Abstract
Weather is an important source of delay for aircraft. Recent studies have shown that certain weather factors have significant influence on air traffic. More than 50% of all delay accounts to weather and causes among others high costs to airlines and passengers. In this work we will show to what extent weather factors in the closer region of Frankfurt Airport have an impact on the delay of flights. Besides the results of a linear regression model we will also present the results of some modern data mining approaches, such as regression trees and fuzzy clustering techniques. With the clustering approach we will show that several weather conditions have a similar influence on the delay of flights. Our analyses focus on the delay that will be explicitly caused by weather factors in the vicinity of the airport, the so-called terminal management area (TMA). Thus, delay caused by weather at the departure airport or by other circumstances during the flight will not bias our results. With our methods it becomes possible to predict the delay of flights if certain weather factors are known. We will specify these factors and quantify their effects on delay.
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© 2005 Springer-Verlag Berlin Heidelberg
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Rehm, F., Klawonn, F. (2005). Learning Methods for Air Traffic Management. In: Godo, L. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2005. Lecture Notes in Computer Science(), vol 3571. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11518655_83
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DOI: https://doi.org/10.1007/11518655_83
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27326-4
Online ISBN: 978-3-540-31888-0
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