TRYSE Research Group, Department of Civil Engineering, University of Granada, ETSI Caminos, Canales y Puertos, c/Severo Ochoa, s/n, 18071 Granada, Spain.
Accid Anal Prev. 2011 Jan;43(1):402-11. doi: 10.1016/j.aap.2010.09.010. Epub 2010 Oct 20.
Several different factors contribute to injury severity in traffic accidents, such as driver characteristics, highway characteristics, vehicle characteristics, accidents characteristics, and atmospheric factors. This paper shows the possibility of using Bayesian Networks (BNs) to classify traffic accidents according to their injury severity. BNs are capable of making predictions without the need for pre assumptions and are used to make graphic representations of complex systems with interrelated components. This paper presents an analysis of 1536 accidents on rural highways in Spain, where 18 variables representing the aforementioned contributing factors were used to build 3 different BNs that classified the severity of accidents into slightly injured and killed or severely injured. The variables that best identify the factors that are associated with a killed or seriously injured accident (accident type, driver age, lighting and number of injuries) were identified by inference.
有几个不同的因素会导致交通事故中的伤害严重程度，例如驾驶员特征、公路特征、车辆特征、事故特征和大气因素。本文展示了使用贝叶斯网络（BN）根据伤害严重程度对交通事故进行分类的可能性。BN 能够在不需要预先假设的情况下进行预测，并用于对具有相互关联组件的复杂系统进行图形表示。本文对西班牙农村公路上的 1536 起事故进行了分析，使用了代表上述影响因素的 18 个变量来构建 3 个不同的 BN，将事故的严重程度分为轻伤和死亡或重伤。通过推理确定了最能识别与死亡或重伤事故相关的因素的变量（事故类型、驾驶员年龄、照明和受伤人数）。
