基于扇区复杂性数据的管制员工作负荷评估#br#

中国民航大学学报 ›› 2019, Vol. 37 ›› Issue (4): 5-10.

基于扇区复杂性数据的管制员工作负荷评估#br#

许辰澄，赵征，胡明华，江斌，孔航

（南京航空航天大学民航学院，南京210016）

出版日期:2019-08-23 发布日期:2020-04-01
作者简介:许辰澄（1995—），男，上海人，硕士研究生，研究方向为空中交通运输规划与管理.
基金资助:
国家自然科学基金项目（U1833126）

Controller workload assessment based on sector complexity data analysis#br#

XU Chencheng, ZHAO Zheng, HU Minghua, JIANG Bing, KONG Hang#br#

（College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

Online:2019-08-23 Published:2020-04-01

摘要/Abstract

摘要： 为准确有效地评估管制员的工作负荷，首先通过调查问卷选取了13 个扇区的数据指标，并通过雷达管制模拟实验收集扇区复杂性数据和管制员对于自身工作负荷的主观自评估值；然后根据数据运用因子分析与主成分分析法将扇区数据指标降维，简化成具有代表性的新主成分并作为自变量；再采用回归方程来建立管制员工作负荷预测模型，并得出管制员工作负荷的预测值；最后将客观数据分析得到的预测值与管制员工作负荷的主观自评估值相比较，验证了模型的准确性。

关键词: 管制员工作负荷, 复杂性指标, 因子分析, 主成分分析, 降维, 回归模型

Abstract: In order to effectively and accurately assess controller workload, data indicators of 13 sectors are firstly selected through questionnaire. Sector complexity data and controllers’ subjective self -evaluation value for their own workload are collected through radar control simulation experiments. Then, dimension reduction of sector data indices has been conducted through data application factor analysis and principal component analysis, which is simplified into a representative new principal component and used as an independent variable. After that, the regression equation is used to establish the controller workload prediction model, and the predicted value of the controller workload is obtained from the model. Finally, comparison between predicted value obtained from objective data analysis with the subjective self -evaluation value of controller workload verifies the model accuracy.

Key words: controller workload, complexity index, factor analysis, principal component analysis, dimension reduction, regression model

中图分类号:

V355.1

许辰澄, 赵征, 胡明华, 江斌, 孔航. 基于扇区复杂性数据的管制员工作负荷评估#br#[J]. 中国民航大学学报, 2019, 37(4): 5-10.

XU Chencheng, ZHAO Zheng, HU Minghua, JIANG Bing, KONG Hang. Controller workload assessment based on sector complexity data analysis#br#[J]. Journal of Civil Aviation University of China, 2019, 37(4): 5-10.

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