Multi-Task Local-Global Graph Network for Flight Delay Prediction
- Resource Type
- Conference
- Authors
- Wang, Tianyi; Chen, Shu-Ching
- Source
- 2022 IEEE 23rd International Conference on Information Reuse and Integration for Data Science (IRI) IRI Information Reuse and Integration for Data Science (IRI), 2022 IEEE 23rd International Conference on. :49-54 Aug, 2022
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineering Profession
Robotics and Control Systems
Industries
Correlation
Systematics
Atmospheric modeling
Predictive models
Multitasking
Airports
flight delay prediction
deep learning
graph convolutional network
multi-task learning
- Language
Airline on-time performance has always been a key factor in evaluating the punctuality of the civil aviation industry and has a profound impact on airlines, airports, and passengers. As a result, there have been increasing demands for the systematic analysis of flight delays and the development of accurate and efficient tools for flight delay prediction. In this paper, a deep learning framework based on graph convolutional networks and multi-task learning is proposed for flight delay prediction. We first use graph convolutional networks to capture the local and global spatial dependencies among the airports. A multi-decoder sequence-to-sequence model is developed to extract the temporal correlation from the data. We further apply a hierarchical graph fusion approach to combine features at different levels of the network to exploit their cross-modality correlations. The model is trained using a dynamic multi-task learning strategy to predict flight arrival and departure delays at the same time to boost the model's generalization and performance. The proposed model is evaluated on a large-scale public flight record dataset against several state-of-the-art methods. The experimental results demonstrate that our model can outperform all baseline methods in predicting short to medium-term flight delays.