Covariate selection from telematics car driving data

被引：33
|
作者：
Wüthrich M.V. [1 ,2 ]
机构：
[1] RiskLab, Department of Mathematics, ETH Zurich, Zurich
[2] Swiss Finance Institute, Zurich
来源：
European Actuarial Journal | 2017年 / 7卷 / 1期
关键词：
Categorical classes; Clustering; Driving habits; Driving styles; K-means clustering; Machine learning; Pattern recognition; Regression; Telematics data; Unsupervised learning;
D O I：
10.1007/s13385-017-0149-z
中图分类号：
学科分类号：
摘要：
Car insurance companies have started to collect high-frequency GPS location data of their car drivers. This data provides detailed information about the driving habits and driving styles of individual car drivers. We illustrate how this data can be analyzed using techniques from pattern recognition and machine learning. In particular, we describe how driving styles can be categorized so that they can be used for a regression analysis in car insurance pricing. © 2017, EAJ Association.
引用
收藏
页码：89 / 108
页数：19
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