dc.creator | Núñez Portillo, Juan Manuel | es |
dc.creator | Valenzuela Romero, Alfonso | es |
dc.creator | Franco Espín, Antonio | es |
dc.creator | Rivas Rivas, Damián | es |
dc.date.accessioned | 2024-05-10T16:33:22Z | |
dc.date.available | 2024-05-10T16:33:22Z | |
dc.date.issued | 2024 | |
dc.identifier.citation | Núñez-Portillo, J.M., Valenzuela, A., Franco, A. y Rivas, D. (2024). Predicting Air Traffic Congestion under Uncertain Adverse Weather. Aerospace, 11 (3), 240. https://doi.org/10.3390/aerospace11030240. | |
dc.identifier.issn | 2226-4310 | es |
dc.identifier.uri | https://hdl.handle.net/11441/158094 | |
dc.description | This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. // (This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management (2nd Edition)). | es |
dc.description.abstract | This paper presents an approach for integrating uncertainty information in air traffic flow management at the tactical phase. In particular, probabilistic methodologies to predict sector demand and sector congestion under adverse weather in a time horizon of 1.5 h are developed. Two sources of uncertainty are considered: the meteorological uncertainty inherent to the forecasting process and the uncertainty in the take-off time. An ensemble approach is adopted to characterize both uncertainty sources. The methodologies rely on a trajectory predictor able to generate an ensemble of 4D trajectories that provides a measure of the trajectory uncertainty, each trajectory avoiding the storm cells encountered along the way. The core of the approach is the statistical processing of the ensemble of trajectories to obtain probabilistic entry and occupancy counts of each sector and their congestion status when the counts are compared to weather-dependent capacity values. A new criterion to assess the risk of sector overload, which takes into account the uncertainty, is also defined. The results are presented for a historical situation over the Austrian airspace on a day with significant convection. | es |
dc.format | application/pdf | es |
dc.format.extent | 24 p. | es |
dc.language.iso | eng | es |
dc.publisher | Multidisciplinary Digital Publishing Institute (MDPI) | es |
dc.relation.ispartof | Aerospace, 11 (3), 240. | |
dc.rights | Atribución 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Air traffic congestion | es |
dc.subject | Tactical flow management | es |
dc.subject | Adverse weather | es |
dc.subject | Meteorological uncertainty | es |
dc.title | Predicting Air Traffic Congestion under Uncertain Adverse Weather | es |
dc.type | info:eu-repo/semantics/article | es |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ingeniería Aeroespacial y Mecánica de Fluidos | es |
dc.relation.projectID | 885919 | es |
dc.relation.projectID | PID2021- 122323OB-C32 | es |
dc.relation.publisherversion | https://www.mdpi.com/2226-4310/11/3/240 | es |
dc.identifier.doi | 10.3390/aerospace11030240 | es |
dc.contributor.group | Universidad de Sevilla. TEP945: Ingeniería Aeroespacial | es |
dc.journaltitle | Aerospace | es |
dc.publication.volumen | 11 | es |
dc.publication.issue | 3 | es |
dc.publication.initialPage | 240 | es |
dc.contributor.funder | European Union (UE). H2020 | es |
dc.contributor.funder | Ministerio de Ciencia, Innovación y Universidades. España | es |
dc.contributor.funder | Agencia Estatal de Investigación. España | es |
dc.contributor.funder | European Union (UE) | es |