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On-Line Location Prediction Exploiting Spatial and Velocity Context

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Abstract

We treat the problem of movement prediction as a classification task. We assume the existence of a (gradually populated and/or trained) knowledge base and try to compare the movement pattern of a certain object with stored information in order to predict its future location. We introduce a novel distance metric function based on weighted spatial and velocity context used for location prediction. The proposed distance metric is compared with other distance metrics in the literature on real traffic data and reveals its superiority.

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Acknowledgments

The research was carried out with the financial support of the Ministry of Education and Science of the Russian Federation under grant agreement #14.575.21.0058.

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Authors and Affiliations

  1. Department of Infocommunication Technologies, ITMO University, Kronverkskiy Pr. 49, 197101, St. Petersburg, Russia

    Theodoros Anagnostopoulos & Arkady Zaslavsky

  2. School of Computing Science, University of Glasgow, Glasgow, G12 8QQ, UK

    Christos Anagnostopoulos

  3. Department of Informatics and Telecommunications, University of Athens, 15784, Athens, Greece

    Stathes Hadjiefthymiades

  4. CSIRO Computational Informatics, CSIRO, Box 312, Clayton South, VIC, 3169, Australia

    Arkady Zaslavsky

Authors
  1. Theodoros Anagnostopoulos
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  2. Christos Anagnostopoulos
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  3. Stathes Hadjiefthymiades
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  4. Arkady Zaslavsky
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Correspondence to Theodoros Anagnostopoulos.

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Anagnostopoulos, T., Anagnostopoulos, C., Hadjiefthymiades, S. et al. On-Line Location Prediction Exploiting Spatial and Velocity Context. Int J Wireless Inf Networks 22, 29–40 (2015). https://doi.org/10.1007/s10776-014-0259-3

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  • DOI: https://doi.org/10.1007/s10776-014-0259-3

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