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GPS trajectory clustering method for decision making on intelligent transportation systems

  • Dades identificatives

    Identificador: imarina:6494762
    Autors:
    Reyes GLanzarini LHasperué WBariviera AF
    Resum:
    © 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.
  • Altres:

    Autor segons l'article: Reyes G; Lanzarini L; Hasperué W; Bariviera AF
    Departament: Gestió d'Empreses
    Autor/s de la URV: Fernández Bariviera, Aurelio
    Paraules clau: Segmentation Perspective Intelligent transportation systems Gps trajectories Distance Clustering
    Resum: © 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.
    Àrees temàtiques: 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
    Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
    ISSN: 10641246
    Adreça de correu electrònic de l'autor: aurelio.fernandez@urv.cat
    Identificador de l'autor: 0000-0003-1014-1010
    Data d'alta del registre: 2024-04-27
    Versió de l'article dipositat: info:eu-repo/semantics/acceptedVersion
    Enllaç font original: https://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs179644
    Referència a l'article segons font original: Journal Of Intelligent & Fuzzy Systems. 38 (5): 5529-5535
    Referència 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 Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    DOI de l'article: 10.3233/JIFS-179644
    Entitat: Universitat Rovira i Virgili
    Any de publicació de la revista: 2020
    Tipus de publicació: Journal Publications
  • Paraules clau:

    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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