A trajectory correlation algorithm based on users' daily routines

Abstract

In recent years, there has been a change in society behavior regarding the way people interact with each other. Particularly, there is a tendency for people to change from real to virtual communities. Consequently, social opportunities are frequently missed because users have to describe manually their daily routines on virtual communities. Nevertheless, mobile social applications emerge to improve social connectivity in real communities by capturing context information of people, points of interest, and places taking into account user trajectories. In this paper, we present a Trajectory Correlation Algorithm based on Users' Daily Routines. The key idea is to provide a solution to capture daily routines in order to find related information of users and, consequently, increase social interactions in real communities. We introduce an algorithm to execute the trajectory correlation process, taking into account daily trajectories and points of interest of users. To validate our proposal, we implemented and tested a mobile social application for tracking daily routines. Besides that, we developed a plug-in on a virtual community platform to execute an optimized trajectory correlation algorithm, which is based on Minimum Bounding Rectangles (MBRs) and Hausdorff Distance. The results show that our proposal is efficient to increase social interactions in real communities by using a mobile social application and a well-known social network platform.