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Methodology for the Identification of Vehicle Congestion Based on Dynamic Clustering

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  • Gary Reyes

    (Carrera de Sistemas Inteligentes, Universidad Bolivariana del Ecuador, Campus Durán Km 5.5 vía Durán Yaguachi, Durán 092405, Ecuador
    Facultad de Ciencias Matemáticas y Físicas, Universidad de Guayaquil, Cdla. Universitaria Salvador Allende, Guayaquil 090514, Ecuador
    These authors contributed equally to this work.)

  • Roberto Tolozano-Benites

    (Carrera de Sistemas Inteligentes, Universidad Bolivariana del Ecuador, Campus Durán Km 5.5 vía Durán Yaguachi, Durán 092405, Ecuador
    These authors contributed equally to this work.)

  • Laura Lanzarini

    (Instituto de Investigación en Informática LIDI (Centro CICPBA), Facultad de Informática, Universidad Nacional de La Plata, Buenos Aires CP1900, Argentina
    These authors contributed equally to this work.)

  • César Estrebou

    (Instituto de Investigación en Informática LIDI (Centro CICPBA), Facultad de Informática, Universidad Nacional de La Plata, Buenos Aires CP1900, Argentina
    These authors contributed equally to this work.)

  • Aurelio F. Bariviera

    (Department of Business & ECO-SOS, Universitat Rovira i Virgili, av. Universitat 1, 43204 Reus, Spain
    These authors contributed equally to this work.)

  • Julio Barzola-Monteses

    (Carrera de Sistemas Inteligentes, Universidad Bolivariana del Ecuador, Campus Durán Km 5.5 vía Durán Yaguachi, Durán 092405, Ecuador
    Facultad de Ciencias Matemáticas y Físicas, Universidad de Guayaquil, Cdla. Universitaria Salvador Allende, Guayaquil 090514, Ecuador
    These authors contributed equally to this work.)

Abstract

Addressing sustainable mobility in urban areas has become a priority in today’s society, given the growing population and increasing vehicular flow in these areas. Intelligent Transportation Systems have emerged as innovative and effective technological solutions for addressing these challenges. Research in this area has become crucial, as it contributes not only to improving mobility in urban areas but also to positively impacting the quality of life of their inhabitants. To address this, a dynamic clustering methodology for vehicular trajectory data is proposed which can provide an accurate representation of the traffic state. Data were collected for the city of San Francisco, a dynamic clustering algorithm was applied and then an indicator was applied to identify areas with traffic congestion. Several experiments were also conducted with different parameterizations of the forgetting factor of the clustering algorithm. We observed that there is an inverse relationship between forgetting and accuracy, and the tolerance allows for a flexible margin of error that allows for better results in precision. The results showed in terms of precision that the dynamic clustering methodology achieved high match rates compared to the congestion indicator applied to static cells.

Suggested Citation

  • Gary Reyes & Roberto Tolozano-Benites & Laura Lanzarini & César Estrebou & Aurelio F. Bariviera & Julio Barzola-Monteses, 2023. "Methodology for the Identification of Vehicle Congestion Based on Dynamic Clustering," Sustainability, MDPI, vol. 15(24), pages 1-18, December.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:24:p:16575-:d:1294616
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    References listed on IDEAS

    as
    1. Reyes, Gary & Lanzarini, Laura & Estrebou, Cesar & Bariviera, Aurelio F., 2022. "Dynamic grouping of vehicle trajectories [Agrupamiento dinámico de trayectorias vehiculares]," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 22(2), pages 141-150.
    2. Hao Zhang & Jing Yang & Gengxin Sun, 2022. "A Case Retrieval Strategy for Traffic Congestion Based on Cluster Analysis," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-8, August.
    3. Shuming Sun & Juan Chen & Jian Sun, 2019. "Traffic congestion prediction based on GPS trajectory data," International Journal of Distributed Sensor Networks, , vol. 15(5), pages 15501477198, May.
    Full references (including those not matched with items on IDEAS)

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