Carregant...
Fitxers
Tipus de document
ArticleVersió
Versió publicadaData de publicació
Llicència de publicació
Si us plau utilitzeu sempre aquest identificador per citar o enllaçar aquest document: https://hdl.handle.net/2445/162081
Assessing driving risk using Internet of Vehicles data: an analysis based on Generalized Linear Models
Títol de la revista
Director/Tutor
ISSN de la revista
Títol del volum
Recurs relacionat
Resum
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
Matèries (anglès)
Citació
Citació
SUN, Shuai, BI, Jun, GUILLÉN, Montserrat, PÉREZ MARÍN, Ana maría. Assessing driving risk using Internet of Vehicles data: an analysis based on Generalized Linear Models. _Sensors_. 2020. Vol. 20, núm. 9, pàgs. 2712. [consulta: 25 de febrer de 2026]. ISSN: 1424-8220. [Disponible a: https://hdl.handle.net/2445/162081]