Affiliations: [a] Universidad de Guayaquil, Facultad de Ciencias Matemáticas y Físicas, Casilla Postal 471 Guayaquil, Ecuador
| [b] Universidad Nacional de La Plata, Facultad de Informática, Instituto de Investigación en Informática LIDI (Centro CICPBA) 1900 La Plata, Buenos Aires, Argentina
| [c] Investigador Asociado – Comisión de Investigaciones Científicas (CIC)
| [d] Universitat Rovira i Virgili, Department of Business, Av. Universitat 43204 Reus, Spain
Correspondence:
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Corresponding author. Aurelio F. Bariviera, Universitat Rovira i Virgili, Department of Business, Av. Universitat 43204 Reus, Spain. E-mail: aurelio.fernandez@urv.cat.
Abstract: 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.
Keywords: Segmentation, clustering, GPS trajectories, intelligent transportation systems
