A survey on graph neural networks for time series: Forecasting, classification, imputation, and anomaly detection
Time series are the primary data type used to record dynamic system measurements and
generated in great volume by both physical sensors and online processes (virtual sensors) …
generated in great volume by both physical sensors and online processes (virtual sensors) …
Deep neural networks for spatial-temporal cyber-physical systems: A survey
Cyber-physical systems (CPS) refer to systems that integrate communication, control, and
computational elements into physical processes to facilitate the control of physical systems …
computational elements into physical processes to facilitate the control of physical systems …
A flight arrival time prediction method based on cluster clustering-based modular with deep neural network
W Deng, K Li, H Zhao - IEEE Transactions on Intelligent …, 2023 - ieeexplore.ieee.org
With the rapid development of the air transportation industry, air traffic is facing a severe test.
The accurate prediction of the estimated arrival time (EAT) plays an important role in rational …
The accurate prediction of the estimated arrival time (EAT) plays an important role in rational …
Deep generative model for periodic graphs
Periodic graphs are graphs consisting of repetitive local structures, such as crystal nets and
polygon mesh. Their generative modeling has great potential in real-world applications such …
polygon mesh. Their generative modeling has great potential in real-world applications such …
A spatial-temporal approach for multi-airport traffic flow prediction through causality graphs
Accurate airport traffic flow estimation is crucial for the secure and orderly operation of the
aviation system. Recent advances in machine learning have achieved promising prediction …
aviation system. Recent advances in machine learning have achieved promising prediction …
Temporal attention aware dual-graph convolution network for air traffic flow prediction
Air traffic flow prediction is vital for its supporting function for collaborative decision making in
Air Traffic Management. However, due to the inherent spatial and temporal dependencies of …
Air Traffic Management. However, due to the inherent spatial and temporal dependencies of …
Spatiotemporal propagation learning for network-wide flight delay prediction
Accurate and interpretable delay predictions are vital for decision-making in the aviation
industry. However, effectively incorporating spatiotemporal dependencies and external …
industry. However, effectively incorporating spatiotemporal dependencies and external …
Machine learning augmented approaches for hub location problems
Hub location problems are widely analyzed in fields of logistic and transportation industry for
cost reduction. In this paper, a novel algorithm framework based on machine learning is …
cost reduction. In this paper, a novel algorithm framework based on machine learning is …
[HTML][HTML] A geographical and operational deep graph convolutional approach for flight delay prediction
CAI Kaiquan, LI Yue, ZHU Yongwen, F Quan… - Chinese Journal of …, 2023 - Elsevier
Flight delay prediction has attracted great interest in civil aviation community due to its
significant role in airline planning, flight scheduling, airport operation, and passenger …
significant role in airline planning, flight scheduling, airport operation, and passenger …
A graph multi-attention network for predicting airport delays
Predicting airport delays is of great importance for aviation operations, from the development
of effective air traffic management strategies to the reallocation of airline resources. In this …
of effective air traffic management strategies to the reallocation of airline resources. In this …