000			01506nam a2200205 a 4500
003			AR-LpUFIB
005			20250312104717.0
008			230201nuuuu xx o 000 0 ||| d
024	8		_aDIF-M8783
040			_aAR-LpUFIB _bspa _cAR-LpUFIB
100	1		_aReyes, Gary
245	1	0	_aDynamic grouping of vehicle trajectories
260			_a[S.l.]: _b[S.n.]
520			_aVehicular 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			_aJournal of Computer Science & Technology, 2022, 22(2), pp. 141-150.
650		4	_aFLUJO DE DATOS
653			_atrayectorias vehiculares
700	1		_aLanzarini, Laura Cristina
942			_cAR _2udc
999			_c57823 _d57823