Vehicular Flow Analysis Using Clusters
- Autores
- Reyes, Gary; Lanzarini, Laura Cristina; Estrebou, César Armando; Maquilón, Víctor
- Año de publicación
- 2021
- Idioma
- inglés
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- The volume of vehicular traffic in large cities has increased in recent years, causing mobility problems, which is why the analysis of vehicular flow data becomes important for researchers. Intelligent Transportation Systems perform vehicle monitoring and control by collecting GPS trajectories, information that provides real-time geographic location of vehicles, which allows the identification of patterns on vehicle flow using clustering techniques. This paper presents a methodology capable of analyzing vehicular flow in a given area, identifying speed ranges and maintaining an updated interactive map that facilitates the identification of areas of possible traffic jams. The results obtained on a dataset from the city of Guayaquil-Ecuador are satisfactory and clearly represent the speed of vehicle displacement by automatically identifying the most representative ranges for each instant of time.
Workshop: WBDMD - Base de Datos y Minería de Datos
Red de Universidades con Carreras en Informática - Materia
-
Ciencias Informáticas
Vehicular flow
Cluster
GPS trajectory - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/4.0/
- Repositorio
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/130341
Ver los metadatos del registro completo
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Vehicular Flow Analysis Using ClustersReyes, GaryLanzarini, Laura CristinaEstrebou, César ArmandoMaquilón, VíctorCiencias InformáticasVehicular flowClusterGPS trajectoryThe volume of vehicular traffic in large cities has increased in recent years, causing mobility problems, which is why the analysis of vehicular flow data becomes important for researchers. Intelligent Transportation Systems perform vehicle monitoring and control by collecting GPS trajectories, information that provides real-time geographic location of vehicles, which allows the identification of patterns on vehicle flow using clustering techniques. This paper presents a methodology capable of analyzing vehicular flow in a given area, identifying speed ranges and maintaining an updated interactive map that facilitates the identification of areas of possible traffic jams. The results obtained on a dataset from the city of Guayaquil-Ecuador are satisfactory and clearly represent the speed of vehicle displacement by automatically identifying the most representative ranges for each instant of time.Workshop: WBDMD - Base de Datos y Minería de DatosRed de Universidades con Carreras en Informática2021-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf261-270http://sedici.unlp.edu.ar/handle/10915/130341enginfo:eu-repo/semantics/altIdentifier/isbn/978-987-633-574-4info:eu-repo/semantics/reference/hdl/10915/129809info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T11:04:53Zoai:sedici.unlp.edu.ar:10915/130341Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 11:04:53.863SEDICI (UNLP) - Universidad Nacional de La Platafalse |
dc.title.none.fl_str_mv |
Vehicular Flow Analysis Using Clusters |
title |
Vehicular Flow Analysis Using Clusters |
spellingShingle |
Vehicular Flow Analysis Using Clusters Reyes, Gary Ciencias Informáticas Vehicular flow Cluster GPS trajectory |
title_short |
Vehicular Flow Analysis Using Clusters |
title_full |
Vehicular Flow Analysis Using Clusters |
title_fullStr |
Vehicular Flow Analysis Using Clusters |
title_full_unstemmed |
Vehicular Flow Analysis Using Clusters |
title_sort |
Vehicular Flow Analysis Using Clusters |
dc.creator.none.fl_str_mv |
Reyes, Gary Lanzarini, Laura Cristina Estrebou, César Armando Maquilón, Víctor |
author |
Reyes, Gary |
author_facet |
Reyes, Gary Lanzarini, Laura Cristina Estrebou, César Armando Maquilón, Víctor |
author_role |
author |
author2 |
Lanzarini, Laura Cristina Estrebou, César Armando Maquilón, Víctor |
author2_role |
author author author |
dc.subject.none.fl_str_mv |
Ciencias Informáticas Vehicular flow Cluster GPS trajectory |
topic |
Ciencias Informáticas Vehicular flow Cluster GPS trajectory |
dc.description.none.fl_txt_mv |
The volume of vehicular traffic in large cities has increased in recent years, causing mobility problems, which is why the analysis of vehicular flow data becomes important for researchers. Intelligent Transportation Systems perform vehicle monitoring and control by collecting GPS trajectories, information that provides real-time geographic location of vehicles, which allows the identification of patterns on vehicle flow using clustering techniques. This paper presents a methodology capable of analyzing vehicular flow in a given area, identifying speed ranges and maintaining an updated interactive map that facilitates the identification of areas of possible traffic jams. The results obtained on a dataset from the city of Guayaquil-Ecuador are satisfactory and clearly represent the speed of vehicle displacement by automatically identifying the most representative ranges for each instant of time. Workshop: WBDMD - Base de Datos y Minería de Datos Red de Universidades con Carreras en Informática |
description |
The volume of vehicular traffic in large cities has increased in recent years, causing mobility problems, which is why the analysis of vehicular flow data becomes important for researchers. Intelligent Transportation Systems perform vehicle monitoring and control by collecting GPS trajectories, information that provides real-time geographic location of vehicles, which allows the identification of patterns on vehicle flow using clustering techniques. This paper presents a methodology capable of analyzing vehicular flow in a given area, identifying speed ranges and maintaining an updated interactive map that facilitates the identification of areas of possible traffic jams. The results obtained on a dataset from the city of Guayaquil-Ecuador are satisfactory and clearly represent the speed of vehicle displacement by automatically identifying the most representative ranges for each instant of time. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-10 |
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 |
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conferenceObject |
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publishedVersion |
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http://sedici.unlp.edu.ar/handle/10915/130341 |
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http://sedici.unlp.edu.ar/handle/10915/130341 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
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info:eu-repo/semantics/altIdentifier/isbn/978-987-633-574-4 info:eu-repo/semantics/reference/hdl/10915/129809 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
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openAccess |
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http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
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application/pdf 261-270 |
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