Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.3279
DC FieldValueLanguage
dc.contributor.authorSanaei, Rasoul-
dc.contributor.authorPinto, Brian Alphonse-
dc.contributor.authorGollnick, Volker-
dc.date.accessioned2021-02-08T07:38:16Z-
dc.date.available2021-02-08T07:38:16Z-
dc.date.issued2021-01-25-
dc.identifierdoi: 10.3390/aerospace8020028-
dc.identifier.citationAerospace 8 (2): 28 (2021)de_DE
dc.identifier.issn2226-4310de_DE
dc.identifier.urihttp://hdl.handle.net/11420/8714-
dc.description.abstractThe 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.-
dc.description.abstractThe 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.en
dc.language.isoende_DE
dc.publisherMultidisciplinary Digital Publishing Institutede_DE
dc.relation.ispartofAerospacede_DE
dc.rightsCC BY 4.0de_DE
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/de_DE
dc.subjectATFM delayde_DE
dc.subjectCNNde_DE
dc.subjectresiliencede_DE
dc.subjectcapacity regulationsde_DE
dc.subject.ddc380: Handel, Kommunikation, Verkehrde_DE
dc.titleToward ATM resiliency : a deep CNN to predict number of delayed flights and ATFM delayde_DE
dc.typeArticlede_DE
dc.date.updated2021-02-05T14:09:48Z-
dc.identifier.doi10.15480/882.3279-
dc.type.diniarticle-
dcterms.DCMITypeText-
tuhh.identifier.urnurn:nbn:de:gbv:830-882.0124118-
tuhh.oai.showtruede_DE
tuhh.abstract.englishThe 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_DE
tuhh.publisher.doi10.3390/aerospace8020028-
tuhh.publication.instituteLufttransportsysteme M-28de_DE
tuhh.identifier.doi10.15480/882.3279-
tuhh.type.opus(wissenschaftlicher) Artikel-
dc.type.driverarticle-
dc.type.casraiJournal Article-
tuhh.container.issue2de_DE
tuhh.container.volume8de_DE
dc.rights.nationallicensefalsede_DE
tuhh.container.articlenumber28de_DE
local.status.inpressfalsede_DE
local.type.versionpublishedVersionde_DE
item.creatorOrcidSanaei, Rasoul-
item.creatorOrcidPinto, Brian Alphonse-
item.creatorOrcidGollnick, Volker-
item.openairetypeArticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.fulltextWith Fulltext-
item.mappedtypeArticle-
item.creatorGNDSanaei, Rasoul-
item.creatorGNDPinto, Brian Alphonse-
item.creatorGNDGollnick, Volker-
item.cerifentitytypePublications-
item.grantfulltextopen-
item.languageiso639-1en-
crisitem.author.deptLufttransportsysteme M-28-
crisitem.author.deptLufttransportsysteme M-28-
crisitem.author.orcid0000-0001-7063-5114-
crisitem.author.orcid0000-0002-3222-109X-
crisitem.author.orcid0000-0001-7214-0828-
crisitem.author.parentorgStudiendekanat Maschinenbau-
crisitem.author.parentorgStudiendekanat Maschinenbau-
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