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dc.contributor.authorMonteiro, Lucas Borges-
dc.contributor.authorRibeiro, Vitor Filincowsky-
dc.contributor.authorGarcia, Cristiano Perez-
dc.contributor.authorRocha Filho, Geraldo Pereira-
dc.contributor.authorLi, Weigang-
dc.date.accessioned2025-09-29T12:08:26Z-
dc.date.available2025-09-29T12:08:26Z-
dc.date.issued2023-01-11-
dc.identifier.citationMONTEIRO, Lucas Borges et al. 4D trajectory conflict detection and resolution using decision tree pruning method. IEEE Latin America Transactions, New York, v. 21, n. 2, p. 277 - 287, 2023. DOI: 10.1109/TLA.2023.10015220. Disponível em: https://ieeexplore.ieee.org/document/10015220. Acesso em: 03 jun. 2025.pt_BR
dc.identifier.urihttp://repositorio.unb.br/handle/10482/52530-
dc.language.isoengpt_BR
dc.publisherIEEEpt_BR
dc.rightsAcesso Restritopt_BR
dc.title4D trajectory conflict detection and resolution using decision tree pruning methodpt_BR
dc.typeArtigopt_BR
dc.subject.keywordATM (Gerenciamento de tráfego aéreo)pt_BR
dc.subject.keywordInteligência artificial (IA)pt_BR
dc.subject.keywordTransporte aéreopt_BR
dc.identifier.doi10.1109/TLA.2023.10015220pt_BR
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/10015220/pt_BR
dc.description.abstract1The aviation community develops Trajectory Based Operations (TBO) as an advancement in Air Traffic Management (ATM). There is still the need for an efficient scheme to present the trajectories, manage their associated data, and further detect and resolve the conflicts (CD&R) that should eventually occur. In this research, we develop a CD&R framework for managing predicted 4-Dimensional Trajectory (4DT). Using Not Only SQL (NoSQL) database (Cassandra and MongoDB), the 4D trajectories of related routes are presented, and the possible conflicts are detected using the strategy of Computing in NoSQL Database. Compared with other conflict detection algorithms, usually by the pairwise method with O(n 2) at least, the proposed Decision Tree Pruning Method (DTPM) effectively treats massive data sets. The 4DT data are collected by Trajectory Predictor (TP) concerning 58% of the whole Brazilian air traffic. The comparison results between Cassandra and MongoDB from the case studies show the effectiveness of the proposed methods for conflict detection. In addition, we prove that the conflict resolution approach is viable for application in real scenarios, finding near-optimal solutions for the conflicts identified by the framework. Finally, we also demonstrated the development of sustainable artificial intelligence in intelligent air transportation to improve safety in air traffic management.pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0003-4941-9542pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0001-9707-8952pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0003-4312-5391pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0001-6795-2768pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0003-1826-1850pt_BR
dc.contributor.affiliationUniversidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computaçãopt_BR
dc.contributor.affiliationUniversidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computaçãopt_BR
dc.contributor.affiliationUniversidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computaçãopt_BR
dc.contributor.affiliationUniversidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computaçãopt_BR
dc.contributor.affiliationUniversidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computaçãopt_BR
dc.description.unidadeInstituto de Ciências Exatas (IE)pt_BR
dc.description.unidadeDepartamento de Ciência da Computação (IE CIC)pt_BR
dc.description.ppgPrograma de Pós-Graduação em Informáticapt_BR
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