Articles producció científica> Gestió d'Empreses

Proposal for a Pivot-Based Vehicle Trajectory Clustering Method

  • Datos identificativos

    Identificador: imarina:9241751
    Autores:
    Reyes, GaryLanzarini, LauraHasperue, WaldoBariviera, Aurelio F.
    Resumen:
    Given the large volume of georeferenced information generated and stored by many types of devices, the study and improvement of techniques capable of operating with these data is an area of great interest. The analysis of vehicular trajectories with the aim of forming clusters and identifying emerging patterns is very useful for characterizing and analyzing transportation flows in cities. This paper presents a new trajectory clustering method capable of identifying clusters of vehicular sub-trajectories in various sectors of a city. The proposed method is based on the use of an auxiliary structure to determine the correct location of the centroid of each group or set of sub-trajectories along the adaptive process. The proposed method was applied on three real databases, as well as being compared with other relevant methods, achieving satisfactory results and showing good cluster quality according to the Silhouette index.
  • Otros:

    Autor según el artículo: Reyes, Gary; Lanzarini, Laura; Hasperue, Waldo; Bariviera, Aurelio F.;
    Departamento: Gestió d'Empreses
    Autor/es de la URV: Fernández Bariviera, Aurelio
    Palabras clave: Time Similarity Perspective Patterns Pattern recognition Machine learning (artificial intelligence) Gps data Geographic information science Distance Data and data science Artificial intelligence and advanced computing applications
    Resumen: Given the large volume of georeferenced information generated and stored by many types of devices, the study and improvement of techniques capable of operating with these data is an area of great interest. The analysis of vehicular trajectories with the aim of forming clusters and identifying emerging patterns is very useful for characterizing and analyzing transportation flows in cities. This paper presents a new trajectory clustering method capable of identifying clusters of vehicular sub-trajectories in various sectors of a city. The proposed method is based on the use of an auxiliary structure to determine the correct location of the centroid of each group or set of sub-trajectories along the adaptive process. The proposed method was applied on three real databases, as well as being compared with other relevant methods, achieving satisfactory results and showing good cluster quality according to the Silhouette index.
    Áreas temáticas: Transportation science & technology Transportation Mechanical engineering Engineering, civil Engenharias iii Engenharias ii Engenharias i Civil and structural engineering
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    Direcció de correo del autor: aurelio.fernandez@urv.cat
    Identificador del autor: 0000-0003-1014-1010
    Fecha de alta del registro: 2024-09-07
    Versión del articulo depositado: info:eu-repo/semantics/acceptedVersion
    Enlace a la fuente original: https://journals.sagepub.com/doi/abs/10.1177/03611981211058429?journalCode=trra
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referencia al articulo segun fuente origial: Transportation Research Record. 2676 (4): 281-295
    Referencia de l'ítem segons les normes APA: Reyes, Gary; Lanzarini, Laura; Hasperue, Waldo; Bariviera, Aurelio F.; (2022). Proposal for a Pivot-Based Vehicle Trajectory Clustering Method. Transportation Research Record, 2676(4), 281-295. DOI: 10.1177/03611981211058429
    DOI del artículo: 10.1177/03611981211058429
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2022
    Tipo de publicación: Journal Publications
  • Palabras clave:

    Civil and Structural Engineering,Engineering, Civil,Mechanical Engineering,Transportation,Transportation Science & Technology
    Time
    Similarity
    Perspective
    Patterns
    Pattern recognition
    Machine learning (artificial intelligence)
    Gps data
    Geographic information science
    Distance
    Data and data science
    Artificial intelligence and advanced computing applications
    Transportation science & technology
    Transportation
    Mechanical engineering
    Engineering, civil
    Engenharias iii
    Engenharias ii
    Engenharias i
    Civil and structural engineering
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