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

Do you have negative results from your research you’d like to share?

Paper statistics

Downloads
620
Abstract Views
1,878
Rank
79,476
PlumX Metrics