论文标题
对飞行需求预测的深度学习
Deep Learning for Flight Demand Forecasting
论文作者
论文摘要
受自然语言处理（NLP）的深度学习成功（DL）的启发，我们应用了尖端的DL技术来预测战略时间范围（4小时或更长时间）的飞行出发需求。这项工作是为了支持MITRE开发的移动应用程序PACER，该应用程序向通用航空（GA）飞行运营商展示了预测的出发需求，因此他们可以更好地了解忙碌期间出发延迟的可能性。涉及Pacer先前设计的基于规则的预测方法的现场演示表明，出发需求的预测准确性仍然有改进的空间。这项研究旨在提高两个关键方面的预测准确性：更好的数据源和鲁棒的预测算法。我们利用了两个数据源：航空系统性能指标（ASPM）和系统广泛的信息管理（游泳）作为我们的输入。然后，我们以注意序列（SEQ2SEQ）和SEQ2SEQ的DL技术训练了预测模型。案例研究表明，我们的SEQ2SEQ具有注意力为四种预测算法中的表现最好。此外，与经典自回旋（AR）预测方法相比，具有更好的数据源，具有注意力的SEQ2SEQ可以减少平均平方误差（MSE）超过60％。
Inspired by the success of deep learning (DL) in natural language processing (NLP), we applied cutting-edge DL techniques to predict flight departure demand in a strategic time horizon (4 hours or longer). This work was conducted in support of a MITRE-developed mobile application, Pacer, which displays predicted departure demand to general aviation (GA) flight operators so they can have better situation awareness of the potential for departure delays during busy periods. Field demonstrations involving Pacer's previously designed rule-based prediction method showed that the prediction accuracy of departure demand still has room for improvement. This research strives to improve prediction accuracy from two key aspects: better data sources and robust forecasting algorithms. We leveraged two data sources, Aviation System Performance Metrics (ASPM) and System Wide Information Management (SWIM), as our input. We then trained forecasting models with DL techniques of sequence to sequence (seq2seq) and seq2seq with attention. The case study has shown that our seq2seq with attention performs best among four forecasting algorithms tested. In addition, with better data sources, seq2seq with attention can reduce mean squared error (mse) over 60%, compared to the classical autoregressive (AR) forecasting method.
