Articles producció científica> Gestió d'Empreses

Methodology for the Identification of Vehicle Congestion Based on Dynamic Clustering

  • Dades identificatives

    Identificador: imarina:9333894
    Autors:
    Reyes, GTolozano-Benites, RLanzarini, LEstrebou, CBariviera, AFBarzola-Monteses, J
    Resum:
    Addressing sustainable mobility in urban areas has become a priority in today's society, given the growing population and increasing vehicular flow in these areas. Intelligent Transportation Systems have emerged as innovative and effective technological solutions for addressing these challenges. Research in this area has become crucial, as it contributes not only to improving mobility in urban areas but also to positively impacting the quality of life of their inhabitants. To address this, a dynamic clustering methodology for vehicular trajectory data is proposed which can provide an accurate representation of the traffic state. Data were collected for the city of San Francisco, a dynamic clustering algorithm was applied and then an indicator was applied to identify areas with traffic congestion. Several experiments were also conducted with different parameterizations of the forgetting factor of the clustering algorithm. We observed that there is an inverse relationship between forgetting and accuracy, and the tolerance allows for a flexible margin of error that allows for better results in precision. The results showed in terms of precision that the dynamic clustering methodology achieved high match rates compared to the congestion indicator applied to static cells.
  • Altres:

    Autor segons l'article: Reyes, G; Tolozano-Benites, R; Lanzarini, L; Estrebou, C; Bariviera, AF; Barzola-Monteses, J
    Departament: Gestió d'Empreses
    Autor/s de la URV: Fernández Bariviera, Aurelio
    Paraules clau: Road networks Patterns Gps trajectories Flow Dynamic clustering Congestion
    Resum: Addressing sustainable mobility in urban areas has become a priority in today's society, given the growing population and increasing vehicular flow in these areas. Intelligent Transportation Systems have emerged as innovative and effective technological solutions for addressing these challenges. Research in this area has become crucial, as it contributes not only to improving mobility in urban areas but also to positively impacting the quality of life of their inhabitants. To address this, a dynamic clustering methodology for vehicular trajectory data is proposed which can provide an accurate representation of the traffic state. Data were collected for the city of San Francisco, a dynamic clustering algorithm was applied and then an indicator was applied to identify areas with traffic congestion. Several experiments were also conducted with different parameterizations of the forgetting factor of the clustering algorithm. We observed that there is an inverse relationship between forgetting and accuracy, and the tolerance allows for a flexible margin of error that allows for better results in precision. The results showed in terms of precision that the dynamic clustering methodology achieved high match rates compared to the congestion indicator applied to static cells.
    Àrees temàtiques: Zootecnia / recursos pesqueiros Renewable energy, sustainability and the environment Ren Medicina i Management, monitoring, policy and law Interdisciplinar Historia Hardware and architecture Green & sustainable science & technology Geography, planning and development Geografía Geociências Environmental studies Environmental sciences Environmental science (miscellaneous) Ensino Engenharias iii Engenharias ii Engenharias i Enfermagem Energy engineering and power technology Education Computer science (miscellaneous) Computer networks and communications Ciências agrárias i Building and construction Biotecnología Biodiversidade Arquitetura, urbanismo e design Arquitetura e urbanismo
    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/2071-1050/15/24/16575
    URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referència a l'article segons font original: Sustainability. 15 (24):
    Referència de l'ítem segons les normes APA: Reyes, G; Tolozano-Benites, R; Lanzarini, L; Estrebou, C; Bariviera, AF; Barzola-Monteses, J (2023). Methodology for the Identification of Vehicle Congestion Based on Dynamic Clustering. Sustainability, 15(24), -. DOI: 10.3390/su152416575
    DOI de l'article: 10.3390/su152416575
    Entitat: Universitat Rovira i Virgili
    Any de publicació de la revista: 2023
    Tipus de publicació: Journal Publications
  • Paraules clau:

    Computer Networks and Communications,Education,Energy Engineering and Power Technology,Environmental Science (Miscellaneous),Environmental Sciences,Environmental Studies,Geography, Planning and Development,Green & Sustainable Science & Technology,Hardware and Architecture,Management, Monitoring, Policy and Law,Ren,Renewable Energy, Sustain
    Road networks
    Patterns
    Gps trajectories
    Flow
    Dynamic clustering
    Congestion
    Zootecnia / recursos pesqueiros
    Renewable energy, sustainability and the environment
    Ren
    Medicina i
    Management, monitoring, policy and law
    Interdisciplinar
    Historia
    Hardware and architecture
    Green & sustainable science & technology
    Geography, planning and development
    Geografía
    Geociências
    Environmental studies
    Environmental sciences
    Environmental science (miscellaneous)
    Ensino
    Engenharias iii
    Engenharias ii
    Engenharias i
    Enfermagem
    Energy engineering and power technology
    Education
    Computer science (miscellaneous)
    Computer networks and communications
    Ciências agrárias i
    Building and construction
    Biotecnología
    Biodiversidade
    Arquitetura, urbanismo e design
    Arquitetura e urbanismo
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