Prediction of multiple runway configurations for efficient flight operations by hybrid LSTM and transformer model.

ABSTRACT: With the continuous growth of civil aviation traffic, traditional manual decision-making approaches for runway configuration struggle to effectively respond to sudden weather changes and temporary flight traffic fluctuations, resulting in low utilization of airport runway resources and significantly impaired operational efficiency. To address this problem, this study proposes a multi-source data fusion framework for airport runway configuration prediction, integrating weather, flight operation and other data. The research begins by identifying three primary data sources: temporal, meteorological, and flight operation that exhibit the highest correlation with runway configuration. Subsequently, a novel Long Short-Term Memory-Transformer (LSTM-Transformer) prediction model is developed, inco

SUBMITTER: Wang X 

PROVIDER: S-EPMC12572324 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature