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Multimodal Behavioral Mobility Pattern Mining and Analysis Using Topic Modeling on GPS Data

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Behavioral Analytics in Social and Ubiquitous Environments (MUSE 2015, MSM 2015, MSM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11406))

Abstract

Identifying risky driving behavior is of central importance for increasing traffic safety. This paper tackles the task of analyzing real (naturalistic) driving data captured by in-vehicle sensors using interpretable data science methods. In particular, we focus on symbolic time-series abstraction and the subsequent behavioral profile identification using topic modeling approaches. For our experiments, we utilize a real-world dataset. Our results indicate interesting behavioral driving profiles including important patterns and factors for traffic safety modeling.

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Notes

  1. 1.

    https://www.crossyn.com/crossyn.

  2. 2.

    \(\mu \) = mean, \(\sigma \) = standard deviation.

  3. 3.

    In their study, [14] concluded that hostile music can lead to distracted drivers.

  4. 4.

    https://github.com/bmabey/pyLDAvis/blob/master/pyLDAvis/sklearn.py.

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Author information

Authors and Affiliations

  1. Tilburg University, Tilburg, The Netherlands

    Sebastiaan Merino & Martin Atzmueller

Authors
  1. Sebastiaan Merino
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  2. Martin Atzmueller
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Corresponding author

Correspondence to Martin Atzmueller .

Editor information

Editors and Affiliations

  1. Tilburg University, Tilburg, The Netherlands

    Martin Atzmueller

  2. BMW Technology Group, Chicago, IL, USA

    Alvin Chin

  3. RWTH Aachen University, Aachen, Germany

    Florian Lemmerich

  4. University of Bergen, Bergen, Norway

    Christoph Trattner

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Merino, S., Atzmueller, M. (2019). Multimodal Behavioral Mobility Pattern Mining and Analysis Using Topic Modeling on GPS Data. In: Atzmueller, M., Chin, A., Lemmerich, F., Trattner, C. (eds) Behavioral Analytics in Social and Ubiquitous Environments. MUSE MSM MSM 2015 2015 2016. Lecture Notes in Computer Science(), vol 11406. Springer, Cham. https://doi.org/10.1007/978-3-030-34407-8_4

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  • DOI: https://doi.org/10.1007/978-3-030-34407-8_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33906-7

  • Online ISBN: 978-3-030-34407-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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