In this paper, we aim to identify the significant variables that contribute to the injury severity level of the person in the car when an accident happens and build a statistical model for predicting the maximum injury severity level as well as estimating the potential economic cost in a car accident based on those variables. The General Estimates System data, which is a representative sample of police-reported motor vehicle crashes of all types collected by the National Highway Transportation Safety Administration, from the years 2012 to 2013 is the main data source. Some other data sources such as the car safety rating from the United State Department of Transformation and the state-specific cost of crash deaths fact sheets are also used in the predictive model building process. An interactive system programmed in HyperText Markup Language, Cascading Style Sheets and JavaScript is developed based on the results of predictive modeling. The system is hosted on a website at http://gessmu.azurewebsites.net for public access. The system allows users to input variables that are significant contributors in car accidents and obtain the predicted maximum injury severity level and potential economic cost of a car accident.

在本文中，我们的目标是确定发生事故时影响车内人员伤害严重程度的重要变量，并建立一个统计模型来预测最大伤害严重程度以及估计潜在的经济成本。基于这些变量的车祸。General Estimates System 数据是 2012 年至 2013 年的主要数据来源，它是美国国家公路运输安全管理局收集的所有类型的警方报告机动车事故的代表性样本。其他一些数据源，例如美国转型部的汽车安全评级和特定于州的碰撞死亡成本情况说明书也用于预测模型构建过程。一个用超文本标记语言编程的交互系统，级联样式表和 JavaScript 是基于预测建模的结果开发的。该系统托管在 http://gessmu.azurewebsites.net 网站上，供公众访问。该系统允许用户输入对车祸有重要影响的变量，并获得预测的最大伤害严重程度和车祸的潜在经济成本。