Improving the accuracy of long-term travel time prediction using heterogeneous ensembles
Autor(a) principal: | |
---|---|
Data de Publicação: | 2015 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://repositorio.inesctec.pt/handle/123456789/3776 http://dx.doi.org/10.1016/j.neucom.2014.08.072 |
Resumo: | This paper is about long-term travel time prediction in public transportation. However, it can be useful for a wider area of applications. It follows a heterogeneous ensemble approach with dynamic selection. A vast set of experiments with a pool of 128 tuples of algorithms and parameter sets (a&ps) has been conducted for each of the six studied routes. Three different algorithms, namely, random forest, projection pursuit regression and support vector machines, were used. Then, ensembles of different sizes were obtained after a pruning step. The best approach to combine the outputs is also addressed. Finally, the best ensemble approach for each of the six routes is compared with the best individual a&ps. The results confirm that heterogeneous ensembles are adequate for long-term travel time prediction. Namely, they achieve both higher accuracy and robustness along time than state-of-the-art learners. |
id |
RCAP_15acc208c22716182abd12348f5d5e79 |
---|---|
oai_identifier_str |
oai:repositorio.inesctec.pt:123456789/3776 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
spelling |
Improving the accuracy of long-term travel time prediction using heterogeneous ensemblesThis paper is about long-term travel time prediction in public transportation. However, it can be useful for a wider area of applications. It follows a heterogeneous ensemble approach with dynamic selection. A vast set of experiments with a pool of 128 tuples of algorithms and parameter sets (a&ps) has been conducted for each of the six studied routes. Three different algorithms, namely, random forest, projection pursuit regression and support vector machines, were used. Then, ensembles of different sizes were obtained after a pruning step. The best approach to combine the outputs is also addressed. Finally, the best ensemble approach for each of the six routes is compared with the best individual a&ps. The results confirm that heterogeneous ensembles are adequate for long-term travel time prediction. Namely, they achieve both higher accuracy and robustness along time than state-of-the-art learners.2017-11-23T11:31:56Z2015-01-01T00:00:00Z2015info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://repositorio.inesctec.pt/handle/123456789/3776http://dx.doi.org/10.1016/j.neucom.2014.08.072engJoão Mendes MoreiraAlípio JorgeJorge Freire SousaCarlos Manuel Soaresinfo:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-05-15T10:20:48Zoai:repositorio.inesctec.pt:123456789/3776Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:53:39.146373Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Improving the accuracy of long-term travel time prediction using heterogeneous ensembles |
title |
Improving the accuracy of long-term travel time prediction using heterogeneous ensembles |
spellingShingle |
Improving the accuracy of long-term travel time prediction using heterogeneous ensembles João Mendes Moreira |
title_short |
Improving the accuracy of long-term travel time prediction using heterogeneous ensembles |
title_full |
Improving the accuracy of long-term travel time prediction using heterogeneous ensembles |
title_fullStr |
Improving the accuracy of long-term travel time prediction using heterogeneous ensembles |
title_full_unstemmed |
Improving the accuracy of long-term travel time prediction using heterogeneous ensembles |
title_sort |
Improving the accuracy of long-term travel time prediction using heterogeneous ensembles |
author |
João Mendes Moreira |
author_facet |
João Mendes Moreira Alípio Jorge Jorge Freire Sousa Carlos Manuel Soares |
author_role |
author |
author2 |
Alípio Jorge Jorge Freire Sousa Carlos Manuel Soares |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
João Mendes Moreira Alípio Jorge Jorge Freire Sousa Carlos Manuel Soares |
description |
This paper is about long-term travel time prediction in public transportation. However, it can be useful for a wider area of applications. It follows a heterogeneous ensemble approach with dynamic selection. A vast set of experiments with a pool of 128 tuples of algorithms and parameter sets (a&ps) has been conducted for each of the six studied routes. Three different algorithms, namely, random forest, projection pursuit regression and support vector machines, were used. Then, ensembles of different sizes were obtained after a pruning step. The best approach to combine the outputs is also addressed. Finally, the best ensemble approach for each of the six routes is compared with the best individual a&ps. The results confirm that heterogeneous ensembles are adequate for long-term travel time prediction. Namely, they achieve both higher accuracy and robustness along time than state-of-the-art learners. |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015-01-01T00:00:00Z 2015 2017-11-23T11:31:56Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://repositorio.inesctec.pt/handle/123456789/3776 http://dx.doi.org/10.1016/j.neucom.2014.08.072 |
url |
http://repositorio.inesctec.pt/handle/123456789/3776 http://dx.doi.org/10.1016/j.neucom.2014.08.072 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/embargoedAccess |
eu_rights_str_mv |
embargoedAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
|
_version_ |
1799131610510000128 |