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http://hdl.handle.net/2445/162081
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 Telemàtica Models lineals (Estadística) Anàlisi de regressió Risk (Insurance) Motor vehicle driving Telematics 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: https://doi.org/10.3390/s20092712 |
It is part of: | Sensors, 2020, vol. 20, num. 9, p. 2712 |
URI: | http://hdl.handle.net/2445/162081 |
Related resource: | https://doi.org/10.3390/s20092712 |
ISSN: | 1424-8220 |
Appears in Collections: | Articles publicats en revistes (Econometria, Estadística i Economia Aplicada) |
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