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Dynamic grouping of vehicle trajectories
Vehicular traffic volume in large cities has increased in recent years, causing mobility problems; therefore,the analysis of vehicle flow data becomes a relevant research topic. Intelligent Transportation Systems monitor and control vehicular movements by collecting GPS trajectories, which provides...
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Published in: | Journal of computer science and technology (La Plata) 2022-10, Vol.22 (2), p.141-150 |
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description | Vehicular traffic volume in large cities has increased in recent years, causing mobility problems; therefore,the analysis of vehicle flow data becomes a relevant research topic. Intelligent Transportation Systems monitor and control vehicular movements by collecting GPS trajectories, which provides the geographic location of vehicles in real time. Thus information is processed using clustering techniques to identify vehicular flow patterns. This work presents a methodologycapable of analyzing the vehicular flow in a given area, identifying speed ranges and keeping an interactivemap updated that facilitates the identification of possible traffic jam areas. The results obtained on threedata sets from the cities of Guayaquil-Ecuador, RomeItaly and Beijing-China are satisfactory and clearlyrepresent the speed of movement of the vehicles, automatically identifying the most representative ranges inreal time.
El volumen de tráfico vehicular de las grandes ciudades se ha incrementado en los últimos a˜nos originando problemas de movilidad, por ello el análisis de los datos del flujo vehicular toma importancia para los investigadores. Los Sistemas Inteligentes de transportación realizan el monitoreo y control vehicular recolectando trayectorias GPS, información que brinda en tiempo real la ubicación geográfica de los vehículos. Su procesamiento por medio de técnicas de agrupamiento permite identificar patrones sobre el flujo vehicular. Este trabajo presenta una metodología capaz de analizar el flujo vehicular en un área dada, identificando los rangos de velocidades y manteniendo actualizado un mapa interactivo que facilita la identificación de zonas de posibles atascos. Los resultados obtenidos sobre tres conjuntos de datos de las ciudades de Guayaquil-Ecuador, Roma-Italia y Beijing-China son satisfactorios y representan claramente la velocidad de desplazamiento de los vehículos identificando de manera automática los rangos más representativos para cada instante de tiempo. |
doi_str_mv | 10.24215/16666038.22.e11 |
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El volumen de tráfico vehicular de las grandes ciudades se ha incrementado en los últimos a˜nos originando problemas de movilidad, por ello el análisis de los datos del flujo vehicular toma importancia para los investigadores. Los Sistemas Inteligentes de transportación realizan el monitoreo y control vehicular recolectando trayectorias GPS, información que brinda en tiempo real la ubicación geográfica de los vehículos. Su procesamiento por medio de técnicas de agrupamiento permite identificar patrones sobre el flujo vehicular. Este trabajo presenta una metodología capaz de analizar el flujo vehicular en un área dada, identificando los rangos de velocidades y manteniendo actualizado un mapa interactivo que facilita la identificación de zonas de posibles atascos. Los resultados obtenidos sobre tres conjuntos de datos de las ciudades de Guayaquil-Ecuador, Roma-Italia y Beijing-China son satisfactorios y representan claramente la velocidad de desplazamiento de los vehículos identificando de manera automática los rangos más representativos para cada instante de tiempo.</description><identifier>ISSN: 1666-6038</identifier><identifier>ISSN: 1666-6046</identifier><identifier>EISSN: 1666-6038</identifier><identifier>DOI: 10.24215/16666038.22.e11</identifier><language>eng</language><publisher>La Plata: Universidad Nacional de La Plata</publisher><subject>Automatic vehicle identification systems ; Clustering ; data stream ; dynamic clustering ; Flow distribution ; Geographical locations ; Intelligent transportation systems ; Real time ; Traffic congestion ; Traffic jams ; Traffic volume ; Trajectories ; vehicular trajectories</subject><ispartof>Journal of computer science and technology (La Plata), 2022-10, Vol.22 (2), p.141-150</ispartof><rights>2022. This work is licensed under https://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c401t-83df57d6792493eb342f9a8f5074b457a7257c48084a9ac12d757c5c43200f083</citedby><orcidid>0000-0001-5926-8827 ; 0000-0003-1014-1010 ; 0000-0002-3711-1906 ; 0000-0001-7027-7564</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2791535769?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,778,782,11671,25736,27907,27908,36043,36995,44346,44573</link.rule.ids></links><search><creatorcontrib>Reyes, Gary</creatorcontrib><creatorcontrib>Lanzarini, Laura</creatorcontrib><creatorcontrib>Estrebou, Cesar</creatorcontrib><creatorcontrib>Bariviera, Aurelio F</creatorcontrib><title>Dynamic grouping of vehicle trajectories</title><title>Journal of computer science and technology (La Plata)</title><description>Vehicular traffic volume in large cities has increased in recent years, causing mobility problems; therefore,the analysis of vehicle flow data becomes a relevant research topic. Intelligent Transportation Systems monitor and control vehicular movements by collecting GPS trajectories, which provides the geographic location of vehicles in real time. Thus information is processed using clustering techniques to identify vehicular flow patterns. This work presents a methodologycapable of analyzing the vehicular flow in a given area, identifying speed ranges and keeping an interactivemap updated that facilitates the identification of possible traffic jam areas. The results obtained on threedata sets from the cities of Guayaquil-Ecuador, RomeItaly and Beijing-China are satisfactory and clearlyrepresent the speed of movement of the vehicles, automatically identifying the most representative ranges inreal time.
El volumen de tráfico vehicular de las grandes ciudades se ha incrementado en los últimos a˜nos originando problemas de movilidad, por ello el análisis de los datos del flujo vehicular toma importancia para los investigadores. Los Sistemas Inteligentes de transportación realizan el monitoreo y control vehicular recolectando trayectorias GPS, información que brinda en tiempo real la ubicación geográfica de los vehículos. Su procesamiento por medio de técnicas de agrupamiento permite identificar patrones sobre el flujo vehicular. Este trabajo presenta una metodología capaz de analizar el flujo vehicular en un área dada, identificando los rangos de velocidades y manteniendo actualizado un mapa interactivo que facilita la identificación de zonas de posibles atascos. 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therefore,the analysis of vehicle flow data becomes a relevant research topic. Intelligent Transportation Systems monitor and control vehicular movements by collecting GPS trajectories, which provides the geographic location of vehicles in real time. Thus information is processed using clustering techniques to identify vehicular flow patterns. This work presents a methodologycapable of analyzing the vehicular flow in a given area, identifying speed ranges and keeping an interactivemap updated that facilitates the identification of possible traffic jam areas. The results obtained on threedata sets from the cities of Guayaquil-Ecuador, RomeItaly and Beijing-China are satisfactory and clearlyrepresent the speed of movement of the vehicles, automatically identifying the most representative ranges inreal time.
El volumen de tráfico vehicular de las grandes ciudades se ha incrementado en los últimos a˜nos originando problemas de movilidad, por ello el análisis de los datos del flujo vehicular toma importancia para los investigadores. Los Sistemas Inteligentes de transportación realizan el monitoreo y control vehicular recolectando trayectorias GPS, información que brinda en tiempo real la ubicación geográfica de los vehículos. Su procesamiento por medio de técnicas de agrupamiento permite identificar patrones sobre el flujo vehicular. Este trabajo presenta una metodología capaz de analizar el flujo vehicular en un área dada, identificando los rangos de velocidades y manteniendo actualizado un mapa interactivo que facilita la identificación de zonas de posibles atascos. Los resultados obtenidos sobre tres conjuntos de datos de las ciudades de Guayaquil-Ecuador, Roma-Italia y Beijing-China son satisfactorios y representan claramente la velocidad de desplazamiento de los vehículos identificando de manera automática los rangos más representativos para cada instante de tiempo.</abstract><cop>La Plata</cop><pub>Universidad Nacional de La Plata</pub><doi>10.24215/16666038.22.e11</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0001-5926-8827</orcidid><orcidid>https://orcid.org/0000-0003-1014-1010</orcidid><orcidid>https://orcid.org/0000-0002-3711-1906</orcidid><orcidid>https://orcid.org/0000-0001-7027-7564</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Automatic vehicle identification systems Clustering data stream dynamic clustering Flow distribution Geographical locations Intelligent transportation systems Real time Traffic congestion Traffic jams Traffic volume Trajectories vehicular trajectories |
title | Dynamic grouping of vehicle trajectories |
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