Departure delay prediction and analysis based on node sequence data of ground support services for transit flights

• Published in: Transportation research. Part C, Emerging technologies Emerging technologies, 2023-08, Vol.153, p.104217, Article 104217
• Main Authors: Yang, Zhao, Chen, Yixin, Hu, Jinbiao, Song, Yilu, Mao, Yi
• Format: Article
• Language: English
• Subjects: Bayesian network; Delay prediction; Departure delay; Machine learning; Neural network; Transit flight
• ISSN: 0968-090X; 1879-2359
• DOI: 10.1016/j.trc.2023.104217

•The departure delay is predicted based on node sequence data of ground support services for transit flights.•The cases of Shanghai Pudong and Kunming Changshui airports are applied for model illustration.•A Bayesian network is constructed for interrelationship analysis between segments.•A GCN-GraphSAGE model is proposed for departure delay prediction.•The proposed GCN-GraphSAGE method is reported to outperform the other benchmark methods. Delays frequently occur during ground support services for transit flights. Due to the limitation of related data acquisition, the delay occurred during this process has seldom been explored. Toward the purpose of departure delay mitigation, the present research provides the node sequence data to identify the critical ground support service nodes that associate with departure delay. Machine learning techniques are used for accurate departure delay prediction of transit flights. First, the topological structure of the neural network is constructed according to the ground service support procedure of transit flights. The flight data from Shanghai Pudong and Kunming Changshui International airports are collected for delay description and model evaluation. Then, similarity of delay scenarios are illustrated and the typical delay patterns are explored based on clustering methods. It is found that there are some node sections that delays routinely occur. Third, a Bayesian network is constructed for delay probability analysis. The conditional probability table describing the impact of delay occurred during each section on the departure delay for transit flights is provided. Finally, the performance of two classes of approaches to predict departure delays are constructed and compared, including three traditional machining learning methods denoted as random forest, decision tree, XGBoost models, and four graph convolutional neural network models namely Graph convolutional network (GCN), Graph attention network (GAT), Graph sample and aggregate (GraphSAGE) and integrated Graph convolutional network and Graph sample and aggregate (GCN-GraphSAGE) models. In general, the proposed GCN-GraphSAGE method achieves the most stable and satisfactory performance for departure delay prediction. The research results can support the dynamic and accurate prediction of departure delays and the refined management of transit flights for delay alleviation.