Feature Extraction from Telematics Car Driving Heatmaps

16 Pages Posted: 16 Nov 2017

See all articles by Guangyuan Gao

Guangyuan Gao

Renmin University of China - School of Statistics

Mario V. Wuthrich

RiskLab, ETH Zurich

Date Written: November 13, 2017

Abstract

Insurance companies have started to collect high-frequency GPS car driving data to analyze the driving styles of their policyholders. In previous work, we have introduced speed and acceleration heatmaps. These heatmaps were categorized with the K-means algorithm to differentiate varying driving styles. In many situations it is useful to have low-dimensional continuous representations instead of unordered categories. In the present work we use singular value decomposition and bottleneck neural networks for principal component analysis. We show that a two-dimensional representation is sufficient to re-construct the heatmaps with high accuracy (measured by Kullback-Leibler divergences).

Keywords: Telematics car driving data, driving styles, unsupervised learning, pattern recognition, image recognition, bottleneck neural network, singular value decomposition, principal component analysis, Kullback-Leibler divergence

JEL Classification: G22, G28

Suggested Citation

Gao, Guangyuan and Wuthrich, Mario V., Feature Extraction from Telematics Car Driving Heatmaps (November 13, 2017). Available at SSRN: https://ssrn.com/abstract=3070069 or http://dx.doi.org/10.2139/ssrn.3070069

Guangyuan Gao

Renmin University of China - School of Statistics ( email )

No.59 Zhongguancun Street, Renmin University
Beijing, 100872
China

Mario V. Wuthrich (Contact Author)

RiskLab, ETH Zurich ( email )

Department of Mathematics
Ramistrasse 101
Zurich, 8092
Switzerland

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