Improved Flight Time Predictions for Fuel Loading Decisions of Scheduled Flights with a Deep Learning Approach

Under increasing economic and environmental pressure, airlines are constantly seeking new technologies and optimizing flight operations to reduce fuel consumption. However, the current policy on fuel loading, which has a significant impact on aircraft weight, leaves room for improvement. Excess fuel is loaded by dispatchers and(or) pilots to ensure safety because of fuel consumption uncertainties, primarily caused by flight time uncertainties, which cannot be predicted by current Flight Planning Systems (FPS). In this paper, we develop a novel spatial weighted recurrent neural network model to provide better flight time predictions by capturing air traffic information at a national scale based on multiple data sources, including Automatic Dependent Surveillance - Broadcast, Meteorological Airdrome Reports, and airline records. In this model, we adopt recurrent neural network layers to extract spatiotemporal correlations between features utilizing the repetitive traffic patterns and interacting elements in aviation traffic networks. A spatial weighted layer is introduced to learn origin-destination (OD) specific features, and a two-step training procedure is introduced to integrate individual OD models into one model for a national air traffic network. This model was trained and tested using one year of historical data from real operations. Results show that our model can provide a more accurate flight time predictions than the FPS and the LASSO methods, especially for flights with extreme delays. We also show that with the improved flight time prediction, fuel loading can be optimized to reduce fuel consumption by 0.83% for an example airline's fleet without increasing the fuel depletion risk.