Map-Reduce for Processing GPS Data from Public Transport in Montevideo, Uruguay

Autores
Massobrio, Renzo; Pías, Andrés; Vázquez, Nicolás; Nesmachnow, Sergio
Año de publicación
2016
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
This article addresses the problem of processing large volumes of historical GPS data from buses to compute quality-of-service metrics for urban transportation systems. We designed and implemented a solution to distribute the data processing on multiple processing units in a distributed computing infrastructure. For the experimental analysis we used historical data from Montevideo, Uruguay. The proposed solution scales properly when processing large volumes of input data, achieving a speedup of up to 22× when using 24 computing resources. As case studies, we used the historical data to compute the average speed of bus lines in Montevideo and identify troublesome locations, according to the delay and deviation of the times to reach each bus stop. Similar studies can be used by control authorities and policy makers to get an insight of the transportation system and improve the quality of service.
Sociedad Argentina de Informática e Investigación Operativa (SADIO)
Materia
Ciencias Informáticas
map-reduce
big data
intelligent transportation systems
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-sa/3.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/56810
Acceder
id SEDICI_0c4d8eb5f84b17e9aa144d2393fdf351
oai_identifier_str oai:sedici.unlp.edu.ar:10915/56810
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Map-Reduce for Processing GPS Data from Public Transport in Montevideo, UruguayMassobrio, RenzoPías, AndrésVázquez, NicolásNesmachnow, SergioCiencias Informáticasmap-reducebig dataintelligent transportation systemsThis article addresses the problem of processing large volumes of historical GPS data from buses to compute quality-of-service metrics for urban transportation systems. We designed and implemented a solution to distribute the data processing on multiple processing units in a distributed computing infrastructure. For the experimental analysis we used historical data from Montevideo, Uruguay. The proposed solution scales properly when processing large volumes of input data, achieving a speedup of up to 22× when using 24 computing resources. As case studies, we used the historical data to compute the average speed of bus lines in Montevideo and identify troublesome locations, according to the delay and deviation of the times to reach each bus stop. Similar studies can be used by control authorities and policy makers to get an insight of the transportation system and improve the quality of service.Sociedad Argentina de Informática e Investigación Operativa (SADIO)2016-09info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf41-54http://sedici.unlp.edu.ar/handle/10915/56810enginfo:eu-repo/semantics/altIdentifier/url/http://45jaiio.sadio.org.ar/sites/default/files/AGRANDA-01.pdfinfo:eu-repo/semantics/altIdentifier/issn/2451-7569info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-sa/3.0/Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:06:08Zoai:sedici.unlp.edu.ar:10915/56810Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:06:08.511SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Map-Reduce for Processing GPS Data from Public Transport in Montevideo, Uruguay
title Map-Reduce for Processing GPS Data from Public Transport in Montevideo, Uruguay
spellingShingle Map-Reduce for Processing GPS Data from Public Transport in Montevideo, Uruguay
Massobrio, Renzo
Ciencias Informáticas
map-reduce
big data
intelligent transportation systems
title_short Map-Reduce for Processing GPS Data from Public Transport in Montevideo, Uruguay
title_full Map-Reduce for Processing GPS Data from Public Transport in Montevideo, Uruguay
title_fullStr Map-Reduce for Processing GPS Data from Public Transport in Montevideo, Uruguay
title_full_unstemmed Map-Reduce for Processing GPS Data from Public Transport in Montevideo, Uruguay
title_sort Map-Reduce for Processing GPS Data from Public Transport in Montevideo, Uruguay
dc.creator.none.fl_str_mv Massobrio, Renzo
Pías, Andrés
Vázquez, Nicolás
Nesmachnow, Sergio
author Massobrio, Renzo
author_facet Massobrio, Renzo
Pías, Andrés
Vázquez, Nicolás
Nesmachnow, Sergio
author_role author
author2 Pías, Andrés
Vázquez, Nicolás
Nesmachnow, Sergio
author2_role author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
map-reduce
big data
intelligent transportation systems
topic Ciencias Informáticas
map-reduce
big data
intelligent transportation systems
dc.description.none.fl_txt_mv This article addresses the problem of processing large volumes of historical GPS data from buses to compute quality-of-service metrics for urban transportation systems. We designed and implemented a solution to distribute the data processing on multiple processing units in a distributed computing infrastructure. For the experimental analysis we used historical data from Montevideo, Uruguay. The proposed solution scales properly when processing large volumes of input data, achieving a speedup of up to 22× when using 24 computing resources. As case studies, we used the historical data to compute the average speed of bus lines in Montevideo and identify troublesome locations, according to the delay and deviation of the times to reach each bus stop. Similar studies can be used by control authorities and policy makers to get an insight of the transportation system and improve the quality of service.
Sociedad Argentina de Informática e Investigación Operativa (SADIO)
description This article addresses the problem of processing large volumes of historical GPS data from buses to compute quality-of-service metrics for urban transportation systems. We designed and implemented a solution to distribute the data processing on multiple processing units in a distributed computing infrastructure. For the experimental analysis we used historical data from Montevideo, Uruguay. The proposed solution scales properly when processing large volumes of input data, achieving a speedup of up to 22× when using 24 computing resources. As case studies, we used the historical data to compute the average speed of bus lines in Montevideo and identify troublesome locations, according to the delay and deviation of the times to reach each bus stop. Similar studies can be used by control authorities and policy makers to get an insight of the transportation system and improve the quality of service.
publishDate 2016
dc.date.none.fl_str_mv 2016-09
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/56810
url http://sedici.unlp.edu.ar/handle/10915/56810
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://45jaiio.sadio.org.ar/sites/default/files/AGRANDA-01.pdf
info:eu-repo/semantics/altIdentifier/issn/2451-7569
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-sa/3.0/
Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-sa/3.0/
Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0)
dc.format.none.fl_str_mv application/pdf
41-54
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
_version_ 1844615932250423296
score 13.070432

Publicaciones similares