Dynamic grouping of vehicle trajectories

• Gary Reyes ^[1] ; Laura Lanzarini ^[2] ; Cesar Estrebou ^[2] ; Aurelio Fernandez Bariviera ^[3]
  1. [1] Universidad de Guayaquil

     Universidad de Guayaquil

     Guayaquil, Ecuador

     
  2. [2] Universidad Nacional de La Plata

     Universidad Nacional de La Plata

     Argentina

     
  3. [3] Universitat Rovira i Virgili

     Universitat Rovira i Virgili

     Tarragona, España

     

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• Localización: Journal of Computer Science and Technology, ISSN-e 1666-6038, Vol. 22, Nº. 2, 2022
• Idioma: inglés
• DOI: 10.24215/16666038.22.e11
• Enlaces
  • Texto completo
• Resumen
  • español

    El volumen de tráfico vehicular de las grandes ciudades se ha incrementado en los últimos años originando problemas de movilidad, por ello el análisis de los datos del flujo vehicular toma importancia para los investigadores. Los Sistemas Inteligentes de transportación realizan el monitoreo y control vehicular recolectando trayectorias GPS, información que brinda en tiempo real la ubicación geográfica de los vehículos. Su procesamiento por medio de técnicas de agrupamiento permite identificar patrones sobre el flujo vehicular. Este trabajo presenta una metodología capaz de analizar el flujo vehicular en un área dada, identificando los rangos de velocidades y manteniendo actualizado un mapa interactivo que facilita la identificación de zonas de posibles atascos. Los resultados obtenidos sobre tres conjuntos de datos de las ciudades de Guayaquil-Ecuador, Roma-Italia y Beijing-China son satisfactorios y representan claramente la velocidad de desplazamiento de los vehículos identificando de manera automática los rangos más representativos para cada instante de tiempo.

  • English

    Vehicular traffic volume in large cities has increased in recent years, causing mobility problems; therefore, the analysis of vehicle flow data becomes a relevant research topic. Intelligent Transportation Systems monitor and control vehicular movements by collecting GPS trajectories, which provides the geographic location of vehicles in real time. Thus information is processed using clustering techniques to identify vehicular flow patterns. This work presents a methodology capable of analyzing the vehicular flow in a given area, identifying speed ranges and keeping an interactive map updated that facilitates the identification of possible traffic jam areas. The results obtained on three data sets from the cities of Guayaquil-Ecuador, RomeItaly and Beijing-China are satisfactory and clearly represent the speed of movement of the vehicles, automatically identifying the most representative ranges in real time.

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