Flight time prediction for fuel loading decisions 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 practice on fuel loading, which has a significant impact on aircraft weight and fuel consumption, has yet to be thoroughly addressed by existing studies. Excess fuel is loaded by dispatchers and (or) pilots to handle 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 (ADS-B), Meteorological Aerodrome Reports (METAR), and airline records. In this model, a spatial weighted layer is designed to extract spatial dependences among network delay states (i.e. average flight delay at each airport and average flight delay of each Origin-Destination (OD) pair for a specific time interval). Then, a new training procedure associated with the spatial weighted layer is introduced to extract OD-specific spatial weights and then integrate into one model for a nationwide air traffic network. Long short-term memory (LSTM) networks are used after the spatial weighted layer to extract the temporal behavior patterns of network delay states. Finally, features from delays, weather, and flight schedules are fed into a fully connected neural network to predict the flight time of a particular flight. The proposed model was evaluated using one year of historical data from an airline’s real operations. Results show that our model can provide a more accurate flight time predictions than baseline methods, especially for flights with extreme delays. We also show that, with the improved flight time prediction, fuel loading can be optimized and resulting reduced fuel consumption by 0.016%–1.915% without increasing the fuel depletion risk.