@article{GU2026, 
author = {Qiuli GU and Lili WANG},
title = {Analysis of individual differences in controller workload},
year = {2026},
journal = {Journal of Beijing University of Aeronautics and Astronautics},
volume = {52},
number = {7},
pages = {2414-2424},
keywords = {air traffic controllers, individual workload, entropy weight-CRITIC combination method, paired samples T-test, one-way analysis of variance},
url = {https://www.sciopen.com/article/10.13700/j.bh.1001-5965.2024.0351},
doi = {10.13700/j.bh.1001-5965.2024.0351},
abstract = {To address the issue of individual variability in workload tolerance, this study establishes a quantitative model for assessing controller workload. A test was devised for the purpose of collecting data from 24 area controllers both before and after their workday. Based on the test data, variables were selected for analysis that were deemed to be sensitive in terms of describing the individual load. Three dimensions were included in the comprehensive evaluation index system: cognitive workload, physiological reaction load, and psychological perception load. A model for the individual load index of controllers was developed. The optimal weights of the individual load index were determined through the application of the entropy weight-CRITIC combination method. The individual workload index of each controller was subsequently calculated. It was determined that there are notable discrepancies in the individual workload of controllers. The cognitive workload index is a principal indicator of the magnitude of the individual workload index for controllers. The cognitive workload is a principal index for determining the magnitude of the controller’s individual workload index. Additionally, there is a positive correlation between cognitive workload and the controller’s capacity for information retrieval, decision-making, and reaction. To further investigate the factors influencing the growth of controllers’ cognitive workload, the reaction time, gaze time, and sweep time of controllers under five distinct traffic levels were quantified. Additionally, pre-post and post-post paired tests were conducted, and one-way analysis of variance was performed between groups with the same indicator. The results showed that while controllers’ reaction ability was more affected by flow parameters, their decision-making and searching abilities were more vulnerable to cumulative load.}
}