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GPS trajectory clustering method for decision making on intelligent transportation systems

  • Datos identificativos

    Identificador: imarina:6494762
    Autores:
    Reyes GLanzarini LHasperué WBariviera AF
    Resumen:
    © 2020 - IOS Press and the authors. All rights reserved. Technological progress facilitates recording and collecting information on vehicles' GPS trajectories on public roads. The intelligent analysis of this data leads to the identification of extremely useful patterns when making decisions in situations related to urbanism, traffic and road congestion, among others. This article presents a GPS trajectory clustering method that uses angular information to segment the trajectories and a similarity function guided by a pivot. In order to initialize the process, it is proposed to segment the region to be analyzed in a uniform way forming a grid. The obtained results after applying the proposed method on a real trajectories database are satisfactory and show significant improvement in comparison with the methods published in the bibliography.
  • Otros:

    Autor según el artículo: Reyes G; Lanzarini L; Hasperué W; Bariviera AF
    Departamento: Gestió d'Empreses
    Autor/es de la URV: Fernández Bariviera, Aurelio
    Palabras clave: Segmentation Perspective Intelligent transportation systems Gps trajectories Distance Clustering
    Resumen: © 2020 - IOS Press and the authors. All rights reserved. Technological progress facilitates recording and collecting information on vehicles' GPS trajectories on public roads. The intelligent analysis of this data leads to the identification of extremely useful patterns when making decisions in situations related to urbanism, traffic and road congestion, among others. This article presents a GPS trajectory clustering method that uses angular information to segment the trajectories and a similarity function guided by a pivot. In order to initialize the process, it is proposed to segment the region to be analyzed in a uniform way forming a grid. The obtained results after applying the proposed method on a real trajectories database are satisfactory and show significant improvement in comparison with the methods published in the bibliography.
    Áreas temáticas: Statistics and probability Interdisciplinar General engineering Ensino Engineering (miscellaneous) Engineering (all) Engenharias iv Engenharias iii Economia Computer science, artificial intelligence Ciências ambientais Ciência da computação Biotecnología Artificial intelligence Administração pública e de empresas, ciências contábeis e turismo
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    ISSN: 10641246
    Direcció de correo del autor: aurelio.fernandez@urv.cat
    Identificador del autor: 0000-0003-1014-1010
    Fecha de alta del registro: 2024-04-27
    Versión del articulo depositado: info:eu-repo/semantics/acceptedVersion
    Enlace a la fuente original: https://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs179644
    Referencia al articulo segun fuente origial: Journal Of Intelligent & Fuzzy Systems. 38 (5): 5529-5535
    Referencia de l'ítem segons les normes APA: Reyes G; Lanzarini L; Hasperué W; Bariviera AF (2020). GPS trajectory clustering method for decision making on intelligent transportation systems. Journal Of Intelligent & Fuzzy Systems, 38(5), 5529-5535. DOI: 10.3233/JIFS-179644
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    DOI del artículo: 10.3233/JIFS-179644
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2020
    Tipo de publicación: Journal Publications
  • Palabras clave:

    Artificial Intelligence,Computer Science, Artificial Intelligence,Engineering (Miscellaneous),Statistics and Probability
    Segmentation
    Perspective
    Intelligent transportation systems
    Gps trajectories
    Distance
    Clustering
    Statistics and probability
    Interdisciplinar
    General engineering
    Ensino
    Engineering (miscellaneous)
    Engineering (all)
    Engenharias iv
    Engenharias iii
    Economia
    Computer science, artificial intelligence
    Ciências ambientais
    Ciência da computação
    Biotecnología
    Artificial intelligence
    Administração pública e de empresas, ciências contábeis e turismo
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