In air traffic control, the airspace is divided into several smaller sectors for better management of air traffic and air traffic controller workload. Such sectors are usually managed by a team of two air traffic controllers: planning controller (D-side) and executive controller (R-side). D-side controller is responsible for processing flight-plan information to plan and organize the flow of traffic entering the sector. R-side controller deals with ensuring safety of flights in their sector. A better understanding and predictability of D-side controller actions, for a given traffic scenario, may help in automating some of its tasks and hence reduce workload. In this paper, we propose a learning model to predict D-side controller actions. The learning problem is modeled as a supervised learning problem, where the target variables are D-side controller actions and the explanatory variables are the aircraft 4D trajectory features. The model is trained on six months of ADS-B data over an en-route sector, and its generalization performance was assessed, using crossvalidation, on the same sector. Results indicate that the model for vertical maneuver actions provides highest prediction accuracy (99%). Besides, the model for speed change and course change action provides predictability accuracy of 80% and 87%, respectively. The model to predict the set of all the actions (altitude, speed, and course change) for each flight achieves an accuracy of 70% implying for 70% of flights; D-side controller’s action can be predicted from trajectory information at sector entry position. In terms of operational validation, the proposed approach is envisioned as ATCO assisting tool, not an autonomous tool. Thus, there is always ATCO discretion element, and as more ATCO actions are collected, the models can be further trained for better accuracy. For future work, we will consider expanding the feature set by including parameters such as weather and wind. Moreover, human in the loop simulation will be performed to measure the effectiveness of the proposed approach.

中文翻译：

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基于机器学习方法的空中交通管制员动作提取-预测模型

在空中交通管制中，将空域划分为几个较小的区域，以更好地管理空中交通和空中交通管制员的工作量。这些部门通常由两个空中交通管制员组成的团队进行管理：计划管制员（D端）和执行管制员（R端）。D侧控制器负责处理飞行计划信息，以计划和组织进入该部门的交通流量。R侧控制器负责确保其所在区域的航班安全。对D有更好的理解和可预测性对于给定的流量情况，侧控制器操作可能有助于自动化其某些任务，从而减少工作量。在本文中，我们提出了一种学习模型来预测D侧控制器的动作。学习问题被建模为监督学习问题，其中目标变量为D侧控制器动作和解释变量是飞机4D轨迹特征。该模型在一个航路扇区上接受了六个月的ADS-B数据训练，并使用交叉验证对同一扇区上的泛化性能进行了评估。结果表明，垂直机动动作模型可提供最高的预测准确性（99％）。此外，速度变化和路线变化动作的模型分别提供了80％和87％的可预测性精度。该模型可预测每次飞行的所有动作（高度，速度和航向变化）的集合，该模型可达到70％的准确度，这意味着70％的飞行；d可以根据扇区入口位置处的轨迹信息来预测侧控制器的动作。在操作验证方面，设想的方法是ATCO辅助工具，而不是自主工具。因此，总有ATCO酌处权元素，并且随着收集更多ATCO动作，可以进一步训练模型以获得更好的准确性。在以后的工作中，我们将考虑通过包括天气和风等参数来扩展功能集。此外，将执行循环仿真中的人员以测量所提出方法的有效性。