基于多数据源的CTC调度台工作负荷研究及评定

The Analysis and Evaluation of CTC Dispatchers Workload Based on Multiple Data Sources

为准确划分CTC调度台的繁忙程度,提出了一种通过CTC调度台的基础数据对调度台进行划分的方法。参照工作负荷研究中常用的方法,即主观评价法和任务分析法,结合统计学方法中的聚类分析,对某铁路集团有限公司中的17个CTC调度台进行繁忙程度划分。为有效地划分繁忙度,首先,在任务分析法详细分析工作内容的基础上,通过相关分析和回归分析探讨基础数据和工作负荷的关系;然后,基于调度台的基础数据,采用聚类分析中的K-means聚类算法对CTC调度台的工作负荷进行繁忙度划分;最后通过对CTC调度员心理负荷和工作压力的主观评价数据的方差分析来验证聚类划分调度台繁忙度的科学合理性。结果表明:聚类分析后的繁忙、普通和轻松台之间在主观评价工作负荷上都有显著差异。

A busyness dividing method is proposed based on preliminary data from CTC(Centralized Traffic Control)dispatching stations for the accurate division of dispatchers’workloads.Based on the methods typically used in workload research,namely subjective evaluation,task analysis,and combing with clustering for statistical analysis,the degree of busyness was differentiated for 17 CTC dispatching stations in a railway bureau.The relationship between the preliminary data and workload was examined through correlation and regression analysis for high-effectivity differentiation based on workload analys is via task analysis.Subsequently,based on initial data from the dispatching station,the k-means clustering algorithm was applied to classify busyness using the workload of the 17 CTC dispatching stations.Finally,one-way ANOVA was applied to determine the difference in the subjective evaluation data between the assigned workload and work stress experienced by the CTC dispatchers.Furthermore,through cluster analysis,the scientific rationale of the busyness division method for the dispatching station was verified.The results show significant differences in the subjective workloads among busy,typical,and dormant stations after cluster analysis.