City Research Online

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 summarized 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.

Publication Type:	Article
Additional Information:	This article is published in a revised form in Annals of Actuarial Science https://www.cambridge.org/core/journals/annals-of-actuarial-science at https://doi.org/10.1017/S1748499520000317. This version is published under a Creative Commons CC-BY-NC-ND. No commercial re-distribution or re-use allowed. Derivative works cannot be distributed. © copyright holder.
Publisher Keywords:	Telematics car driving data, driving styles, unsupervised learning, image processing, transfer learning, AlexNet
Subjects:	H Social Sciences > HE Transportation and Communications H Social Sciences > HM Sociology Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments:	Bayes Business School > Actuarial Science & Insurance
SWORD Depositor:	Symplectic Administrator

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