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Data Stream Processing Method for Clustering of Trajectories

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Technologies and Innovation (CITI 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1658))

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Abstract

The constant advances in techniques for recording and collecting GPS trajectory information, the increase in the number of devices that collect this type of information such as video cameras, traffic sensors, smart phones, etc., has resulted in a large volume of information. Being able to process this information through data streams that allow intelligent analysis of the data in real time is an area where many researchers are currently making efforts to identify solutions. GPS trajectory clustering techniques allow the identification of vehicle patterns over large volumes of data. This paper presents a method that processes data streams for dynamic clustering of vehicular GPS trajectories. The proposed method here receives a GPS data stream, processes it using a buffer memory and the creation of a grid with the use of indexes, and subsequently analyzes each cell of the grid with the use of a dynamic clustering technique that extracts the characteristics of reduced zones of the study area, visualizing common speed ranges in interactive maps. To validate the proposed method, two data sets from Rome-Italy and Guayaquil-Ecuador were used, and measurements were made of execution time, used memory and silhouette coefficient. The obtained results are satisfactory.

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Notes

  1. 1.

    Guayaquil dataset is available at https://github.com/gary-reyes-zambrano/Guayaquil-DataSet.

  2. 2.

    Roma dataset available at https://github.com/gary-reyes-zambrano/Roma-Dataset.

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Author information

Authors and Affiliations

  1. Facultad de Ciencias Matemáticas y Físicas, Universidad de Guayaquil, Cdla. Universitaria Salvador Allende, Guayaquil, 090514, Ecuador

    Gary Reyes

  2. Facultad de Informática, Instituto de Investigación en Informática LIDI (Centro CICPBA) 1900 La Plata, Universidad Nacional de La Plata, Buenos Aires, Argentina

    Laura Lanzarini & César Estrebou

  3. Department of Business, Universitat Rovira i Virgili, Reus, Spain

    Aurelio Bariviera

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  1. Gary Reyes
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  2. Laura Lanzarini
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  3. César Estrebou
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  4. Aurelio Bariviera
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Corresponding author

Correspondence to Gary Reyes .

Editor information

Editors and Affiliations

  1. Universidad de Murcia, Murcia, Spain

    Rafael Valencia-García

  2. Universidad Agraria del Ecuador, Guayaquil, Ecuador

    Martha Bucaram-Leverone

  3. Universidad Agraria del Ecuador, Guayaquil, Ecuador

    Javier Del Cioppo-Morstadt

  4. Universidad Agraria del Ecuador, Guayaquil, Ecuador

    Néstor Vera-Lucio

  5. Universidad Agraria del Ecuador, Guayaquil, Ecuador

    Emma Jácome-Murillo

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Reyes, G., Lanzarini, L., Estrebou, C., Bariviera, A. (2022). Data Stream Processing Method for Clustering of Trajectories. In: Valencia-García, R., Bucaram-Leverone, M., Del Cioppo-Morstadt, J., Vera-Lucio, N., Jácome-Murillo, E. (eds) Technologies and Innovation. CITI 2022. Communications in Computer and Information Science, vol 1658. Springer, Cham. https://doi.org/10.1007/978-3-031-19961-5_11

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  • DOI: https://doi.org/10.1007/978-3-031-19961-5_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-19960-8

  • Online ISBN: 978-3-031-19961-5

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