基于贝叶斯网络的轨道交通司机胜任力评价  

Competency Evaluation of Rail Transport Drivers Based on Bayesian Networks

作　　者：褚福磊[1] 叶龙[1] 

机构地区：[1]北京交通大学经济管理学院,北京100044

出　　处：《中国铁道科学》2012年第5期127-131,共5页China Railway Science

基　　金：北京市自然科学基金资助项目(9113026);新世纪优秀人才支持计划资助项目(NCET-09-0210);中央高校基本科研业务费专项资金资助项目(2012YJS041)

摘　　要：根据调查分析得到轨道交通司机胜任力评价的4个一级评价指标和16个二级评价指标。将此评价指标作为变量,基于贝叶斯网络原理,构建司机胜任力贝叶斯网络。选取职业心理特征和职业理论特征作为中间节点,规章制度掌握程度、车辆理论知识水平、学习能力和复杂反应能力作为父节点,司机胜任力作为子结点,得到简化的司机胜任力子贝叶斯网络。利用该子贝叶斯网络对实例的分析结果表明:司机职业心理特征对司机胜任力影响较大;评价结果准确地反映了司机胜任力各项评价指标间的相互作用关系;该方法可为司机的选拔、培训和退役提供定量分析的依据。We obtained the competency evaluation indicators of rail transport drivers according to the survey analysis, and the indicators included 4 first-level evaluation indicators and 16 second-level evaluation indicators. To regard these indicators as variables, and based on the principles of Bayesian networks, the competence Bayesian networks of rail transport drivers were built. We chose vocational psychology characteristics and vocational theory characteristics as the intermediate nodes, the extent of mastering the rules and regulations, level of vehicle theoretical knowledge, learning ability and complexity response ability as father node, competency of rail transport drivers as child node, and then we built the simplified sub-Bayesian network of rail transport drivers' competency. Through analyzing the example by this sub-Bayesian network, the results show that vocational psychology characteristics play a more important role in the competence of rail transport drivers. The evaluation results can accurately reflect the interactions among all the evaluation indicators for the competence of rail transport drivers. The method can provide a quantitative analysis basis for rail transport drivers' selection, training and retirement.

关 键 词：轨道交通 贝叶斯网络 司机胜任力 评价指标 概率 

分 类 号：U298.1[交通运输工程—交通运输规划与管理] X910[交通运输工程—道路与铁道工程]