交通运输系统工程与信息

交通运输系统工程与信息 ›› 2026, Vol. 26 ›› Issue (5): 297-308.DOI: 10.16097/j.cnki.1009-6744.2026.05.025

• 系统工程理论与方法 • 上一篇    下一篇

融合多模态数据的管制员个体工作负荷承受能力差异研究

顾秋丽a,b ，王莉莉*c   

  1. 中国民航大学，a.网络安全和信息化办公室；b.安全科学与工程学院；c.空中交通管理学院，天津300300
  • 收稿日期:2026-02-16 修回日期:2026-07-07 接受日期:2026-07-10 出版日期:2026-10-25 发布日期:2026-10-21
  • 作者简介:顾秋丽（1988—），女，辽宁锦州人，讲师。
  • 基金资助:
    波音中国合作项目 (H04120230018)；国家自然科学基金联合基金 (U1633124)。

Individual Workload Tolerance Differences Among Air Traffic Controllers Through Multimodal Data Fusion

GU Qiulia,b, WANG Lili*c   

  1. a. Network Security and Informatization Office; b. College of Safety Science and Engineering; c. College of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China
  • Received:2026-02-16 Revised:2026-07-07 Accepted:2026-07-10 Online:2026-10-25 Published:2026-10-21
  • Supported by:
    Boeing China Cooperation Project (H04120230018)；Joint Funds of the National Natural Science Foundation of China (U1633124)。

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摘要： 空中交通管制员的个体工作负荷承受能力是空管系统韧性的决定性因素。现有关于管制员工作负荷的研究均为群体均值模型，难以适配管制员的个体差异。本文选取24名一线管制员，在岗前与岗后不同状态下开展模拟管制对照实验，同步采集多模态生理数据对管制员一个工作周期的个体工作负荷承受能力开展量化研究。首先，采用加权偏最小二乘路径模型（Partial Least Squares Path Modeling,PLS-PM）将多维生理与眼动指标融合为表征工作负荷的潜变量（Latent Variable,LV），并构建其与交通流特征间的耦合关系；进而，基于贝叶斯信息准则（Bayesian Information Criterion, BIC）拟合分段线性回归模型（Piecewise Linear Regression Model, PWLF），为每位管制员标定潜变量初始阈值。在此基础上，引入基于标准化潜变量得分1.0σ的动态更新机制，确定自适应阈值，实现高负荷状态的精准标注及持续高负荷起始点的识别。最后，综合初始阈值、自适应阈值和持续高负荷起始点值(映射回流量坐标)，确定岗前与岗后个性化管制架次阈值，并通过岗前与岗后的纵向对比实现对管制员工作负荷承受能力的量化评估与分型。研究结果表明，不同管制员个体工作负荷承受能力存在差异，根据差异可将管制员划分为3类：“抗疲劳型”（占比20.8%）、“稳态维持型”（占比66.7%）和“疲劳敏感型”（占比12.5%），实际工作中应重点关注疲劳敏感型群体。该结果能够为管制单位优化人员岗位匹配策略提供参考。

关键词: 航空运输, 管制员个体差异, 多模态数据融合, 工作负荷承受能力

Abstract: The individual workload tolerance of air traffic controllers is the decisive determinant of air traffic control (ATC) system resilience. Existing studies on air traffic controller workload are largely based on group-level mean models, which are inadequate for capturing individual variability. To address this limitation, this paper recruited 24 frontline controllers and conducted simulated ATC experiments under contrasting pre-duty (alert) and post-duty (fatigued) conditions, while multimodal physiological data was collected simultaneously to quantify individual workload tolerance across a complete work cycle. First, a weighted Partial Least Squares Path Model (PLS-PM) was employed to fuse the multidimensional physiological and oculomotor indicators into a Latent Variable (LV) which representes comprehensive workload, thereby establishing its coupling relationship with traffic flow characteristics. Subsequently, guided by the Bayesian Information Criterion (BIC), a Piecewise Linear Regression Model (PWLF) was fitted to calibrate the personalized initial thresholds within the LV space for each controller. Building on this, a dynamic update mechanism-defined as 1.0 standard deviation (σ) above the mean of standardized latent scores-was introduced to determine adaptive thresholds, enabling precise labeling of high-workload states and identification of the onset point of sustained high workload. Finally, by integrating the initial thresholds, adaptive thresholds, and the onset point of sustained high workload, and mapping these back to traffic flow coordinates, we established personalized aircraft count thresholds for both pre-duty and post duty phases. Longitudinal within-subject comparisons facilitated the quantitative assessment and classification of workload tolerance profiles. Results revealed three distinct types: Fatigue-Resistant(20.8%), Steady-State Maintaining (66.7%), and Fatigue Sensitive(12.5%). Operational practice should give priority attention to the fatigue-sensitive group. These findings provide a valuable guidance for ATC units to optimize person-job matching strategies.

Key words: air transportation, individual differences of controllers, multimodal data fusion, workload tolerance

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