No abstract

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 methodologycapable of analyzing the vehicular flow in a given area, identifying speed ranges and keeping an interactivemap updated that facilitates the identification of possible traffic jam areas. The results obtained on threedata sets from the cities of Guayaquil-Ecuador, RomeItaly and Beijing-China are satisfactory and clearlyrepresent the speed of movement of the vehicles, automatically identifying the most representative ranges inreal time.

No abstract

Given the large volume of georeferenced information generated and stored by many types of devices, the study and improvement of techniques capable of operating with these data is an area of great interest. The analysis of vehicular trajectories with the aim of forming clusters and identifying emerging patterns is very useful for characterizing and analyzing transportation flows in cities. This paper presents a new trajectory clustering method capable of identifying clusters of vehicular sub-trajectories in various sectors of a city. The proposed method is based on the use of an auxiliary structure to determine the correct location of the centroid of each group or set of sub-trajectories along the adaptive process. The proposed method was applied on three real databases, as well as being compared with other relevant methods, achieving satisfactory results and showing good cluster quality according to the Silhouette index.