空中交通管制员的工作负荷预测长期依赖粗粒度模型，这篇论文用图网络+概率方法把精度提了一个台阶。做航空调度、空域管理的团队可以直接参考其算法思路，提前45分钟预判复杂度比传统方法更准。
该研究提出了一种基于图模型的概率方法，用于预测英国伦敦中部扇区（LMS）的空中交通复杂度。通过改进现有过滤算法，识别需要管制员监控或解冲突的相关飞机对，作为管制员工作负荷的代理指标。新算法在50个标记场景上的F1分数从0.69提升至0.84。研究构建了LMS航路网络的图表示，并建模飞机到达时间的不确定性，可提前45分钟预测管制员工作负荷。该方法与真实相关交互的斯皮尔曼相关系数为0.68，优于传统交通量预测的0.55，为扇区配置和人员排班提供了数据驱动工具。
Graph-based Complexity Forecasts in UK En Route Airspace Using Relevant Aircraft Interactions
Effectively managing Air Traffic Control Officer (ATCO) workload is crucial in maintaining operational safety. Group supervisors use tools that estimate upcoming traffic load to aid decision-making. However, industry-standard models can fail to capture the nuances of upcoming air traffic complexity. This study presents a probabilistic approach to forecast the complexity of an airspace sector using the number of relevant aircraft pairs, i.e., those that require monitoring or deconfliction by a controller, as a proxy measure for ATCO workload. We adapted an existing filter algorithm to make it suitable for use in London Middle Sector (LMS), a complex airspace sector with multiple flows of traffic above some of the busiest airports in Europe. Through iterative feedback with ATCOs, the algorithm was refined and extended to handle specific geometric and operational considerations. The updated algorithm outperformed the original, with an F1-score of 0.84 compared to 0.69 on a labelled set of 50 traffic scenarios. To produce forecasts of future numbers of relevant aircraft pairs in the sector, a graph representation of the LMS route network was constructed, standardising the spatial fidelity of route legs. The forecasting method accounts for uncertainty in aircraft arrival times by modelling the probability of each aircraft occupying route segments at future query times. When combined with historic distributions of relevant interactions and a live operational data stream, predictions of upcoming ATCO workload could be made up to 45 minutes in advance. The proposed method to forecast upcoming workload showed a significantly stronger correlation with actual relevant interactions (Spearman's $ρ= 0.68$) than a standard traffic volume prediction ($ρ= 0.55$). The resulting data-driven tool shows promise for use by group supervisors to inform sector configuration and ATCO rostering decisions.