Dynamic grouping of vehicle trajectories

Detalles Bibliográficos
Autor Principal: Reyes, Gary
Otros autores o Colaboradores: Lanzarini, Laura Cristina
Formato: Libro
Datos de publicación: [S.l.]: [S.n.]
Temas:
Acceso en línea:Consultar en el Cátalogo
Resumen: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 obtained results on three data sets from the cities of Guayaquil-Ecuador, Rome- Italy and Beijing-China are satisfactory and clearly represent the speed of movement of the vehicles, automatically identifying the most representative ranges in real time.

MARC

LEADER 00000nam a2200000 a 4500
003 AR-LpUFIB
005 20250312104717.0
008 230201nuuuu xx o 000 0 ||| d
024 8 |a DIF-M8783 
040 |a AR-LpUFIB  |b spa  |c AR-LpUFIB 
100 1 |a Reyes, Gary 
245 1 0 |a Dynamic grouping of vehicle trajectories 
260 |a [S.l.]:  |b [S.n.] 
520 |a 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 obtained results on three data sets from the cities of Guayaquil-Ecuador, Rome- Italy and Beijing-China are satisfactory and clearly represent the speed of movement of the vehicles, automatically identifying the most representative ranges in real time. 
534 |a Journal of Computer Science & Technology, 2022, 22(2), pp. 141-150. 
650 4 |a FLUJO DE DATOS 
653 |a trayectorias vehiculares 
700 1 |a Lanzarini, Laura Cristina 
942 |c AR  |2 udc 
952 |0 0  |1 0  |2 udc  |4 0  |7 3  |8 BD  |9 85046  |a DIF  |b DIF  |d 2025-03-12  |l 0  |r 2025-03-12 10:48:15  |u https://doi.org/10.24215/16666038.22.e11  |w 2025-03-12  |y AR 
999 |c 57823  |d 57823