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 …
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 …
Fine-grained vessel traffic flow prediction with a spatio-temporal multigraph convolutional network
The accurate and robust prediction of vessel traffic flow is gaining importance in maritime
intelligent transportation system (ITS), such as vessel traffic services, maritime spatial …
intelligent transportation system (ITS), such as vessel traffic services, maritime spatial …
Spatio-Temporal Predictive Modeling Techniques for Different Domains: a Survey
Spatio-temporal prediction tasks play a crucial role in facilitating informed decision-making
through anticipatory insights. By accurately predicting future outcomes, the ability to …
through anticipatory insights. By accurately predicting future outcomes, the ability to …
Data-driven transfer learning framework for estimating on-ramp and off-ramp traffic flows
To develop the most appropriate control strategy and monitor, maintain, and evaluate the
traffic performance of the freeway weaving areas, state and local Departments of …
traffic performance of the freeway weaving areas, state and local Departments of …
STGSA: A novel spatial-temporal graph synchronous aggregation model for traffic prediction
The success of intelligent transportation systems relies heavily on accurate traffic prediction,
in which how to model the underlying spatial-temporal information from traffic data has come …
in which how to model the underlying spatial-temporal information from traffic data has come …
PI-STGnet: Physics-integrated spatiotemporal graph neural network with fundamental diagram learner for highway traffic flow prediction
At present, traffic state prediction primarily relies on purely data-driven methods, ignoring the
incorporation of physical constraints within the field of traffic flow. Taking this as a starting …
incorporation of physical constraints within the field of traffic flow. Taking this as a starting …
Towards integrated and fine-grained traffic forecasting: A spatio-temporal heterogeneous graph transformer approach
Fine-grained traffic forecasting is crucial for the management of urban transportation
systems. Road segments and intersection turns, as vital elements of road networks, exhibit …
systems. Road segments and intersection turns, as vital elements of road networks, exhibit …
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 …
ST-A-PGCL: Spatiotemporal adaptive periodical graph contrastive learning for traffic prediction under real scenarios
Y Qu, J Rong, Z Li, K Chen - Knowledge-Based Systems, 2023 - Elsevier
Exploring complicated dynamic spatiotemporal correlations has always been a challenging
issue in traffic prediction. Besides, methods that make predictions directly from data with …
issue in traffic prediction. Besides, methods that make predictions directly from data with …