Evaluation of driving risk at different speeds
Mario V. Wüthrich and
Insurance: Mathematics and Economics, 2019, vol. 88, issue C, 108-119
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)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:insuma:v:88:y:2019:i:c:p:108-119
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