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Title: Assessing driving risk using Internet of Vehicles data: an analysis based on Generalized Linear Models
Author: Sun, Shuai
Bi, Jun
Guillén, Montserrat
Pérez Marín, Ana María
Keywords: Risc (Assegurances)
Conducció de vehicles de motor
Models lineals (Estadística)
Anàlisi de regressió
Risk (Insurance)
Motor vehicle driving
Linear models (Statistics)
Regression analysis
Issue Date: 10-May-2020
Publisher: MDPI
Abstract: With the major advances made in internet of vehicles (IoV) technology in recent years, usage-based insurance (UBI) products have emerged to meet market needs. Such products, however, critically depend on driving risk identification and driver classification. Here, ordinary least square and binary logistic regressions are used to calculate a driving risk score on short-term IoV data without accidents and claims. Specifically, the regression results reveal a positive relationship between driving speed, braking times, revolutions per minute and the position of the accelerator pedal. Different classes of risk drivers can thus be identified. This study stresses both the importance and feasibility of using sensor data for driving risk analysis and discusses the implications for traffic safety and motor insurance
Note: Reproducció del document publicat a:
It is part of: Sensors, 2020, vol. 20, num. 9, p. 2712
Related resource:
ISSN: 1424-8220
Appears in Collections:Articles publicats en revistes (Econometria, Estadística i Economia Aplicada)

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