Mar 7, 2021 · In this paper, we propose a Multi-Stage Attention Spatial-Temporal Graph Networks (MASTGN). First, an internal attention mechanism is designed ...
A novel spatial-temporal model based on an attention one-dimension convolutional neural network (1D-CNN) and a gated interpretable framework, which models ...
Apr 26, 2024 · Traffic flow prediction is a typical spatial–temporal data prediction problem. How to capture the spatial–temporal correlation from traffic data ...
Missing: stage | Show results with:stage
Jun 18, 2024 · Recent studies have shown that spatial-temporal graph neural networks exhibit great potential applied to traffic prediction, which combines ...
Yin et al. [35] proposed a multi-stage attention spatiotemporal graph network, designed to model the complex nonlinear interactions between traffic flow and ...
People also ask
What is LSTM for network traffic prediction?
What is spatial and temporal attention?
What is the network traffic prediction method?
In this paper, we focus on the spatio-temporal factors, and propose a graph multi-attention network (GMAN) to predict traffic conditions for time steps.
Missing: stage | Show results with:stage
Feb 22, 2024 · Abstract: Multi-step traffic speed prediction is a challenging issue due to the multiple spatial-temporal dependencies among roads.
This paper proposes an adaptive spatial-temporal graph neural network model based on the multi-head attention mechanism for traffic flow prediction.
In this paper, we propose a novel attention based spatial-temporal graph con- volutional network (ASTGCN) model to solve traffic flow forecasting problem.
Missing: stage | Show results with:stage
This paper presents a traffic forecasting model which combines a graph convolutional network, a gated recurrent unit, and a multi-head attention mechanism
Missing: stage | Show results with:stage