TL;DR
A novel traffic forecasting framework, FusionGraphSAGE with Neural Networks (FGSNN), which combines a predefined static adjacency matrix based on node distances with a dynamic adaptive graph to better capture evolving traffic relationships and achieves collaborative spatiotemporal feature learning and refined traffic flow prediction.
TETRA is proposed, a hybrid spatio-temporal traffic forecasting model that integrates Graph Convolutional Networks (GCNs) with Extended Long Short-Term Memory (xLSTM) to capture complex multi-timescale temporal patterns, including congestion propagation and delayed recovery dynamics, which are not well represented by c...
A prediction model that incorporates multiple attention mechanisms with spatiotemporal graph convolutional networks (HASTGCN) and designs a spatiotemporal map convolution module to collaboratively model the dynamic spatiotemporal connection of traffic flow collaboratively model is used.
A Spatial-Temporal Graph Neural Network framework that can learn a combination of spatial and temporal relationships in road networks and changing time-varying patterns in traffic flow is used, which implies that adaptive spatial learning, together with the temporal sequence modeling, can produce much better forecastin...
An improved Transformer prediction model that integrates a graph convolutional network (GCN) and a self-attention mechanism is proposed for traffic flow prediction, combining temporal self-attention and learnable temporal encoding to capture both long-term traffic evolution patterns and sudden fluctuations.
The proposed MSG-LLM traffic flow forecasting framework significantly outperforms existing mainstream models in both short-term and long-term traffic flow forecasting tasks, validating the effectiveness and strong generalization ability of MSG-LLM in modeling complex traffic systems.
This framework introduces an adaptive graph learning module that dynamically infers meaningful connectivity relationships among traffic sensors—not relying on fixed geographic or distance-based assumptions—but instead leveraging real-time traffic correlations and node-level embeddings, enabling effective modeling of bo...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
