Convolutional neural network classification of telematics car driving data

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.

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Year of publication:	2019
Authors:	Gao, Guangyuan ; Wüthrich, Mario V.
Published in:	Risks. - Basel : MDPI, ISSN 2227-9091. - Vol. 7.2019, 1, p. 1-18
Publisher:	Basel : MDPI
Subject:	telematics car driving data \| driving styles \| pattern recognition \| image recognition \| convolutional neural networks

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Type of publication:	Article
Type of publication (narrower categories):	Article
Language:	English
Other identifiers:	10.3390/risks7010006 [DOI] 1667828150 [GVK] hdl:10419/257844 [Handle]
Source:	EconStor

Persistent link: https://www.econbiz.de/10013200424