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Airport traffic flow prediction is a fundamental research topic in the field of air traffic flow management. Most existing works focus on the single airport traffic flow prediction with temporal dynamics but fail to consider the influence of the topological airport network. In this paper, a novel deep learning-based framework, called airport traffic flow prediction network (ATFPNet), is proposed to capture spatial-temporal dependencies of the historical airport traffic flow (departure and arrival) for the multiple-step situational (network-level) arrival flow prediction. Firstly, considering the nature of the airport distribution and the context of air transportation, a special semantic graph built on the flight schedule is applied to represent the airport network, which is the key to encoding the situational airport traffic flow into a single representation. Then, the graph convolution operator and the gated recurrent unit are combined to extract high-level transition patterns of airport traffic flow in the spatial and temporal dimensions. Finally, a real-world airport traffic flow dataset is applied to validate the effectiveness of the proposed model, and the experimental results demonstrate that the ATFPNet outperforms other baselines on different prediction horizons. Specifically, the proposed method achieves up to 17% MAE improvement compared to baselines. Based on the proposed approach, efficient traffic planning is expected to be achieved for airport management.

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