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Dynamic Detection of Transportation Modes Using Keypoint Prediction

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Machine Learning, Optimization, and Big Data (MOD 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9432))

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Abstract

This paper proposes an approach that makes logical knowledge-based decisions, to determine the transportation mode a person is using in real-time. The focus is set to the detection of different public transportation modes. Hereby it is analyzed how additional contextual information can be used to improve the decision making process. The methodology implemented is capable to differentiate between different modes of transportation including walking, driving by car, taking the bus, tram and (suburbain) trains. The implemented knowledge-based system is based on the idea of Keypoints, which provide contextual information about the environment. The proposed algorithm reached an accuracy of about 95 %, which outclasses other methodologies in detecting the different public transportation modes a person is currently using.

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Author information

Authors and Affiliations

  1. Connected Drive Department, BMW Research and Technology, Munich, Germany

    Olga Birth

  2. Institute for Applied Informatics Cooperative Systems, Garching, Germany

    Aaron Frueh & Johann Schlichter

Authors
  1. Olga Birth
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  2. Aaron Frueh
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  3. Johann Schlichter
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Corresponding author

Correspondence to Olga Birth .

Editor information

Editors and Affiliations

  1. University of Florida, Gainsville, Florida, USA

    Panos Pardalos

  2. University of Catania, Catania, Italy

    Mario Pavone

  3. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  4. University of Catania, Catania, Italy

    Vincenzo Cutello

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© 2015 Springer International Publishing Switzerland

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Birth, O., Frueh, A., Schlichter, J. (2015). Dynamic Detection of Transportation Modes Using Keypoint Prediction. In: Pardalos, P., Pavone, M., Farinella, G., Cutello, V. (eds) Machine Learning, Optimization, and Big Data. MOD 2015. Lecture Notes in Computer Science(), vol 9432. Springer, Cham. https://doi.org/10.1007/978-3-319-27926-8_5

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  • DOI: https://doi.org/10.1007/978-3-319-27926-8_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27925-1

  • Online ISBN: 978-3-319-27926-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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