Articles producció científica> Gestió d'Empreses

Dynamic grouping of vehicle trajectories

  • Identification data

    Identifier: imarina:9283416
    Authors:
    Reyes GLanzarini LEstrebou CBariviera A
    Abstract:
    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 mon-itor and control vehicular movements by collecting GPS trajectories, which provides the geographic lo-cation of vehicles in real time. Thus information is processed using clustering techniques to identify vehicular flow patterns. This work presents a methodology capable of analyzing the vehicular flow in a given area, identifying speed ranges and keeping an interactive map updated that facilitates the identification of pos-sible traffic jam areas. The obtained results on three data sets from the cities of Guayaquil-Ecuador, Rome-Italy and Beijing-China are satisfactory and clearly represent the speed of movement of the vehicles, auto-matically identifying the most representative ranges in real time.
  • Others:

    Author, as appears in the article.: Reyes G; Lanzarini L; Estrebou C; Bariviera A
    Department: Gestió d'Empreses
    URV's Author/s: Fernández Bariviera, Aurelio
    Keywords: Vehicular trajectories Dynamic clustering Data stream
    Abstract: 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 mon-itor and control vehicular movements by collecting GPS trajectories, which provides the geographic lo-cation of vehicles in real time. Thus information is processed using clustering techniques to identify vehicular flow patterns. This work presents a methodology capable of analyzing the vehicular flow in a given area, identifying speed ranges and keeping an interactive map updated that facilitates the identification of pos-sible traffic jam areas. The obtained results on three data sets from the cities of Guayaquil-Ecuador, Rome-Italy and Beijing-China are satisfactory and clearly represent the speed of movement of the vehicles, auto-matically identifying the most representative ranges in real time.
    Thematic Areas: Software Hardware and architecture Computer vision and pattern recognition Computer science, artificial intelligence Computer science applications Computer science (miscellaneous) Artificial intelligence
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: aurelio.fernandez@urv.cat
    Author identifier: 0000-0003-1014-1010
    Record's date: 2024-09-07
    Papper version: info:eu-repo/semantics/publishedVersion
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: Journal Of Computer Science & Technology (Jcs&t). 22 (2): 141-150
    APA: Reyes G; Lanzarini L; Estrebou C; Bariviera A (2022). Dynamic grouping of vehicle trajectories. Journal Of Computer Science & Technology (Jcs&t), 22(2), 141-150. DOI: 10.24215/16666038.22.e11
    Article's DOI: 10.24215/16666038.22.e11
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2022
    Publication Type: Journal Publications
  • Keywords:

    Artificial Intelligence,Computer Science (Miscellaneous),Computer Science Applications,Computer Science, Artificial Intelligence,Computer Vision and Pattern Recognition,Hardware and Architecture,Software
    Vehicular trajectories
    Dynamic clustering
    Data stream
    Software
    Hardware and architecture
    Computer vision and pattern recognition
    Computer science, artificial intelligence
    Computer science applications
    Computer science (miscellaneous)
    Artificial intelligence
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