@article{ZHAO2026, 
author = {Yongkang ZHAO and Zuolong LIU and Xia FENG},
title = {FTCA-Transformer: A method for predicting flight off-block time},
year = {2026},
journal = {Acta Aeronautica et Astronautica Sinica},
volume = {47},
number = {5},
keywords = {air transportation, airport collaborative decision-making, milestones in flight ground support, prediction of flight off-block time, time convolutional network, Gibbs phenomenon, frequency enhanced channel attention},
url = {https://www.sciopen.com/article/10.7527/S1000-6893.2025.32357},
doi = {10.7527/S1000-6893.2025.32357},
abstract = {Accurate prediction of the Actual Off-Block Time (AOBT) is a crucial component for optimizing airport operational efficiency and enhancing the performance of Airport Collaborative Decision-Making (A-CDM) systems. To address the challenges posed by complex spatiotemporal dependencies and Gibbs phenomena in time-series variations, this study proposes a hybrid prediction model-FTCA-Transformer-integrating a Frequency-enhanced Channel Attention mechanism with a Temporal Convolutional Network (TCN) and Transformer architecture. First, milestone events in ground handling operations are encoded into unified sequential data, enabling the extraction of hidden temporal and seasonal features across timestamps. Next, the TCN captures local temporal segment information, while the multi-head self-attention mechanism in the Transformer extends temporal correlations from individual points to segment-level dependencies, overcoming the limitations of recurrent neural networks in modeling long-range dependencies. Furthermore, a Frequency-enhanced Channel Attention mechanism based on Discrete Cosine Transform (DCT) is introduced to effectively exploit frequency-domain features of ground operation data while mitigating Gibbs phenomena associated with conventional frequency transforms. Finally, experimental results using real ground handling operation data from a major hub airport in North China demonstrate that, under single-step prediction, the proposed FTCA-Transformer outperforms baseline methods, reducing the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE) by 48.0%, 53.5% and 44.7%, respectively. Ablation studies further confirm the positive contributions of each module within the model.}
}