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Driving Risk Assessment Using Near-Miss Events Based on Panel Poisson Regression and Panel Negative Binomial Regression

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...

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Autores principales: Sun, Shuai, Bi, Jun, Guillen, Montserrat, Pérez-Marín, Ana M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8305578/
https://www.ncbi.nlm.nih.gov/pubmed/34209743
http://dx.doi.org/10.3390/e23070829
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author Sun, Shuai
Bi, Jun
Guillen, Montserrat
Pérez-Marín, Ana M.
author_facet Sun, Shuai
Bi, Jun
Guillen, Montserrat
Pérez-Marín, Ana M.
author_sort Sun, Shuai
collection PubMed
description 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 ( [Formula: see text] = 997.0, [Formula: see text] = 1022.7) is seen to perform better than Poisson regression ( [Formula: see text] = 7051.8, [Formula: see text] = 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.
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spelling pubmed-83055782021-07-25 Driving Risk Assessment Using Near-Miss Events Based on Panel Poisson Regression and Panel Negative Binomial Regression Sun, Shuai Bi, Jun Guillen, Montserrat Pérez-Marín, Ana M. Entropy (Basel) Article 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 ( [Formula: see text] = 997.0, [Formula: see text] = 1022.7) is seen to perform better than Poisson regression ( [Formula: see text] = 7051.8, [Formula: see text] = 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. MDPI 2021-06-29 /pmc/articles/PMC8305578/ /pubmed/34209743 http://dx.doi.org/10.3390/e23070829 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Sun, Shuai
Bi, Jun
Guillen, Montserrat
Pérez-Marín, Ana M.
Driving Risk Assessment Using Near-Miss Events Based on Panel Poisson Regression and Panel Negative Binomial Regression
title Driving Risk Assessment Using Near-Miss Events Based on Panel Poisson Regression and Panel Negative Binomial Regression
title_full Driving Risk Assessment Using Near-Miss Events Based on Panel Poisson Regression and Panel Negative Binomial Regression
title_fullStr Driving Risk Assessment Using Near-Miss Events Based on Panel Poisson Regression and Panel Negative Binomial Regression
title_full_unstemmed Driving Risk Assessment Using Near-Miss Events Based on Panel Poisson Regression and Panel Negative Binomial Regression
title_short Driving Risk Assessment Using Near-Miss Events Based on Panel Poisson Regression and Panel Negative Binomial Regression
title_sort driving risk assessment using near-miss events based on panel poisson regression and panel negative binomial regression
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8305578/
https://www.ncbi.nlm.nih.gov/pubmed/34209743
http://dx.doi.org/10.3390/e23070829
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