A CNN-LSTM framework for flight delay prediction

Flight delay prediction has become one of the most critical topics in intelligent aviation systems due to its essential role in flight scheduling, airline planning, and airport operation. The accurate prediction of flight delays is very challenging because numerical factors will affect flight delays. Moreover, owing to the connectivity of the aviation system, flight delays also present complex spatial-temporal correlations, including the spatial correla-tions between airports along with the temporal correlations among timestamps. To address these challenges, we proposed a CNN-LSTM deep learning framework to consider the spatial-temporal correlations together with the extrinsic features for flight delay prediction. The CNN-LSTM model consists of a Convolution neural network (CNN) architecture to learn the spatial correlations followed by a Long short-term memory (LSTM) architecture to capture the temporal correlations. The spatial-temporal correlations obtained from the CNN-LSTM framework are then fused with the extrinsic features (e.g., airline issues, distance, schedule fly time, etc.) as inputs of the random forest (RF) model for flight delay prediction. The U.S. domestic flights in 2019 are collected from the Bureau of Transport Statistics to confirm the outperformance of the proposed model. The results show that the accuracy of the CNN-LSTM model reaches 92.39%. For the on-time samples, approximately 91% are correctly identified; for the delayed samples, the classification accuracy reaches 84% which exhibits better performance compared with several benchmark models. The created prediction model of this study could provide useful in-formation for airport regulators in understanding the potential delays in advance and developing effective airport management strategies for improving airport on-time performance.