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Toward ATM resiliency: a deep CNN to predict number of delayed flights and ATFM delay
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| Veröffentlicht in: | Aerospace Volume 8 (2021), issue 2, Artikel 28; insgesamt 22 Seiten |
|---|---|
| Personen und Körperschaften: | , , , , |
| Titel: | Toward ATM resiliency: a deep CNN to predict number of delayed flights and ATFM delay/ Rasoul Sanaei, Brian Alphonse Pinto and Volker Gollnick |
| Format: | E-Book-Kapitel |
| Sprache: | Englisch |
| veröffentlicht: |
25 January 2021
|
| Gesamtaufnahme: |
: Aerospace, Volume 8 (2021), issue 2, Artikel 28; insgesamt 22 Seiten
, volume:8 |
| Schlagwörter: | |
| Quelle: | Verbunddaten SWB Lizenzfreie Online-Ressourcen |
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| contents | The European Air Traffic Management Network (EATMN) is comprised of various stakeholders and actors. Accordingly, the operations within EATMN are planned up to six months ahead of target date (tactical phase). However, stochastic events and the built-in operational flexibility (robustness), along with other factors, result in demand and capacity imbalances that lead to delayed flights. The size of the EATMN and its complexity challenge the prediction of the total network delay using analytical methods or optimization approaches. We face this challenge by proposing a deep convolutional neural network (DCNN), which takes capacity regulations as the input. DCNN architecture successfully improves the prediction results by 50 percent (compared to random forest as the baseline model). In fact, the trained model on 2016 and 2017 data is able to predict 2018 with a mean absolute percentage error of 22% and 14% for the delay and delayed traffic, respectively. This study presents a method to provide more accurate situational awareness, which is a must for the topic of network resiliency. |
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| spelling | Sanaei, Rasoul VerfasserIn (DE-588)1226686400 (DE-627)1747720097 aut, Toward ATM resiliency a deep CNN to predict number of delayed flights and ATFM delay Rasoul Sanaei, Brian Alphonse Pinto and Volker Gollnick, 25 January 2021, Diagramme, 22, Text txt rdacontent, Computermedien c rdamedia, Online-Ressource cr rdacarrier, Sonstige Körperschaft: Technische Universität Hamburg, Sonstige Körperschaft: Technische Universität Hamburg, Institut für Lufttransportsysteme, DE-830 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2, The European Air Traffic Management Network (EATMN) is comprised of various stakeholders and actors. Accordingly, the operations within EATMN are planned up to six months ahead of target date (tactical phase). However, stochastic events and the built-in operational flexibility (robustness), along with other factors, result in demand and capacity imbalances that lead to delayed flights. The size of the EATMN and its complexity challenge the prediction of the total network delay using analytical methods or optimization approaches. We face this challenge by proposing a deep convolutional neural network (DCNN), which takes capacity regulations as the input. DCNN architecture successfully improves the prediction results by 50 percent (compared to random forest as the baseline model). In fact, the trained model on 2016 and 2017 data is able to predict 2018 with a mean absolute percentage error of 22% and 14% for the delay and delayed traffic, respectively. This study presents a method to provide more accurate situational awareness, which is a must for the topic of network resiliency., DE-830 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/, ATFM delay DSpace, CNN DSpace, resilience DSpace, capacity regulations DSpace, Pinto, Brian Alphonse VerfasserIn aut, Gollnick, Volker 1964- VerfasserIn (DE-588)129040525 (DE-627)387890874 (DE-576)297462385 aut, Technische Universität Hamburg (DE-588)1112763473 (DE-627)866918418 (DE-576)476770564 oth, Institut für Lufttransportsysteme (DE-588)113826007X (DE-627)895590646 (DE-576)492453209 oth, Enthalten in Aerospace Basel : MDPI, 2014 Volume 8 (2021), issue 2, Artikel 28; insgesamt 22 Seiten Online-Ressource (DE-627)778375048 (DE-600)2756091-0 (DE-576)401032795 2226-4310 nnns, volume:8 year:2021 number:2 extent:22, http://nbn-resolving.de/urn:nbn:de:gbv:830-882.0124118 Resolving-System kostenfrei, https://doi.org/10.15480/882.3279 Resolving-System kostenfrei, http://hdl.handle.net/11420/8714 Resolving-System kostenfrei, https://doi.org/10.3390/aerospace8020028 Resolving-System, https://doi.org/10.3390/aerospace8020028 LFER, http://nbn-resolving.de/urn:nbn:de:gbv:830-882.0124118 LFER, LFER 2021-03-09T02:03:40Z |
| spellingShingle | Sanaei, Rasoul, Pinto, Brian Alphonse, Gollnick, Volker, Toward ATM resiliency: a deep CNN to predict number of delayed flights and ATFM delay, The European Air Traffic Management Network (EATMN) is comprised of various stakeholders and actors. Accordingly, the operations within EATMN are planned up to six months ahead of target date (tactical phase). However, stochastic events and the built-in operational flexibility (robustness), along with other factors, result in demand and capacity imbalances that lead to delayed flights. The size of the EATMN and its complexity challenge the prediction of the total network delay using analytical methods or optimization approaches. We face this challenge by proposing a deep convolutional neural network (DCNN), which takes capacity regulations as the input. DCNN architecture successfully improves the prediction results by 50 percent (compared to random forest as the baseline model). In fact, the trained model on 2016 and 2017 data is able to predict 2018 with a mean absolute percentage error of 22% and 14% for the delay and delayed traffic, respectively. This study presents a method to provide more accurate situational awareness, which is a must for the topic of network resiliency., ATFM delay, CNN, resilience, capacity regulations |
| title | Toward ATM resiliency: a deep CNN to predict number of delayed flights and ATFM delay |
| title_auth | Toward ATM resiliency a deep CNN to predict number of delayed flights and ATFM delay |
| title_full | Toward ATM resiliency a deep CNN to predict number of delayed flights and ATFM delay Rasoul Sanaei, Brian Alphonse Pinto and Volker Gollnick |
| title_fullStr | Toward ATM resiliency a deep CNN to predict number of delayed flights and ATFM delay Rasoul Sanaei, Brian Alphonse Pinto and Volker Gollnick |
| title_full_unstemmed | Toward ATM resiliency a deep CNN to predict number of delayed flights and ATFM delay Rasoul Sanaei, Brian Alphonse Pinto and Volker Gollnick |
| title_in_hierarchy | Toward ATM resiliency: a deep CNN to predict number of delayed flights and ATFM delay / Rasoul Sanaei, Brian Alphonse Pinto and Volker Gollnick, |
| title_short | Toward ATM resiliency |
| title_sort | toward atm resiliency a deep cnn to predict number of delayed flights and atfm delay |
| title_sub | a deep CNN to predict number of delayed flights and ATFM delay |
| topic | ATFM delay, CNN, resilience, capacity regulations |
| topic_facet | ATFM delay, CNN, resilience, capacity regulations |
| url | http://nbn-resolving.de/urn:nbn:de:gbv:830-882.0124118, https://doi.org/10.15480/882.3279, http://hdl.handle.net/11420/8714, https://doi.org/10.3390/aerospace8020028 |
| urn | urn:nbn:de:gbv:830-882.0124118 |