Feature extraction from telematics car driving heatmaps
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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 (autoencoders) 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).
KeywordsTelematics car driving data Driving styles Unsupervised learning Pattern recognition Image recognition Bottleneck neural network Autoencoder Singular value decomposition Principal component analysis K-means algorithm Kullback–Leibler divergence
Guangyuan Gao: Financially supported by the Social Science Fund of China (Grant no. 16ZDA052) and MOE National Key Research Bases for Humanities and Social Sciences (Grant no. 16JJD910001).
- 1.Ayuso M, Guillen M, Pérez-Marín AM (2016). Telematics and gender discrimination: some usage-based evidence on whether men’s risk of accidents differs from women’s. Risks 4/2, article 10Google Scholar
- 8.Verbelen R, Antonio K, Claeskens G (2018) Unraveling the predictive power of telematics data in car insurance pricing. J Roy Stat Soc Ser C (Appl Stat) (to appear)Google Scholar
- 12.Wüthrich MV, Buser C (2016) Data analytics for non-life insurance pricing. SSRN Manuscript ID 2870308. Version October 25, 2017Google Scholar