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Machine Learning for Automobile Driver Identification Using Telematics Data

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1097))

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Abstract

Recent years have seen rapid developments in the automotive industry and the Internet of Things (IoT). One such development is the use of onboard telematic devices that generate data about the car and the driver’s behaviour. This data can be used for identifying drivers through their driving habits. This paper proposes a novel driver identification methodology for extracting and learning driving signatures embedded within the telematics data. First, features representatives of driving style are extracted and derived such as longitudinal acceleration, longitudinal jerk and heading speed from the raw telematics data of GPS coordinates, speed and heading angle. Next, statistical feature matrices are obtained for these features using sliding windows. Finally, several traditional machine learning models are trained over these matrices to learn individual drivers. Results show a driver identification accuracy of 90% for a dataset consisting of only two drivers; the accuracy falls gradually as the number of drivers increases.

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Acknowledgements

The dataset used in this study was provided by MachinesTalk [18], a data analytics company in Saudi Arabia. The authors are thankful to Mr. Mohammed Alkhoshail of MachinesTalk, who arranged for access to the telematics data and provided us the opportunity to work with it.

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Authors and Affiliations

  1. College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia

    Hanadi Alhamdan

  2. School of Computing, Dublin City University, Dublin, Ireland

    Musfira Jilani

Authors
  1. Hanadi Alhamdan
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  2. Musfira Jilani
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Corresponding authors

Correspondence to Hanadi Alhamdan or Musfira Jilani .

Editor information

Editors and Affiliations

  1. Princess Nourah Bint Abdul Rahman University, Riyadh, Saudi Arabia

    Auhood Alfaries

  2. Princess Nourah Bint Abdul Rahman University, Riyadh, Saudi Arabia

    Hanan Mengash

  3. Hasselt University, Hasselt, Belgium

    Ansar Yasar

  4. Acadia University, Wolfville, NS, Canada

    Elhadi Shakshuki

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Alhamdan, H., Jilani, M. (2019). Machine Learning for Automobile Driver Identification Using Telematics Data. In: Alfaries, A., Mengash, H., Yasar, A., Shakshuki, E. (eds) Advances in Data Science, Cyber Security and IT Applications. ICC 2019. Communications in Computer and Information Science, vol 1097. Springer, Cham. https://doi.org/10.1007/978-3-030-36365-9_24

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  • DOI: https://doi.org/10.1007/978-3-030-36365-9_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36364-2

  • Online ISBN: 978-3-030-36365-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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