Data Mining and Knowledge Discovery

, Volume 30, Issue 6, pp 1480–1519

Online route prediction based on clustering of meaningful velocity-change areas

  • Fernando Terroso-Saenz
  • Mercedes Valdes-Vela
  • Antonio F. Skarmeta-Gomez
Article

DOI: 10.1007/s10618-016-0452-3

Cite this article as:
Terroso-Saenz, F., Valdes-Vela, M. & Skarmeta-Gomez, A.F. Data Min Knowl Disc (2016) 30: 1480. doi:10.1007/s10618-016-0452-3
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Abstract

Personal route prediction has emerged as an important topic within the mobility mining domain. In this context, many proposals apply an off-line learning process before being able to run the on-line prediction algorithm. The present work introduces a novel framework that integrates the route learning and the prediction algorithm in an on-line manner. By means of a thin-client and server architecture, it also puts forward a new concept for route abstraction based on the detection of spatial regions where certain velocity features of routes frequently change. The proposal is evaluated by real-world and synthetic datasets and compared with a well-established mechanism by exhibiting quite promising results.

Keywords

Route prediction Density-based clustering Mobility mining 

Copyright information

© The Author(s) 2016

Authors and Affiliations

  • Fernando Terroso-Saenz
    • 1
  • Mercedes Valdes-Vela
    • 1
  • Antonio F. Skarmeta-Gomez
    • 1
  1. 1.Department of Information and Communication Engineering, Faculty of Computer ScienceUniversity of MurciaMurciaSpain

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