… condition is not restricted by the road network and we cannot predict the state of traffic data
… traffic forecasting method called the temporal graph convolutional network (T-GCN) for traffic …

… dependency of road traffic, we adopt graph convolution to … matrix that better reflects innate
traffic proximity. In prior scholarship [… for traffic forecasting: the Hybrid Spatio-Temporal Graph …

… the sequences of hypergraph and graph, in which a Dynamic … on the two graphs with
merged spatial-temporal features, our … by the extensive traffic prediction experiments in the …

… -temporal graph network (MVSTGN), which combines attention and convolution mechanisms
into traffic … capture spatial-temporal characteristics for cellular traffic prediction, and the …

… traffic information. In addition, we construct a graph convolution cycle module that captures
local temporal … graph module to jointly capture the spatial-temporal dependence of the …

… spatial and temporal dependencies. In this paper, we propose a novel … temporal graph
neural network for traffic flow prediction, which can comprehensively capture spatial and temporal …

… prediction model. In this paper, we propose a spatial-temporal graph attention based dynamic
graph … GAGCN), which is employed to predict the road network traffic flow based on spatial-…

… Graph Transformer (MGT) to solve traffic prediction problems. MGT makes full use of attention
mechanisms in both temporal … (hence the name Meta Graph Transformer) to incorporate …

… , traffic forecasting is performed to predict future traffic states … dynamic temporal variation
features from the traffic data, that … model to learn the temporal variation trends of the traffic state. …

… model both the dynamics of traffic flows and their correlations. … -temporal graph-based
convolutional layer that is able to jointly extract both spatio and temporal information from the traffic …