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March 29, 2026AerospaceOpen Access

DDA-SIM-ATT: A Synergistic Multi-Module Fusion Model for High-Precision Prediction of Departure Flight Taxi-Out Time

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Authors

YLYue LuYLYanzhi LiQZQingwei Zhong

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Overview

This model predicts taxi-out time in flights, improving airport efficiency and reducing delays.

Key Points

  • The aim is to enhance the accuracy of predicting departure flight taxi-out times by addressing data challenges.
  • Proposed a multi-module fusion model named DDA-SIM-ATT-CatBoost.
  • Implemented a Dynamic Data Augmentation module to mitigate data imbalance.
  • Utilized a Similarity Theory module for precise historical pattern matching.
  • Applied an Attention Mechanism module to recalibrate feature weights.
  • Conducted experiments using real-world departure data from a major airport.
  • Achieved 74.57%, 89.12%, and 97.76% prediction accuracies within specified error margins.
  • Obtained Mean Absolute Percentage Error of 10.34%, Mean Absolute Error of 87.55 s, and RMSE of 125.61 s.
  • Demonstrated significant performance improvement over baseline models like XGBoost and Random Forest.

Cite This Study

Lu et al. (2026) studied this question.

synapsesocial.com/papers/69c8c2b8de0f0f753b39d289https://doi.org/10.3390/aerospace13040314
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