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Jul 30, 2023 · The proposed traffic flow prediction framework based on clustering and heterogeneous graph neural network consists of three modules: clustering ...
Aug 10, 2023 · A Traffic Flow Prediction Framework Based on Clustering and Heterogeneous Graph Neural Networks. Authors: Author Picture Lei Luo. Shandong ...
Jan 22, 2024 · Therefore, this paper proposes a novel spatial-temporal model based on an attention one-dimension convolutional neural network (1D-CNN) and a ...
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What is a neural network based traffic flow prediction?
What is the traffic flow model?
A short-term traffic flow prediction model based on an improved gate recurrent unit neural network ... Spatial-temporal fusion graph neural networks for traffic ...
ClusterST: Clustering ... A Dynamic Heterogeneous Graph Convolution Network For Traffic Flow Prediction[J]. ... Traffic Flow Prediction Model Based on Graph Neural ...
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Qi et al. [37] proposed an asynchronous graph convolutional networks (FedAGCN) based on joint learning, starting from the accuracy and time cost of traffic ...
Jun 11, 2021 · To tackle this problem, we propose a Streaming Traffic Flow Forecasting Framework, TrafficStream, based on Graph Neural Networks (GNNs) and ...
Missing: Clustering Heterogeneous
Our model leverages a spatial module that employs graph convolutional network representation learning alongside transfer entropy to capture causal relationships ...
Apr 7, 2024 · In [30], the authors proposed the so-called Phase Space Graph Convolutional Network (PSGCN), an innovative framework based on a graph ...
TrafficStream: A Streaming Traffic Flow Forecasting Framework Based on Graph Neural Networks and Continual Learning (IJCAI, 2021) [paper]; DSTAGNN: Dynamic ...
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