@article{ref1,
title="Driving risk assessment using near-miss events based on panel Poisson regression and panel negative binomial regression",
journal="Entropy (Basel, Switzerland)",
year="2021",
author="Sun, Shuai and Bi, Jun and Guillén, Montserrat and Pérez-Marín, Ana M.",
volume="23",
number="7",
pages="e23070829-e23070829",
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.<p /> <p>Language: en</p>",
language="en",
issn="1099-4300",
doi="10.3390/e23070829",
url="http://dx.doi.org/10.3390/e23070829"
}