Grouping Similar Trajectories for Carpooling Purposes

Abstract

Vehicle congestion is a serious concern in metropolitan areas. Some policies have been adopted in order to soften the problem: construction of alternative routes, encouragement for the use of bicycles, improvement on public transportation, among others. A practice that might help is carpooling. Carpooling consists in sharing private vehicle space among people with similar trajectories. Although there exist some software initiatives to facilitate the carpooling practice, none of them actually provides some key facilities such as searching for people with similar trajectories. The way in which such a trajectory is represented is also central. In the specific context of carpooling, the use of Points of Interest (POI) as a method for trajectory discretization is rather relevant. In this paper, we consider that and other assumptions to propose an innovative approach to generate trajectory clusters for carpooling purposes, based on Optics algorithm. We also propose a new similarity measure for trajectories. Two experiments have been performed in order to prove the feasibility of the approach. Furthermore, we compare our approach with K-means and Optics. Results have showed that the proposed approach has results similar for Davies-Boulding index (DBI).