Sun, ShuaiBi, JunGuillén, MontserratPérez Marín, Ana María2020-05-232020-05-232020-05-101424-8220https://hdl.handle.net/2445/162081With 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 insuranceapplication/pdfengcc-by (c) Sun, Shuai et al., 2020http://creativecommons.org/licenses/by/3.0/esRisc (Assegurances)Conducció de vehicles de motorTelemàticaModels lineals (Estadística)Anàlisi de regressióRisk (Insurance)Motor vehicle drivingTelematicsLinear models (Statistics)Regression analysisinfo:eu-repo/semantics/article7009882020-05-23info:eu-repo/semantics/openAccess32397508