Predicting Future Potential Flight Routes via Inductive Graph Representation Learning

Air transport activity has been considered as an important part of modern societies with high demand for massive scale mobilizations. One of the fundamental challenges in air transportation is to predict future potential flight routes connecting newly developed cities around the world. In this paper, we aim to solve the flight routes prediction problem under graph theory perspective as a link prediction task by mapping the existing airports as graph vertices and flight routes as graph links. We generate low-dimensional feature vectors of vertices by learning the embedding function. For each selected vertex in the input network, we sample the neighborhood and aggregate feature information from neighbors to predict and generalize to unseen links and vertices. The effectiveness of our prediction approach is shown using the Open Flights database containing 568 airlines with 65,535 routes between 3,425 airports. The promising performance of 90% accuracy is gained by the prediction model.