Citation

Sun S, Bi J, Guillén M, Pérez-Marín AM. Entropy (Basel) 2021; 23(7): e23070829.

Copyright

(Copyright © 2021, MDPI: Multidisciplinary Digital Publications Institute)

DOI

10.3390/e23070829

PMID

unavailable

Abstract

This study proposes a method for identifying and evaluating driving risk as a first step towards calculating premiums in the newly emerging context of usage-based insurance. Telematics data gathered by the Internet of Vehicles (IoV) contain a large number of near-miss events which can be regarded as an alternative for modeling claims or accidents for estimating a driving risk score for a particular vehicle and its driver. Poisson regression and negative binomial regression are applied to a summary data set of 182 vehicles with one record per vehicle and to a panel data set of daily vehicle data containing four near-miss events, i.e., counts of excess speed, high speed brake, harsh acceleration or deceleration and additional driving behavior parameters that do not result in accidents. Negative binomial regression (AICoverspeed = 997.0, BICoverspeed = 1022.7) is seen to perform better than Poisson regression (AICoverspeed = 7051.8, BICoverspeed = 7074.3). Vehicles are separately classified to five driving risk levels with a driving risk score computed from individual effects of the corresponding panel model. This study provides a research basis for actuarial insurance premium calculations, even if no accident information is available, and enables a precise supervision of dangerous driving behaviors based on driving risk scores.

Language: en

Keywords

count data model; driving behavior; driving risk assessment; driving risk score; econometrics; generalized linear model; near-miss event; panel data analysis; telematics; usage-based insurance