EconPapers    
Economics at your fingertips  
 

Evaluation of driving risk at different speeds

Guangyuan Gao, Mario V. Wüthrich and Hanfang Yang

Insurance: Mathematics and Economics, 2019, vol. 88, issue C, 108-119

Abstract: Telematics car driving data describes drivers’ driving characteristics. This paper studies the driving characteristics at different speeds and their predictive power for claims frequency modeling. We first extract covariates from telematics car driving data using K-medoids clustering and principal components analysis. These telematics covariates are then used as explanatory variables for claims frequency modeling, in which we analyze their predictive power. Moreover, we use these telematics covariates to challenge the classical covariates usually used in practice.

Keywords: Telematics data; v-a heatmap; K-medoids algorithm; Principal components analysis; Generalized additive model; Variable selection; Poisson regression; Claims frequency modeling (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167668718304979
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:insuma:v:88:y:2019:i:c:p:108-119

Access Statistics for this article

Insurance: Mathematics and Economics is currently edited by R. Kaas, Hansjoerg Albrecher, M. J. Goovaerts and E. S. W. Shiu

More articles in Insurance: Mathematics and Economics from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().

 
Page updated 2019-11-24
Handle: RePEc:eee:insuma:v:88:y:2019:i:c:p:108-119