Trajectory prediction is an essential component of the next generation national air transportation system. Reliable trajectory prediction models need to consider uncertainties coming from multiple sources. Environmental factor is one of the most significant reasons affecting trajectory prediction models and is the focus of this study. This paper propose an advanced Bayesian Deep Learning method for aircraft trajectory prediction considering weather impacts. A brief review of both deterministic and probabilistic trajectory prediction methods is given, with a specific focus on learning-based methods. Next, a deterministic trajectory prediction model with classical deep learning methods is proposed to handle both spatial and temporal information using a nested convolution neural network, recurrent neural network, and fully-connected neural network. Following this, the deterministic neural network model is extended to be a Bayesian deep learning model to consider uncertainties where the posterior distributions of parameters are estimated with variational inference for enhanced efficiency. Both mean prediction and confidence intervals are obtained giving the last on-file flight plans and weather data in the region. The proposed methodology is validated using air traffic and weather data from the Sherlock data warehouse. Data pre-processing procedures for big data analytics are discussed in detail. Demonstration and metrics-based validation are performed during severe convective weather conditions for several air traffic control centers. The results show a significant reduction in prediction variance. A comparison with existing methods is also performed. Several conclusions and future works are given based on the proposed study.

轨迹预测是下一代国家航空运输系统的重要组成部分。可靠的轨迹预测模型需要考虑来自多个来源的不确定性。环境因素是影响轨迹预测模型的最重要原因之一，也是本研究的重点。本文提出了一种先进的贝叶斯深度学习方法，用于考虑天气影响的飞机轨迹预测。给出了确定性和概率轨迹预测方法的简要回顾，特别关注基于学习的方法。接下来，提出了一种具有经典深度学习方法的确定性轨迹预测模型，以使用嵌套卷积神经网络、循环神经网络和全连接神经网络处理空间和时间信息。在此之后，确定性神经网络模型被扩展为贝叶斯深度学习模型，以考虑不确定性，其中使用变分推理估计参数的后验分布以提高效率。获得平均预测和置信区间，给出该地区最后存档的飞行计划和天气数据。提议的方法使用来自 Sherlock 数据仓库的空中交通和天气数据进行验证。详细讨论了大数据分析的数据预处理程序。几个空中交通管制中心在强对流天气条件下进行了演示和基于指标的验证。结果显示预测方差显着减少。还进行了与现有方法的比较。