A multi-view attention-based spatial-temporal network for airport arrival flow prediction

Accurate airport arrival flow prediction is a precondition for intelligent air traffic flow management. However, most existing studies focus on the dynamic traffic flow in a single-airport scenario, which usually ignores the spatial interactions among airports. Modelling network-wide spatial dependencies among airports is difficult because it requires models to consider multiple underlying factors jointly. We propose a multi-view fusion approach to automatically learn an adjacency matrix from flight duration and flight schedule factors. The learned adjacency matrix is then fed into a specially designed graph convolutional block, which governs the message passing process among airports. Finally, the graph convolutional block with the learned adjacency matrix is embedded into the gated recurrent units to capture temporal dependencies. Experimental results on a real-world dataset for the multistep prediction task show the effectiveness and efficacy of the proposed model. In addition, visualisation and analysis of the learned adjacency matrix verify that the proposed multi-view fusion approach is capable of learning informative spatial interaction patterns.