Data Mining and Knowledge Discovery

, Volume 30, Issue 6, pp 1480–1519 | Cite as

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

  • Fernando Terroso-SaenzEmail author
  • Mercedes Valdes-Vela
  • Antonio F. Skarmeta-Gomez


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.


Route prediction Density-based clustering Mobility mining 



This research is partially funded by the Spanish Ministry of Economy and Competitiveness’ project “Dynamic and Emergent intelligent for Smart Cities based on Internet of Things” TIN2014-52099-R and the European Commission through the ENTROPY-649849 EU Project.


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Copyright information

© The Author(s) 2016

Authors and Affiliations

  • Fernando Terroso-Saenz
    • 1
    Email author
  • 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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