Airport flight ground service time prediction with missing data using graph convolutional neural network imputation and bidirectional sliding mechanism

Accurate flight ground service time prediction plays a key role in the efficient operation of airports, which can help managers make better resource scheduling optimization decisions, reduce flight delays and improve airport operation capabilities. However, since flight ground service data is typically acquired by sensor equipment or manually recorded, data missing and abnormalities caused by sensor failure or manual recording errors are an unavoidable occurrence, making the prediction problem challenging. Although missing values can be imputed, the traditional methods usually fill in missing values and then make predictions separately, which leads to a two-step process and increases the prediction error. Besides, the existing one-step methods still suffer some limitations and shortcomings: failure to integrate bidirectional spatial–temporal dependencies leads to unsatisfying model prediction accuracy and poor robustness under high data missing rates, which cannot be directly used in the study of flight ground service time prediction with missing values. To solve this problem, we presented a sliding bidirectional graph convention network model framework that capable of dealing predicting with missing values. First, we proposed a new adjacency matrix construction method to capture more comprehensive spatial–temporal dependencies and developed a graph convention network based missing value imputation unity (GCNM) to inference and filling missing features. Then, multiple GCNM units are stacked to develop a bidirectional module BDGCNM to realize the filling and prediction into one step. Finally, we introduced a variable sliding window mechanism to improve the model’s robustness. Extensive experiments on real airport data sets and PEMS traffic data sets show that our method is more accurate and robust than the current state-of-the-art.