Gary ReyesRoberto Tolozano-BenitesLaura Cristina LanzariniCésar Armando EstrebouAurelio F. BarivieraJulio Barzola–Monteses2026-03-242026-03-24202410.3390/ijgi13030073https://doi.org/10.3390/ijgi13030073https://andeanlibrary.org/handle/123456789/100068Citaciones: 1Persistently, urban regions grapple with the ongoing challenge of vehicular traffic, a predicament fueled by the incessant expansion of the population and the rise in the number of vehicles on the roads. The recurring challenge of vehicular congestion casts a negative influence on urban mobility, thereby diminishing the overall quality of life of residents. It is hypothesized that a dynamic clustering method of vehicle trajectory data can provide an accurate and up-to-date representation of real-time traffic behavior. To evaluate this hypothesis, data were collected from three different cities: San Francisco, Rome, and Guayaquil. A dynamic clustering algorithm was applied to identify traffic congestion patterns, and an indicator was applied to identify and evaluate the congestion conditions of the areas. The findings indicate a heightened level of precision and recall in congestion classification when contrasted with an approach relying on static cells.enIdentification (biology)Computer scienceBiologyarticle