Predictive classification and understanding of weather impact on airport performance through machine learning

Efficient airport operations depend on appropriate actions and reactions to current constraints. Local weather events and their impact on airport performance may have network-wide effects. The classification of expected weather impacts enables efficient consideration in airport operations on a tacti...

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Main Authors: Schultz, Michael, Reitmann, Stefan, Alam, Sameer
Other Authors: Air Traffic Management Research Institute
Format: Article
Language:English
Published: 2022
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Online Access:https://hdl.handle.net/10356/154997
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1549972022-01-29T20:10:24Z Predictive classification and understanding of weather impact on airport performance through machine learning Schultz, Michael Reitmann, Stefan Alam, Sameer Air Traffic Management Research Institute Engineering::Aeronautical engineering Machine Learning Air Traffic Management Efficient airport operations depend on appropriate actions and reactions to current constraints. Local weather events and their impact on airport performance may have network-wide effects. The classification of expected weather impacts enables efficient consideration in airport operations on a tactical level. We classify airport performance with recurrent and convolutional neural networks considering weather data. We are using London–Gatwick Airport to apply our developed approach. The weather data is derived from local meteorological reports and airport performance is derived from both flight plan data and reported delays. We show that the application of machine learning approaches is an appropriate method to quantify the correlation between decreased airport performance and the severity of local weather events. The developed models could achieve prediction accuracy higher than 90% for departure movements. We see our approach as one key element for a deeper understanding of interdependencies between local and network operations in the air transportation system. Accepted version 2022-01-26T02:37:35Z 2022-01-26T02:37:35Z 2021 Journal Article Schultz, M., Reitmann, S. & Alam, S. (2021). Predictive classification and understanding of weather impact on airport performance through machine learning. Transportation Research Part C: Emerging Technologies, 131, 103119-. https://dx.doi.org/10.1016/j.trc.2021.103119 0968-090X https://hdl.handle.net/10356/154997 10.1016/j.trc.2021.103119 2-s2.0-85113679227 131 103119 en Transportation Research Part C: Emerging Technologies © 2021 Elsevier Ltd. All rights reserved. This paper was published in Transportation Research Part C: Emerging Technologies and is made available with permission of Elsevier Ltd. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Aeronautical engineering
Machine Learning
Air Traffic Management
spellingShingle Engineering::Aeronautical engineering
Machine Learning
Air Traffic Management
Schultz, Michael
Reitmann, Stefan
Alam, Sameer
Predictive classification and understanding of weather impact on airport performance through machine learning
description Efficient airport operations depend on appropriate actions and reactions to current constraints. Local weather events and their impact on airport performance may have network-wide effects. The classification of expected weather impacts enables efficient consideration in airport operations on a tactical level. We classify airport performance with recurrent and convolutional neural networks considering weather data. We are using London–Gatwick Airport to apply our developed approach. The weather data is derived from local meteorological reports and airport performance is derived from both flight plan data and reported delays. We show that the application of machine learning approaches is an appropriate method to quantify the correlation between decreased airport performance and the severity of local weather events. The developed models could achieve prediction accuracy higher than 90% for departure movements. We see our approach as one key element for a deeper understanding of interdependencies between local and network operations in the air transportation system.
author2 Air Traffic Management Research Institute
author_facet Air Traffic Management Research Institute
Schultz, Michael
Reitmann, Stefan
Alam, Sameer
format Article
author Schultz, Michael
Reitmann, Stefan
Alam, Sameer
author_sort Schultz, Michael
title Predictive classification and understanding of weather impact on airport performance through machine learning
title_short Predictive classification and understanding of weather impact on airport performance through machine learning
title_full Predictive classification and understanding of weather impact on airport performance through machine learning
title_fullStr Predictive classification and understanding of weather impact on airport performance through machine learning
title_full_unstemmed Predictive classification and understanding of weather impact on airport performance through machine learning
title_sort predictive classification and understanding of weather impact on airport performance through machine learning
publishDate 2022
url https://hdl.handle.net/10356/154997
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