Measuring the Relevance of Trajectory Matching and Profile Matching in the Context of Carpooling Computational Systems

Carpooling consists of sharing individual vehicle space among people with comparable trajectories. Although there are some software initiatives to help carpooling practice, none of them really implements features similarly to searching for people with similar trajectories and profile. In this study, we propose an innovative approach to generate clusters of users that share similar trajectories and profile for carpooling purposes based on Optics, K-means algorithm and ensemble learning. First, we provide a proper definition of fundamental elements of the carpooling context in order to contribute to a standardization of the concerning nomenclatures. Next, we perform four different experiments for the purpose of showing the feasibility of the approach. We also contribute to the construction of a real dataset (donated to UCI), properly depicted, used in two of these experiments. Results with Davies-Boulding index indicate that the generated clusters are feasible to the design of a carpooling recommendation system. Time performance evaluation of the approach has been also performed for both dynamic program analyses via software profiling method and time complexity analysis according to Big O notation.