基于贝叶斯网络的铁路交通事故预警研究

Research on railway transportation accidents warning based on Bayesian network

为提高铁路安全管理水平,将贝叶斯网络与解释结构模型(ISM)相结合,建立铁路交通事故预警模型。首先,建立项点指标体系,确定事故矩阵;其次,结合因果效应算法与ISM,计算节点优先次序,并将其与事故矩阵代入K2算法,生成贝叶斯网络结构,采用最大期望法计算贝叶斯网络参数;然后,结合贝叶斯网络逆向推理计算项点频率与事故发生概率间的关系,利用历史数据计算事故预警阈值;最后,以成都局调车事故为例进行分析验证。结果表明:影响调车脱轨事故的核心因素为非集中区不执行要道还道制度、未严格执行调车推送作业“四必须”制度、违规手动干预驼峰溜放作业、调车作业跟车不到位或站位不当;调车脱轨事故预警阈值为0.0361。

In order to enhance the level of railway safety management,Bayesian network and ISM were combined to establish a warning model for railway accidents.Firstly,the item point system was established,and the accident matrix was determined.Secondly,the causal effect algorithm was combined with ISM to calculate the node influence ranking.The ranking and the accident matrix were substituted into K2 algorithm to calculate the Bayesian network structure.Meanwhile,the expectation-maximization algorithm was used to calculate the parameters of Bayesian network.The relationship between the item point frequency and the accident probability was calculated using the backward inference of Bayesian network.The accident warning threshold could be obtained by using historical data.Finally,Chengdu bureau shunting accidents were taken for case analysis.The results show as follows:main factors affecting the derailment accidents are not implementing the system of request to open turnout and horn feedback in the non-centralized area,not strictly implementing the″four must″system for shunting service,illegal interventions of hump slip operation,and improper following or standing position in the shunting service.The warning threshold of the derailment accident is 0.0361.