Air traffic control (ATC) is a safety-critical service system that demands constant attention from ground air traffic controllers (ATCos) to maintain daily aviation operations. The workload of the ATCos can have negative effects on operational safety and airspace usage. To avoid overloading and ensure an acceptable workload level for the ATCos, it is important to predict the ATCos' workload accurately for mitigation actions. In this paper, we first perform a review of research on ATCo workload, mostly from the air traffic perspective. Then, we briefly introduce the setup of the human-in-the-loop (HITL) simulations with retired ATCos, where the air traffic data and workload labels are obtained. The simulations are conducted under three Phoenix approach scenarios while the human ATCos are requested to self-evaluate their workload ratings (i.e., low-1 to high-7). Preliminary data analysis is conducted. Next, we propose a graph-based deep-learning framework with conformal prediction to identify the ATCo workload levels. The number of aircraft under the controller's control varies both spatially and temporally, resulting in dynamically evolving graphs. The experiment results suggest that (a) besides the traffic density feature, the traffic conflict feature contributes to the workload prediction capabilities (i.e., minimum horizontal/vertical separation distance); (b) directly learning from the spatiotemporal graph layout of airspace with graph neural network can achieve higher prediction accuracy, compare to hand-crafted traffic complexity features; (c) conformal prediction is a valuable tool to further boost model prediction accuracy, resulting a range of predicted workload labels. The code used is available at \href{https://github.com/ymlasu/para-atm-collection/blob/master/air-traffic-prediction/ATC-Workload-Prediction/}{$\mathsf{Link}$}.

翻译：空中交通管制（ATC）是一项安全保障型服务系统，要求地面管制员持续关注以维持日常航空运行。管制员的工作负荷可能对运行安全和空域使用产生负面影响。为避免超负荷运行并确保管制员处于可接受的工作负荷水平，准确预测其工作负荷以采取缓解措施至关重要。本文首先从空中交通视角对管制员工作负荷相关研究进行综述，随后简要介绍包含退休管制员参与的人机环仿真实验设置，从中获取空中交通数据与工作负荷标签。实验在三种凤凰城进近场景下开展，要求管制员自评工作负荷等级（低-1至高-7），并完成初步数据分析。在此基础上，本文提出一种基于图深度学习框架的共形预测方法，用于识别管制员工作负荷等级。管制员所控航空器数量在时空维度动态变化，形成动态演化图结构。实验结果表明：（a）除交通密度特征外，交通冲突特征（即最小水平/垂直间隔距离）有助于提升工作负荷预测能力；（b）相较于人工设计的交通复杂度特征，采用图神经网络直接从空域时空图结构中学习可获得更高预测精度；（c）共形预测是进一步提升模型预测精度的有效工具，可生成预测工作负荷的置信区间。实验代码详见：\href{https://github.com/ymlasu/para-atm-collection/blob/master/air-traffic-prediction/ATC-Workload-Prediction/}{$\mathsf{Link}$}。