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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.