Articles producció científica> Gestió d'Empreses

Method for the Identification and Classification of Zones with Vehicular Congestion

  • Dades identificatives

    Identificador: imarina:9366389
    Autors:
    Reyes, GaryTolozano-Benites, RobertoLanzarini, LauraEstrebou, CesarBariviera, Aurelio FBarzola-Monteses, Julio
    Resum:
    Persistently, urban regions grapple with the ongoing challenge of vehicular traffic, a predicament fueled by the incessant expansion of the population and the rise in the number of vehicles on the roads. The recurring challenge of vehicular congestion casts a negative influence on urban mobility, thereby diminishing the overall quality of life of residents. It is hypothesized that a dynamic clustering method of vehicle trajectory data can provide an accurate and up-to-date representation of real-time traffic behavior. To evaluate this hypothesis, data were collected from three different cities: San Francisco, Rome, and Guayaquil. A dynamic clustering algorithm was applied to identify traffic congestion patterns, and an indicator was applied to identify and evaluate the congestion conditions of the areas. The findings indicate a heightened level of precision and recall in congestion classification when contrasted with an approach relying on static cells.
  • Altres:

    Autor segons l'article: Reyes, Gary; Tolozano-Benites, Roberto; Lanzarini, Laura; Estrebou, Cesar; Bariviera, Aurelio F; Barzola-Monteses, Julio
    Departament: Gestió d'Empreses
    Autor/s de la URV: Fernández Bariviera, Aurelio
    Paraules clau: Road networks Patterns Gps trajectories Dynamic clustering Congestion Classification
    Resum: Persistently, urban regions grapple with the ongoing challenge of vehicular traffic, a predicament fueled by the incessant expansion of the population and the rise in the number of vehicles on the roads. The recurring challenge of vehicular congestion casts a negative influence on urban mobility, thereby diminishing the overall quality of life of residents. It is hypothesized that a dynamic clustering method of vehicle trajectory data can provide an accurate and up-to-date representation of real-time traffic behavior. To evaluate this hypothesis, data were collected from three different cities: San Francisco, Rome, and Guayaquil. A dynamic clustering algorithm was applied to identify traffic congestion patterns, and an indicator was applied to identify and evaluate the congestion conditions of the areas. The findings indicate a heightened level of precision and recall in congestion classification when contrasted with an approach relying on static cells.
    Àrees temàtiques: Zootecnia / recursos pesqueiros Saúde coletiva Remote sensing Medicina veterinaria Geography, planning and development Geography, physical Geografía Geociências Engenharias i Earth and planetary sciences (miscellaneous) Computers in earth sciences Computer science, information systems Ciencias sociales Ciências ambientais Ciências agrárias i Ciência da computação Biodiversidade
    Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
    Adreça de correu electrònic de l'autor: aurelio.fernandez@urv.cat
    Identificador de l'autor: 0000-0003-1014-1010
    Data d'alta del registre: 2024-08-03
    Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
    Enllaç font original: https://www.mdpi.com/2220-9964/13/3/73
    URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referència a l'article segons font original: Isprs International Journal Of Geo-Information. 13 (3): 73-
    Referència de l'ítem segons les normes APA: Reyes, Gary; Tolozano-Benites, Roberto; Lanzarini, Laura; Estrebou, Cesar; Bariviera, Aurelio F; Barzola-Monteses, Julio (2024). Method for the Identification and Classification of Zones with Vehicular Congestion. Isprs International Journal Of Geo-Information, 13(3), 73-. DOI: 10.3390/ijgi13030073
    DOI de l'article: 10.3390/ijgi13030073
    Entitat: Universitat Rovira i Virgili
    Any de publicació de la revista: 2024
    Tipus de publicació: Journal Publications
  • Paraules clau:

    Computer Science, Information Systems,Computers in Earth Sciences,Earth and Planetary Sciences (Miscellaneous),Geography, Physical,Geography, Planning and Development,Remote Sensing
    Road networks
    Patterns
    Gps trajectories
    Dynamic clustering
    Congestion
    Classification
    Zootecnia / recursos pesqueiros
    Saúde coletiva
    Remote sensing
    Medicina veterinaria
    Geography, planning and development
    Geography, physical
    Geografía
    Geociências
    Engenharias i
    Earth and planetary sciences (miscellaneous)
    Computers in earth sciences
    Computer science, information systems
    Ciencias sociales
    Ciências ambientais
    Ciências agrárias i
    Ciência da computação
    Biodiversidade
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