University of Limerick, Ireland.
University of Limerick, Ireland.
Accid Anal Prev. 2018 Apr;113:244-256. doi: 10.1016/j.aap.2018.01.037. Epub 2018 Mar 7.
An extensive number of research studies have attempted to capture the factors that influence the severity of vehicle impacts. The high number of risks facing all traffic participants has led to a gradual increase in sophisticated data collection schemes linking crash characteristics to subsequent severity measures. This study serves as a departure from previous research by relating injuries suffered in road traffic accidents to expected trauma compensation payouts and deriving a quantitative cost function. Data from the National Highway Traffic Safety Administration's (NHTSA) Crash Injury Research (CIREN) database for the years 2005-2014 is combined with the Book of Quantum, an Irish governmental document that offers guidelines on the appropriate compensation to be awarded for injuries sustained in accidents. A multiple linear regression is carried out to identify the crash factors that significantly influence expected compensation costs and compared to ordered and multinomial logit models. The model offers encouraging results given the inherent variation expected in vehicular incidents and the subjectivity influencing compensation payout judgments, attaining an adjusted-R fit of 20.6% when uninfluential factors are removed. It is found that relative speed at time of impact and dark conditions increase the expected costs, while rear-end incidents, incident sustained in van-based trucks and incidents sustained while turning result in lower expected compensations. The number of airbags available in the vehicle is also a significant factor. The scalar-outcome approach used in this research offers an alternative methodology to the discrete-outcome models that dominate traffic safety analyses. The results also raise queries on the future development of claims reserving (capital allocations earmarked for future expected claims payments) as advanced driver assistant systems (ADASs) seek to eradicate the most frequent types of crash factors upon which insurance mathematics base their assumptions.
大量的研究试图捕捉影响车辆碰撞严重程度的因素。由于所有交通参与者都面临着大量的风险，因此逐渐增加了将碰撞特征与后续严重程度衡量标准联系起来的复杂数据收集方案。本研究通过将道路交通伤害与预期创伤赔偿支出联系起来，并得出定量成本函数，与以往的研究有所不同。该研究结合了国家公路交通安全管理局（NHTSA）的碰撞伤害研究（CIREN）数据库（2005-2014 年）的数据和爱尔兰政府的《量子书》（Book of Quantum），后者提供了关于因事故而应给予的适当赔偿的指导方针。通过多项线性回归来确定显著影响预期赔偿成本的碰撞因素，并与有序和多项逻辑回归模型进行比较。鉴于车辆事故中预期的固有变化和影响赔偿支出判断的主观性，该模型提供了令人鼓舞的结果，在去除无影响因素后，调整后的 R 拟合度达到 20.6%。研究发现，碰撞时的相对速度和黑暗条件会增加预期成本，而追尾事故、厢式货车事故和转弯时发生的事故会导致预期赔偿降低。车辆中可用的安全气囊数量也是一个重要因素。本研究中使用的标量结果方法为主导交通安全分析的离散结果模型提供了一种替代方法。研究结果还对未来的理赔预留（为未来预期理赔支付指定的资本分配）提出了质疑，因为先进的驾驶员辅助系统（ADAS）试图消除保险数学所依据的假设中最常见的碰撞因素类型。
