Evaluation of a data flow clustering algorithm for GPS trajectory analysis

Keywords: Clustering, GPS Trajectories, Algorithms, Data flow

Abstract

The large volume of data generated today by various GPS devices makes the analysis of this data a field of current research interest. Clustering algorithms identify patterns in a massive set of data. In the field of transportation, in the case of vehicles circulating through the streets of a city, these algorithms help to identify congestion, common traffic flows, frequent events such as vehicle stops, among others. A GPS trajectory is defined by a set of geographic locations, each of which is represented by its latitude and longitude at an instant in time. There are vehicular GPS trajectories that are nowadays generated by intelligent transportation systems in cities. This work evaluates a data flow clustering algorithm called Dyclee in the processing and analysis of two data sets from the cities of Guayaquil-Ecuador and Rome-Italy. The results obtained from the evaluation of the two data sets using Dyclee allow the identification of vehicular patterns that occur at different time instants, being able to show common speed ranges. In addition, measurements are made to determine the quality of the resulting groups, the results obtained are satisfactory.

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Published
2022-09-30
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Abstract 55
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How to Cite
Reyes Zambrano, G., Zambrano Meza, E. S., León-Granizo, O., & Crespo León, C. (2022). Evaluation of a data flow clustering algorithm for GPS trajectory analysis. Ecuadorian Science Journal, 6(2), 33-45. https://doi.org/10.46480/esj.6.2.99
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Research Paper
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