Short-Term Nationwide Airport Throughput Prediction With Graph Attention Recurrent Neural Network.

ABSTRACT: 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

SUBMITTER: Zhu X 

PROVIDER: S-EPMC9234322 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature