基于贝叶斯网络的公交车事故外因分析

为了系统的探究车、路和环境共同作用下的公交车事故致因，利用A市259条公交车事故数据，基于专家知识与数据融合方法建立贝叶斯网络结构；利用服从Dirichlet分布的贝叶斯方法进行参数学习。在验证了模型的有效性后，结合贝叶斯网络模型，运用团树传播算法推理了各变量之间的关系。研究结果表明：天气、时间、道路线形、地点都可能导致公交车事故；根据概率由大到小，导致的事故类型依次为非碰撞事故、与小汽车、非机动车、公交车碰撞；且每一事故类型的伤亡情况及最强致因因素都不尽相同。除此之外，各致因因素在不同情况下，导致的事故类型及伤亡情况概率也有差异。相关结果可以为政府及企业建立事故管理制度，减少公交车事故的发生提供一定的依据。

Bayesian network-based bus accidents external causation analysis

In order to systematically explore the causes of bus accidents under the joint action of vehicles, roads and environment, the data of 259 bus accidents in A city were used to establish a Bayesian network structure based on experts knowledge and data fusion method. The parameter learning is carried out by using the Bayesian method which obeys the Dirichlet distribution. After verifying the effectiveness of the model, combined with the Bayesian network model, the cluster tree propagation algorithm was used to infer the relationship between the variables. The research results show that weather, time, road alignment, and location may all cause bus accidents; according to the probability from high to low, the types of accidents caused are non-collision accidents, collisions with cars, non-motor vehicles, and buses; the casualties and the strongest causal factors of each type of accident are different. In addition, in the different circumstances of each cause, the probability of the type of accident and the probability of casualties are also different. The relevant results can provide certain basis for government and enterprises to establish an accident management system and reduce the occurrence of bus accidents.