Accurate prediction of flight-level passenger traffic is of paramount
importance in airline operations, influencing key decisions from pricing to
route optimization. This study introduces a novel, multimodal deep learning
approach to the challenge of predicting flight-level passenger traffic,
yielding substantial accuracy improvements compared to traditional models.
Leveraging an extensive dataset from American Airlines, our model ingests
historical traffic data, fare closure information, and seasonality attributes
specific to each flight. Our proposed neural network integrates the strengths
of Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN),
exploiting the temporal patterns and spatial relationships within the data to
enhance prediction performance. Crucial to the success of our model is a
comprehensive data processing strategy. We construct 3D tensors to represent
data, apply careful masking strategies to mirror real-world dynamics, and
employ data augmentation techniques to enrich the diversity of our training
set. The efficacy of our approach is borne out in the results: our model
demonstrates an approximate 33\% improvement in Mean Squared Error (MSE)
compared to traditional benchmarks. This study, therefore, highlights the
significant potential of deep learning techniques and meticulous data
processing in advancing the field of flight traffic prediction.

借助美国航空公司的大量数据，本研究提出了一种新颖的多模态深度学习方法，以准确预测航班客流量，相较传统模型获得了显著的准确性提升。将循环神经网络（RNN）和卷积神经网络（CNN）相结合，并利用数据的时间和空间关系来增强预测性能，同时运用全面的数据处理策略，如构建三维张量、谨慎的遮掩策略和数据增强技术。研究结果表明，相较于传统基准模型，该方法的均方误差（MSE）约提高了 33%。因此，本研究强调了深度学习技术和细致数据处理在航班客流量预测领域的重要潜力。