Spatio-temporal graph neural networks for predictive learning in urban computing: A survey
With recent advances in sensing technologies, a myriad of spatio-temporal data has been
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …
Long-range transformers for dynamic spatiotemporal forecasting
Multivariate time series forecasting focuses on predicting future values based on historical
context. State-of-the-art sequence-to-sequence models rely on neural attention between …
context. State-of-the-art sequence-to-sequence models rely on neural attention between …
Advances in spatiotemporal graph neural network prediction research
Being a kind of non-Euclidean data, spatiotemporal graph data exists everywhere from traffic
flow, air quality index to crime case, etc. Unlike the raster data, the irregular and disordered …
flow, air quality index to crime case, etc. Unlike the raster data, the irregular and disordered …
Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learning
Graph learning plays a pivotal role and has gained significant attention in various
application scenarios, from social network analysis to recommendation systems, for its …
application scenarios, from social network analysis to recommendation systems, for its …
Multi-scale spatial-temporal aware transformer for traffic prediction
R Tian, C Wang, J Hu, Z Ma - Information Sciences, 2023 - Elsevier
Traffic prediction is an important part of smart city management. Accurate traffic prediction
can be deployed in urban applications such as congestion alerting and route planning, thus …
can be deployed in urban applications such as congestion alerting and route planning, thus …
Spatio-temporal graph convolutional networks via view fusion for trajectory data analytics
Trajectory data contains rich spatial and temporal information. Turning trajectories into
graphs and then analyzing them efficiently in an AI-empowered way is a representative …
graphs and then analyzing them efficiently in an AI-empowered way is a representative …
A trend graph attention network for traffic prediction
C Wang, R Tian, J Hu, Z Ma - Information Sciences, 2023 - Elsevier
Traffic prediction is an important part of urban computing. Accurate traffic prediction assists
the public in planning travel routes and relevant departments in traffic management, thus …
the public in planning travel routes and relevant departments in traffic management, thus …
GraphSAGE-based dynamic spatial–temporal graph convolutional network for traffic prediction
T Liu, A Jiang, J Zhou, M Li… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Traffic networks exhibit complex spatial-temporal dependencies, and accurately capturing
such dependencies is critical to improving prediction accuracy. Recently, many deep …
such dependencies is critical to improving prediction accuracy. Recently, many deep …
AdpSTGCN: Adaptive spatial–temporal graph convolutional network for traffic forecasting
Traffic flow forecasting plays a crucial role in applications such as intelligent transportation
systems. Despite significant research in this field, the current methods have limitations that …
systems. Despite significant research in this field, the current methods have limitations that …
Benchtemp: A general benchmark for evaluating temporal graph neural networks
To handle graphs in which features or connections are evolving over time, a series of
temporal graph neural networks (TGNNs) have been proposed. Despite the success of …
temporal graph neural networks (TGNNs) have been proposed. Despite the success of …