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This study investigates the potential of improving flight trajectory prediction by incorporating meteorological factors, such as wind direction and wind speed into models that utilize Automatic Dependent Surveillance Broadcast (ADS-B) data. The random forest regression model is considered and tested using three different datasets. Evaluation is conducted based on the Mean Absolute Error (MAE) to assess positional prediction performance. The evaluation results highlight the complexity of capturing environmental influences and suggest directions for further investigation. From this study, altitude served as a critical indicator for estimating the direction of runway entry improving model accuracy.

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Toward Accurate Flight Trajectory Prediction: Insights from ADS-B and Wind Data Integration

  • 会议论文

  • 发表日期：

    2025年

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  • Koichi Kakimoto,
  • Kenta Seida,
  • Makoto Ikeda,
  • Leonard Barolli

摘要

This study investigates the potential of improving flight trajectory prediction by incorporating meteorological factors, such as wind direction and wind speed into models that utilize Automatic Dependent Surveillance Broadcast (ADS-B) data. The random forest regression model is considered and tested using three different datasets. Evaluation is conducted based on the Mean Absolute Error (MAE) to assess positional prediction performance. The evaluation results highlight the complexity of capturing environmental influences and suggest directions for further investigation. From this study, altitude served as a critical indicator for estimating the direction of runway entry improving model accuracy.