moon missions has been in decline, but in the last couple years there has been an increasing interest in returning. Scientific studies and observations will be performed and will give humanity a better understanding of the solar system and our place in it. As a result there will be a need for a direct communication line between the lunar surface and Earth, such that rovers can be controlled in real-time and immediately relay their data back, independent of their location on the Moon.