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Date
2021-07Type
- Journal Article
Abstract
It has become of key interest in the insurance industry to understand and extract information from telematics car driving data. Telematics car driving data of individual car drivers can be summarised in so-called speed-acceleration heatmaps. The aim of this study is to cluster such speed-acceleration heatmaps to different categories by analysing similarities and differences in these heatmaps. Making use of local smoothness properties, we propose to process these heatmaps as RGB images. Clustering can then be achieved by involving supervised information via a transfer learning approach using the pre-trained AlexNet to extract discriminative features. The K-means algorithm is then applied on these extracted discriminative features for clustering. The experiment results in an improvement of heatmap clustering compared to classical approaches. Show more
Publication status
publishedExternal links
Journal / series
Annals of Actuarial ScienceVolume
Pages / Article No.
Publisher
University PressSubject
Telematics car driving data; Driving styles; Unsupervised learning; Image processing; Transfer learningOrganisational unit
08813 - Wüthrich, Mario Valentin (Tit.-Prof.)
08813 - Wüthrich, Mario Valentin (Tit.-Prof.)
02204 - RiskLab / RiskLab
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