Author, as appears in the article.: Reyes, Gary; Lanzarini, Laura; Hasperue, Waldo; Bariviera, Aurelio F.;
Department: Gestió d'Empreses
URV's Author/s: Fernández Bariviera, Aurelio
Keywords: 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
Abstract: 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.
Thematic Areas: Transportation science & technology Transportation Mechanical engineering Engineering, civil Engenharias iii Engenharias ii Engenharias i Civil and structural engineering
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/acceptedVersion
Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
Papper original source: Transportation Research Record. 2676 (4): 281-295
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
Entity: Universitat Rovira i Virgili
Journal publication year: 2022
Publication Type: Journal Publications
Repositori URV