预测天空：一种用于航班层级客流预测的新模型
Predicting the Skies: A Novel Model for Flight-Level Passenger Traffic Forecasting
AI总结：
本研究提出一种整合RNN和CNN的多模态深度学习模型，通过构建3D张量等数据处理策略预测航班层级客流量，相比传统基准在MSE上提升约33%。
摘要：
准确预测航班层级客流量在航空公司运营中至关重要，影响从定价到航线优化的关键决策。本研究提出了一种新颖的多模态深度学习方法来应对预测航班层级客流量的挑战，与传统的模型相比，取得了显著的准确性提升。利用来自 American Airlines 的庞大数据集，我们的模型吸收了历史流量数据、票价关闭信息以及特定于每个航班的季节性属性。我们提出的神经网络整合了 Recurrent Neural Networks (RNN) 和 Convolutional Neural Networks (CNN) 的优势，利用数据内的时间模式和空间关系来增强预测性能。我们模型成功的关键在于全面的数据处理策略。我们构建 3D 张量来表示数据，应用仔细的掩码策略以反映现实世界的动态，并采用数据增强技术来丰富我们训练集的多样性。我们方法的有效性在结果中得到了证明：与传统的基准相比，我们的模型在 Mean Squared Error (MSE) 上表现出约 33% 的提升。因此，本研究突显了深度学习技术和细致的数据处理在推进航班流量预测领域的巨大潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
