Cargando…
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...
Autores principales: | , , , |
---|---|
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 |
_version_ | 1783727607475339264 |
---|---|
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. |
format | Online Article Text |
id | pubmed-8305578 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT sunshuai drivingriskassessmentusingnearmisseventsbasedonpanelpoissonregressionandpanelnegativebinomialregression AT bijun drivingriskassessmentusingnearmisseventsbasedonpanelpoissonregressionandpanelnegativebinomialregression AT guillenmontserrat drivingriskassessmentusingnearmisseventsbasedonpanelpoissonregressionandpanelnegativebinomialregression AT perezmarinanam drivingriskassessmentusingnearmisseventsbasedonpanelpoissonregressionandpanelnegativebinomialregression |