Trajectory prediction (TP) plays an important role in supporting the decision-making of Air Traffic Controllers (ATCOs). Traditional TP methods are deterministic and physics-based, with parameters that are calibrated using aircraft surveillance data harvested across the world. These models are, therefore, agnostic to the intentions of the pilots and ATCOs, which can have a significant effect on the observed trajectory, particularly in the lateral plane. This work proposes a generative method for lateral TP, using probabilistic machine learning to model the effect of the epistemic uncertainty arising from the unknown effect of pilot behaviour and ATCO intentions. The models are trained to be specific to a particular sector, allowing local procedures such as coordinated entry and exit points to be modelled. A dataset comprising a week's worth of aircraft surveillance data, passing through a busy sector of the United Kingdom's upper airspace, was used to train and test the models. Specifically, a piecewise linear model was used as a functional, low-dimensional representation of the ground tracks, with its control points determined by a generative model conditioned on partial context. It was found that, of the investigated models, a Bayesian Neural Network using the Laplace approximation was able to generate the most plausible trajectories in order to emulate the flow of traffic through the sector.

翻译：航迹预测（TP）在支持空中交通管制员（ATCOs）决策中扮演着重要角色。传统TP方法基于确定性物理模型，其参数通过全球采集的航空器监视数据校准。因此，这些模型对飞行员与管制员意图不敏感——而这类意图对观测航迹（尤其是在水平面内）具有显著影响。本文提出一种用于水平方向TP的生成方法，采用概率机器学习建模由飞行员行为及管制指令未知影响引起的认知不确定性。该模型针对特定扇区进行训练，能够对协调进出点等局部管制程序进行建模。研究使用了穿越英国高空繁忙扇区的一周航空器监视数据集进行模型训练与测试。具体而言，采用分段线性模型作为地面航迹的功能性低维表示，其控制点由基于部分情境条件的生成模型确定。研究发现，在所研究的模型中，采用拉普拉斯近似的贝叶斯神经网络能够生成最合理的航迹，从而模拟扇区内的交通流。