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Predicting the Mode of Transport from GPS Trajectories

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Proceedings of the 5th International Conference on Big Data and Internet of Things (BDIoT 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 489))

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Abstract

In this paper, we present a framework that can automatically classify the transportation mode based only on the GPS trajectory of an individual. We intend to show that the extraction of extra features besides speed, acceleration, and the bearing rate [35, 36] enables many classifiers to achieve very efficient generalization. We apply machine learning algorithms, Recurrent Neural Network and Convolutional Neural Network. Finally, we compare our approach with state-of-art transportation mode prediction strategies.

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Author information

Authors and Affiliations

  1. Moulay Ismail University, Meknes, Morocco

    Hichame Kabiri & Youssef Ghanou

Authors
  1. Hichame Kabiri
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  2. Youssef Ghanou
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Corresponding author

Correspondence to Hichame Kabiri .

Editor information

Editors and Affiliations

  1. ENSIAS, Mohammed V University, Rabat, Morocco

    Mohamed Lazaar

  2. UNILEHAVRE, UNIROUEN, Normandie Université, Le Havre, France

    Claude Duvallet

  3. Vrije Universiteit Brussel, Brussels, Belgium

    Abdellah Touhafi

  4. ENSA, Abdelmalek Essaâdi University, Tetuan, Morocco

    Mohammed Al Achhab

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Kabiri, H., Ghanou, Y. (2022). Predicting the Mode of Transport from GPS Trajectories. In: Lazaar, M., Duvallet, C., Touhafi, A., Al Achhab, M. (eds) Proceedings of the 5th International Conference on Big Data and Internet of Things. BDIoT 2021. Lecture Notes in Networks and Systems, vol 489. Springer, Cham. https://doi.org/10.1007/978-3-031-07969-6_15

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