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Convolutional Neural Network Classification of Telematics Car Driving Data

1
Center for Applied Statistics and School of Statistics, Renmin University of China, Beijing 100872, China
2
RiskLab, Department of Mathematics, ETH Zurich, 8092 Zürich, Switzerland
*
Author to whom correspondence should be addressed.
Received: 29 October 2018 / Revised: 21 December 2018 / Accepted: 9 January 2019 / Published: 10 January 2019
(This article belongs to the Special Issue Insurance: Spatial and Network Data)
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Abstract

The aim of this project is to analyze high-frequency GPS location data (second per second) of individual car drivers (and trips). We extract feature information about speeds, acceleration, deceleration, and changes of direction from this high-frequency GPS location data. Time series of this feature information allow us to appropriately allocate individual car driving trips to selected drivers using convolutional neural networks. View Full-Text
Keywords: telematics car driving data; driving styles; pattern recognition; image recognition; convolutional neural networks telematics car driving data; driving styles; pattern recognition; image recognition; convolutional neural networks
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Gao, G.; Wüthrich, M.V. Convolutional Neural Network Classification of Telematics Car Driving Data. Risks 2019, 7, 6.

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