基于VTS数据挖掘的地铁司机关键认知能力因素识别  

Identification of key cognitive ability factors for metro train drivers based on VTS data mining

作　　者：施展旺 杨聚芬 朱海燕[1] SHI Zhanwang;YANG Jufen;ZHU Haiyan(School of Urban Railway Transportation,Shanghai University of Engineering Science,Shanghai 201620,China)

机构地区：[1]上海工程技术大学城市轨道交通学院,上海201620

出　　处：《上海工程技术大学学报》2024年第4期375-381,共7页Journal of Shanghai University of Engineering Science

基　　金：国家自然科学基金资助(52302438)。

摘　　要：运用维也纳测试系统(Vienna test system,VTS)对354名地铁司机的认知能力进行测评,通过K-means聚类算法对VTS数据进行无监督学习建模,得到司机认知能力分类模型。以Recall值最大为目标函数,对认知能力分类模型进行XGBoost训练和优化,采用SHAP算法对模型中各项认知能力特征指标的重要度进行分析,识别出平均反应时间、正确总数和视野范围三项关键因素以及它们之间的交互作用。研究结果用于认知与应急能力领域,可为地铁司机的遴选、在岗测评和培训提供一种更精确的工具。Cognitive abilities of 354 metro train drivers were assessed by using the Vienna test system(VTS).An unsupervised learning model was developed through K-means clustering algorithm on the VTS data to establish a cognitive ability classification model.With the maximum Recall value as the objective function,XGBoost training and optimization were performed on the classification model.SHAP algorithm was employed to analyze the importance of various cognitive ability feature indicators in the model,and three key factors that mean reaction time,total correct responses,visual field range,as well as their interactions were identified.The research results can provide a more precise tool for the selection,on-the-job assessment,and training of metro train drivers when applied to the field of cognition and emergency capabilities.

关 键 词：地铁司机 认知能力 维也纳测试系统 数据挖掘 特征重要性分析 

分 类 号：U298[交通运输工程—交通运输规划与管理]