一种基于贝叶斯网络模型的交通事故预测方法

大部分的交通事故都可以预测.有效的交通事故预测能从很大程度上减少人员伤亡和交通阻塞.贝叶斯网络是目前不确定知识和推理领域最有效的理论模型之一.该文提出了一种基于贝叶斯网络模型理论的交通事故预测方法.在综合考虑交通事故成因的基础上利用领域专家知识构建网络模型,在已有的事故数据的基础上提出基于贝叶斯法则的学习算法,并通过计算变量间的条件概率来计算事故发生的可能性,达到事故预测的目的.文章的最后,通过历史数据进行仿真实验,对仿真结果和该模型的适用范围进行了分析.

A Traffic Accident Prediction Method Based on Bayesian Network Model

Most traffic accidents can be predicted.An efficient traffic accident prediction method can reduce the casualties and the traffic jams in great extent.Bayesian network is one of the most efficient models in the uncertain knowledge and reasoning field.A traffic accident prediction model based on Bayesian network is put forward in this paper.The network model is based on knowledge of the field experts and the causes of traffic accident.The studying algorithm is adopted on the basis of historical accident data,which produces the probability of accidents by calculating the conditional probability among variables.Finally through the simulation of historical data,it analyzes the practical situations to which the model can be applied.