  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. 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 Aerodrome
Reports, and airline records. In this model, a spatial weighted layer is
designed to extract spatial dependences among network delay states. Then, a new
training procedure associated with the spatial weighted layer is introduced to
extract OD-specific spatial weights. Long short-term memory networks are used
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 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 in reduced fuel
consumption by 0.016%-1.915% without increasing the fuel depletion risk.
