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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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Fig. 1.

References

  1. Dabiri, S., Heaslip, K.: Inferring transportation modes from GPS trajectories using a convolutional neural network. Transp. Res. Part C Emerg. Technol. 86, 360–371 (2018)

    CrossRef  Google Scholar 

  2. Endo, Y., Toda, H., Nishida, K., Kawanobe, A.: Deep feature extraction from trajectories for transportation mode estimation. In: Bailey, J., Khan, L., Washio, T., Dobbie, G., Huang, J.Z., Wang, R. (eds.) PAKDD 2016. LNCS (LNAI), vol. 9652, pp. 54–66. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-31750-2_5

    CrossRef  Google Scholar 

  3. Han, J., Pei, J., Kamber, M.: Data Mining: Concepts and Techniques. Elsevier, Amsterdam (2011)

    MATH  Google Scholar 

  4. Jiang, X., Souza, E.N., Pesaranghader, A., Hu, B., Silver, D.L., Matwin, S.: trajectorynet: an embedded GPS trajectory representation for point-based classification using recurrent neural networks. arXiv preprint arXiv:1705.02636 (2017)

  5. Soares Júnior, A., Moreno, B.N., Times, V.C., Matwin, S., dos Anjos Formiga Cabral, L.: GRASP-UTS: an algorithm for unsupervised trajectory segmentation. Int. J. Geogr. Inf. Sci. 29(1), 46–68 (2015)

    CrossRef  Google Scholar 

  6. Lin, M., Hsu, W.-J.: Mining GPS data for mobility patterns: a survey. Pervasive Mob. Comput. 12, 1–16 (2014)

    CrossRef  Google Scholar 

  7. Spaccapietra, S., Parent, C., Damiani, M.L., Macedo, J.A., Porto, F., Vangenot, C.: A conceptual view on trajectories. Data Knowl. Eng. 65(1), 126–146 (2008)

    CrossRef  Google Scholar 

  8. Stenneth, L., Wolfson, O., Yu, P.S., Xu, B.: Transportation mode detection using mobile phones and GIS information. In: Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, GIS 2011, pp. 54–63. ACM, New York (2011)

    Google Scholar 

  9. Xiao, Z., Wang, Y., Fu, K., Fan, W.: Identifying different transportation modes from trajectory data using tree-based ensemble classifiers. ISPRS 6(2), 57 (2017)

    Google Scholar 

  10. Yanyun, G., Fang, Z., Shaomeng, C., Haiyong, L.: A convolutional neural networks based transportation mode identification algorithm. In: 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–7, September 2017

    Google Scholar 

  11. Zheng, Y., Li, Q., Chen, Y., Xie, X., Ma, W.-Y.: Understanding mobility based on GPS data. In: Proceedings of the 10th International Conference on Ubiquitous Computing, pp. 312–321. ACM (2008)

    Google Scholar 

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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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Correspondence to Mohammad Etemad .

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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

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  • Online ISBN: 978-3-319-89656-4

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