基于贝叶斯网络的高速铁路行车调度指挥人因可靠性研究

A Study on Human Reliability of High Speed Railway Traffic Dispatching Command Based on Bayesian Network

高速铁路行车调度系统在整个高铁运输大系统中处于核心位置,虽然行车调度大多已经采用了自动化的作业流程,但在设备故障、不良天气等突发情况时,许多具体的作业组织和管理仍需行车调度指挥人员的干预。结合人因失误因果概念模型和贝叶斯网络分析方法,考虑情境环境状态因子交互关系的人因可靠性分析方法,以成都铁路局的人为失误数据统计表作为分析样本,找出行车调度人员工作中存在的主要问题。结果表明:"人员配置"、"完成任务的可用时间"及"培训"的节点状态变化会对作业人员的可靠性产生很大影响,说明所建立的方法准确可靠,能够高效地辨识人因可靠性。

High speed railway traffic control system is the core component in the whole high-speed rail transportation system. Although it has implemented the automatic dispatching workflow, railway management still has to involve personnel intervention, especially in unexpected disruptions such as equipment breakdown, extreme weather, etc. This study applies the human-error-concept model and Bayesian-network-analysis method to analyze the human error data of Chengdu Railway Bureau, and find the main reliability issues of personnel dispatching. The results show that the following factors such as ＂the allocation of personnel＂, ＂available working time＂ and ＂personnel training＂ have significant impacts on traffic dispatching reliability. The proposed method is validated to be applicable to efficiently identify human reliability factors in high speed railway traffic dispatching.