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...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/154997 |
| _version_ | 1826124315437826048 |
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| author | Schultz, Michael Reitmann, Stefan Alam, Sameer |
| author2 | Air Traffic Management Research Institute |
| author_facet | Air Traffic Management Research Institute Schultz, Michael Reitmann, Stefan Alam, Sameer |
| author_sort | Schultz, Michael |
| collection | NTU |
| 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. |
| first_indexed | 2024-10-01T06:18:38Z |
| format | Journal Article |
| id | ntu-10356/154997 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:18:38Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ntu-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 |
| 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 |
| title | 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_short | 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 |
| topic | Engineering::Aeronautical engineering Machine Learning Air Traffic Management |
| url | https://hdl.handle.net/10356/154997 |
| work_keys_str_mv | AT schultzmichael predictiveclassificationandunderstandingofweatherimpactonairportperformancethroughmachinelearning AT reitmannstefan predictiveclassificationandunderstandingofweatherimpactonairportperformancethroughmachinelearning AT alamsameer predictiveclassificationandunderstandingofweatherimpactonairportperformancethroughmachinelearning |