Zhu Xinting, Lin Yu, He Yuxin, Tsui Kwok-Leung, Chan Pak Wai, Li Lishuai
School of Data Science, City University of Hong Kong, Kowloon, Hong Kong SAR, China.
College of Urban Transportation and Logistics, Shenzhen Technology University, Shenzhen, China.
Front Artif Intell. 2022 Jun 13;5:884485. doi: 10.3389/frai.2022.884485. eCollection 2022.
With the dynamic air traffic demand and the constrained capacity resources, accurately predicting airport throughput is essential to ensure the efficiency and resilience of air traffic operations. Many research efforts have been made to predict traffic throughputs or flight delays at an airport or over a network. However, it is still a challenging problem due to the complex spatiotemporal dynamics of the highly interacted air transportation systems. To address this challenge, we propose a novel deep learning model, graph attention neural network stacking with a Long short-term memory unit (GAT-LSTM), to predict the short-term airport throughput over a national air traffic network. LSTM layers are included to extract the temporal correlations in the data, while the graph attention mechanism is used to capture the spatial dependencies. For the graph attention mechanism, two graph modeling methods, airport-based graph and OD-pair graph are explored in this study. We tested the proposed model using real-world air traffic data involving 65 major airports in China over 3 months in 2017 and compared its performance with other state-of-the-art models. Results showed that the temporal pattern was the dominate factor, compared to the spatial pattern, in predicting airport throughputs over an air traffic network. Among the prediction models that we compared, both the proposed model and LSTM performed well on prediction accuracy over the entire network. Better performance of the proposed model was observed when focusing on airports with larger throughputs. We also conducted an analysis on model interpretability. We found that spatiotemporal correlations in the data were learned and shown the model parameters, which helped us to gain insights into the topology and the dynamics of the air traffic network.
面对动态的空中交通需求和有限的容量资源，准确预测机场吞吐量对于确保空中交通运营的效率和弹性至关重要。已经开展了许多研究工作来预测机场或网络的交通吞吐量或航班延误。然而，由于高度交互的航空运输系统复杂的时空动态特性，这仍然是一个具有挑战性的问题。为应对这一挑战，我们提出了一种新颖的深度学习模型，即结合长短期记忆单元的图注意力神经网络（GAT-LSTM），用于预测国家空中交通网络的短期机场吞吐量。LSTM层用于提取数据中的时间相关性，而图注意力机制则用于捕捉空间依赖性。对于图注意力机制，本研究探索了两种图建模方法，即基于机场的图和OD对图。我们使用2017年3个月内涉及中国65个主要机场的真实空中交通数据对所提出的模型进行了测试，并将其性能与其他现有最先进模型进行了比较。结果表明，在预测空中交通网络的机场吞吐量时，与空间模式相比，时间模式是主导因素。在我们比较的预测模型中，所提出的模型和LSTM在整个网络的预测准确性方面都表现良好。当关注吞吐量较大的机场时，所提出的模型表现出更好的性能。我们还对模型的可解释性进行了分析。我们发现数据中的时空相关性在模型参数中得到了体现，这有助于我们深入了解空中交通网络的拓扑结构和动态特性。
