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Canadian Conference on Artificial Intelligence

Canadian AI 2018: Advances in Artificial Intelligence pp 259–264Cite as

Predicting Transportation Modes of GPS Trajectories Using Feature Engineering and Noise Removal

Part of the Lecture Notes in Computer Science book series (LNAI,volume 10832)

Abstract

Understanding transportation mode from GPS (Global Positioning System) traces is an essential topic in the data mobility domain. In this paper, a framework is proposed to predict transportation modes. This framework follows a sequence of five steps: (i) data preparation, where GPS points are grouped in trajectory samples; (ii) point features generation; (iii) trajectory features extraction; (iv) noise removal; (v) normalization. We show that the extraction of the new point features: bearing rate, the rate of rate of change of the bearing rate and the global and local trajectory features, like medians and percentiles enables many classifiers to achieve high accuracy (96.5%) and f1 (96.3%) scores. We also show that the noise removal task affects the performance of all the models tested. Finally, the empirical tests where we compare this work against state-of-art transportation mode prediction strategies show that our framework is competitive and outperforms most of them.

Keywords

  • Feature engineering
  • Noise removal
  • Trajectory classification

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Acknowledgments

The authors would like to thank NSERC (Natural Sciences and Engineering Research Council of Canada) for financial support.

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Authors and Affiliations

  1. Institute for Big Data Analytics, Dalhousie University, Halifax, Canada

    Mohammad Etemad, Amílcar Soares Júnior & Stan Matwin

  2. Institute for Computer Science, Polish Academy of Sciences, Warsaw, Poland

    Stan Matwin

Authors
  1. Mohammad Etemad
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  2. Amílcar Soares Júnior
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  3. Stan Matwin
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Corresponding author

Correspondence to Mohammad Etemad .

Editor information

Editors and Affiliations

  1. Ryerson University, Toronto, Ontario, Canada

    Ebrahim Bagheri

  2. McGill University, Montréal, Québec, Canada

    Jackie C.K. Cheung

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Etemad, M., Soares Júnior, A., Matwin, S. (2018). Predicting Transportation Modes of GPS Trajectories Using Feature Engineering and Noise Removal. In: Bagheri, E., Cheung, J. (eds) Advances in Artificial Intelligence. Canadian AI 2018. Lecture Notes in Computer Science(), vol 10832. Springer, Cham. https://doi.org/10.1007/978-3-319-89656-4_24

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  • DOI: https://doi.org/10.1007/978-3-319-89656-4_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-89655-7

  • Online ISBN: 978-3-319-89656-4

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