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
相关论文
共 50 条
  • [1] What can we learn from telematics car driving data: A survey
    Gao, Guangyuan
    Meng, Shengwang
    Wuthrich, Mario V.
    [J]. INSURANCE MATHEMATICS & ECONOMICS, 2022, 104 : 185 - 199
  • [2] Convolutional Neural Network Classification of Telematics Car Driving Data
    Gao, Guangyuan
    Wuethrich, Mario, V
    [J]. RISKS, 2019, 7 (01)
  • [3] Boosting Poisson regression models with telematics car driving data
    Guangyuan Gao
    He Wang
    Mario V. Wüthrich
    [J]. Machine Learning, 2022, 111 : 243 - 272
  • [4] Boosting Poisson regression models with telematics car driving data
    Gao, Guangyuan
    Wang, He
    Wuethrich, Mario, V
    [J]. MACHINE LEARNING, 2022, 111 (01) : 243 - 272
  • [5] Claims frequency modeling using telematics car driving data
    Gao, Guangyuan
    Meng, Shengwang
    Wuthrich, Mario V.
    [J]. SCANDINAVIAN ACTUARIAL JOURNAL, 2019, (02) : 143 - 162
  • [6] Feature extraction from telematics car driving heatmaps
    Gao G.
    Wüthrich M.V.
    [J]. European Actuarial Journal, 2018, 8 (2) : 383 - 406
  • [7] IMPROVING AUTOMOBILE INSURANCE CLAIMS FREQUENCY PREDICTION WITH TELEMATICS CAR DRIVING DATA
    Meng, Shengwang
    Wang, He
    Shi, Yanlin
    Gao, Guangyuan
    [J]. ASTIN BULLETIN-THE JOURNAL OF THE INTERNATIONAL ACTUARIAL ASSOCIATION, 2022, 52 (02) : 363 - 391
  • [8] Variable Selection Algorithm for a Mixture of Poisson Regression for Handling Overdispersion in Claims Frequency Modeling Using Telematics Car Driving Data
    Chan, Jennifer S. K.
    Choy, S. T. Boris
    Makov, Udi
    Shamir, Ariel
    Shapovalov, Vered
    [J]. RISKS, 2022, 10 (04)
  • [9] Covariate Selection from Data Collection Onwards: A Methodology for Neurosurgeons
    Keen, Ryan
    Tiemeier, Henning
    [J]. WORLD NEUROSURGERY, 2022, 161 : 245 - 250
  • [10] Variable Selection with Incomplete Covariate Data
    Claeskens, Gerda
    Consentino, Fabrizio
    [J]. BIOMETRICS, 2008, 64 (04) : 1062 - 1069
12345