Flight delay prediction from spatial and temporal perspective

In this paper, we propose a novel prediction framework (ST-Random Forest) for flight delay prediction from temporal and spatial perspective. We first apply complex network theory to extract the spatial feature of the aviation network at edge-, node-, and network-level. Furthermore, considering the temporal correlation of weather condition and airport crowdedness on flight delays, we create a prediction framework based on LSTM units to extract the temporal property of crowdedness and weather condition. Finally, we use the factors (e.g., spatial, temporal and extrinsic) that affect flight delays as inputs and apply Random Forest as classifier to predict flight delays. We apply and test our approach in a case study at China domestic flights between Jun and Aug 2016; after evaluation, we find that the accuracy of our proposed model reaches 92.39%. For the on-time samples, approximately 86% are correct identified; for the delayed samples, the classification accuracy reaches 95%. The ST-Random Forest model contributes to aviation authorities and airport regulators by creating real-time monitoring and high accuracy prediction system to alleviate flight delays and providing insightful suggestions to develop effective air traffic control strategies.