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dc.contributor.author
Cardoso Pereira, Isadora
dc.contributor.author
Borges, João B.
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Barros, Pedro H.
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Loureiro, Antonio F.
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Rosso, Osvaldo Anibal
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dc.contributor.author
Ramos, Heitor S.
dc.date.available
2023-08-28T14:04:57Z
dc.date.issued
2022-01
dc.identifier.citation
Cardoso Pereira, Isadora; Borges, João B.; Barros, Pedro H.; Loureiro, Antonio F.; Rosso, Osvaldo Anibal; et al.; Leveraging the self-transition probability of ordinal patterns transition network for transportation mode identification based on GPS data; Springer; Nonlinear Dynamics; 107; 1; 1-2022; 889-908
dc.identifier.issn
0924-090X
dc.identifier.uri
http://hdl.handle.net/11336/209501
dc.description.abstract
Analyzing people’s mobility and identifying the transportation mode is essential for cities to create travel diaries. It can help develop essential technologies to reduce traffic jams and travel time between their points, thus helping to improve the quality of life of citizens. Previous studies in this context extracted many specialized features, reaching hundreds of them. This approach requires domain knowledge. Other strategies focused on deep learning methods, which need intense computational power and more data than traditional methods to train their models. In this work, we propose using information theory quantifiers retained from the ordinal patterns (OPs) transformation for transportation mode identification. Our proposal presents the advantage of using fewer data. OP is also computationally inexpensive and has low dimensionality. It is beneficial for scenarios where it is hard to collect information, such as Internet-of-things contexts. Our results demonstrated that OP features enhance the classification results of standard features in such scenarios.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Springer
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dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
INFORMATION THEORY
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MOBILITY
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ORDINAL PATTERNS
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ORDINAL PATTERNS TRANSITION NETWORK
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TIME SERIES
dc.subject.classification
Otras Ciencias Físicas
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dc.subject.classification
Ciencias Físicas
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dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
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dc.title
Leveraging the self-transition probability of ordinal patterns transition network for transportation mode identification based on GPS data
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2023-07-07T18:00:08Z
dc.journal.volume
107
dc.journal.number
1
dc.journal.pagination
889-908
dc.journal.pais
Alemania
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dc.description.fil
Fil: Cardoso Pereira, Isadora. Universidade Federal de Minas Gerais; Brasil
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Fil: Borges, João B.. Universidade Federal do Rio Grande do Norte; Brasil
dc.description.fil
Fil: Barros, Pedro H.. Universidade Federal de Minas Gerais; Brasil
dc.description.fil
Fil: Loureiro, Antonio F.. Universidade Federal de Minas Gerais; Brasil
dc.description.fil
Fil: Rosso, Osvaldo Anibal. Universidade Federal de Alagoas; Brasil. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física La Plata; Argentina
dc.description.fil
Fil: Ramos, Heitor S.. Universidade Federal de Minas Gerais; Brasil
dc.journal.title
Nonlinear Dynamics
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dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1007/s11071-021-07059-x
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