A Big Service with Network Represent Learning for Quantified Flight Delay Prediction

An air traffic network is a special and complex Spatio-temporal network. What makes it unique is that multi-data sources-including airports, airlines and air routes-spatial dependence and strong temporal dependence in a dynamic environment. In this paper, we use big service to predict the flight departure delay time in air traffic networks. In the local services layer, we use graph sequences to model the Spatiotemporal network from multi-data sources, what is, using graphs to model the spatial dependence, and using sequences to model the temporal dependence. In the domain-oriented services layer, we use graph neural network to embed the graph sequence. We validate the method on an air Spatiotemporal network. Then, we use the embedding to estimate the departure delay time of the flight based on real-time conditions. In the demand-oriented services layer, we design a weighted cross entropy loss function and use a special evaluation to predict the flight departure delay time by the embedding in the domain-oriented services layer. Evaluated through a series of experiments on a real-world data set, we show that the method produces an effective result on the Spatio-temporal network which is substantially better than state-of-the-art alternative task: flight delay estimation. And it performs well in predicting the departure delay time with a total accuracy of 0.87.