Research Article Open Access

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

Michael Cruz1, Hendrik Macedo1 and Adolfo Guimarães2
  • 1 Universidade Federal de Sergipe, Brazil
  • 2 Universidade Tiradentes, Brazil

Abstract

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.

Journal of Computer Science
Volume 14 No. 2, 2018, 199-209

DOI: https://doi.org/10.3844/jcssp.2018.199.209

Submitted On: 22 May 2017 Published On: 3 February 2018

How to Cite: Cruz, M., Macedo, H. & Guimarães, A. (2018). Measuring the Relevance of Trajectory Matching and Profile Matching in the Context of Carpooling Computational Systems. Journal of Computer Science, 14(2), 199-209. https://doi.org/10.3844/jcssp.2018.199.209

  • 2,727 Views
  • 1,493 Downloads
  • 0 Citations

Download

Keywords

  • Carpooling
  • Trajectory Similarity
  • User Profile Similarity
  • Clustering