… neural network-based traffic forecasting method, the temporal graph convolutional network
(… the above problems, we propose a new traffic forecasting method called the temporal graph …

… gated block in which a new graph attention network called cosAtt and graph convolution …
a new graph convolutional networks model LSGCN for long and short-term traffic prediction. In …

… spatial structure of the road network. In this paper, we introduce a graph network and propose
an optimized graph convolution recurrent neural network for traffic prediction, in which the …

… the micro graph of road networks. In this paper, we propose a novel … Graph Convolution
Networks (HGCN) for traffic forecasting by operating on both the micro and macro traffic graphs. …

… In this paper, we propose a novel deep learning framework STGCN for traffic prediction,
integrating graph convolution and gated temporal convolution through spatio-temporal con…

… To address the above challenges, in this article, we propose a novel traffic prediction
framework, named Dynamic Graph Convolutional Recurrent Network (DGCRN). In DGCRN, hyper-…

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

… detector data prediction), ignoring the segment-based traffic prediction tasks. In this study, …
spatiotemporal graph attention network (ASTGAT) for segment-level traffic speed prediction. In …

… In this study, an attention temporal graph convolutional network (A3T-GCN) was proposed
to … , traffic forecasting is performed to predict future traffic states according to historical traffic …

… dynamics in the traffic network. To deal … Network (DKFN) to forecast the network-wide traffic
state by modeling the self and neighbor dependencies as two streams, and their predictions …