A Flight Delay Prediction Model Based on Improved Spatio-Temporal Synchronous Graph Convolutional Networks

Flight delay prediction is highly complex due to multiple influencing factors, including weather conditions, aircraft types, and airline strategies, with delays in preceding flights potentially causing cascading effects on subsequent flights. This paper proposes a flight delay prediction model based on an improved Spatio-Temporal Synchronous Graph Convolutional Network (STSGCN), incorporating collaborative methods and mechanism design to enhance the model's ability to process and predict using multi-source data. First, flight features are integrated with external features such as weather conditions, aircraft types, and airline codes to construct dynamic graph-structured data, enhancing the comprehensiveness of feature representation. Second, the model introduces a spatio-temporal convolutional network with attention mechanisms, collaboratively capturing complex dependencies in both temporal and spatial dimensions. Finally, flight chain features are integrated to enable a two-stage prediction of flight delays. Experimental results show that, compared with the existing model STSGCN, the proposed model reduces the average error in single flight delay prediction by 6 %, achieving 6.63 minutes, and has significant improvements in delay propagation pattern recognition and accuracy.