Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China.
Department of Econometrics, Riskcenter-IREA, Universitat de Barcelona, 08034 Barcelona, Spain.
Sensors (Basel). 2020 May 9;20(9):2712. doi: 10.3390/s20092712.
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.
近年来，随着车联网（IoV）技术的重大进展，基于使用情况的保险（UBI）产品应运而生，以满足市场需求。然而，此类产品严重依赖于驾驶风险识别和驾驶员分类。在这里，我们使用普通最小二乘法和二元逻辑回归来计算无事故和索赔的短期 IoV 数据的驾驶风险评分。具体而言，回归结果显示驾驶速度、制动次数、每分钟转速和油门踏板位置之间存在正相关关系。因此，可以识别不同风险等级的驾驶员。本研究强调了使用传感器数据进行驾驶风险分析的重要性和可行性，并讨论了其对交通安全和汽车保险的影响。
