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) …
Graph neural network for traffic forecasting: The research progress
Traffic forecasting has been regarded as the basis for many intelligent transportation system
(ITS) applications, including but not limited to trip planning, road traffic control, and vehicle …
(ITS) applications, including but not limited to trip planning, road traffic control, and vehicle …
Towards graph foundation models: A survey and beyond
Emerging as fundamental building blocks for diverse artificial intelligence applications,
foundation models have achieved notable success across natural language processing and …
foundation models have achieved notable success across natural language processing and …
On region-level travel demand forecasting using multi-task adaptive graph attention network
Accurate travel demand forecasting at the regional level benefits to urban traffic
management and service operations. Irregular regions can be naturally represented by …
management and service operations. Irregular regions can be naturally represented by …
Uncertainty quantification of spatiotemporal travel demand with probabilistic graph neural networks
Recent studies have significantly improved the prediction accuracy of travel demand using
graph neural networks. However, these studies largely ignored uncertainty that inevitably …
graph neural networks. However, these studies largely ignored uncertainty that inevitably …
Deep trip generation with graph neural networks for bike sharing system expansion
Bike sharing is emerging globally as an active, convenient, and sustainable mode of
transportation. To plan successful bike-sharing systems (BSSs), many cities start from a …
transportation. To plan successful bike-sharing systems (BSSs), many cities start from a …
Spatiotemporal graph neural networks with uncertainty quantification for traffic incident risk prediction
Predicting traffic incident risks at granular spatiotemporal levels is challenging. The datasets
predominantly feature zero values, indicating no incidents, with sporadic high-risk values for …
predominantly feature zero values, indicating no incidents, with sporadic high-risk values for …
Graph neural networks for road safety modeling: datasets and evaluations for accident analysis
We consider the problem of traffic accident analysis on a road network based on road
network connections and traffic volume. Previous works have designed various deep …
network connections and traffic volume. Previous works have designed various deep …
Privacy-preserving individual-level covid-19 infection prediction via federated graph learning
Accurately predicting individual-level infection state is of great value since its essential role
in reducing the damage of the epidemic. However, there exists an inescapable risk of …
in reducing the damage of the epidemic. However, there exists an inescapable risk 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 …