Assessing Driving Risk Using Internet of Vehicles Data: An Analysis Based on Generalized Linear Models

Sensors

Ana M. Pérez-Marín¹  Montserrat Guillen¹  Shuai Sun²  Jun Bi² 

[1] Department of Econometrics, Riskcenter-IREA, Universitat de Barcelona, 08034 Barcelona, Spain;Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China;

关键词: internet of vehicles;    usage-based insurance (UBI);    driving risk;    driver classification;    regression analysis;   

DOI  :  10.3390/s20092712

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown