Temporal Graph Neural Networks for Traffic Prediction

Temporal Graph Neural Networks for Traffic Prediction - arXiv

We build a study called STG4Traffic using the deep learning framework PyTorch to establish a standardized and scalable benchmark on two types of traffic ...

STG4Traffic: A Survey and Benchmark of Spatial-Temporal Graph ...

Recent studies have shown that spatial-temporal graph neural networks exhibit great potential applied to traffic prediction, which combines ...

STNN: A Spatial-Temporal Graph Neural Network for Traffic Prediction

In this paper, we propose a novel spatial-temporal graph neural network (STNN), an end-to-end solution for traffic prediction that simultaneously captures ...

Spatial–temporal graph neural network traffic prediction based load ...

Introduction · Spatial–temporal-events cross attention graph convolution neural network (STECA-GCN) is derived to predict traffic flow accurately. · Base ...

lehaifeng/T-GCN: Temporal Graph Convolutional Network ... - GitHub

Accurate and real-time traffic forecasting plays an important role in the Intelligent Traffic System and is of great significance for urban traffic planning, ...

Spatio-temporal graph neural networks for missing data completion ...

Therefore, we propose an end-to-end traffic model dealing with missing data - missing traffic data completion graph neural networks (MTC-GNN). Experiments ...

T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction

To capture the spatial and tem- poral dependences simultaneously, we propose a novel neural network-based traffic forecasting method, the temporal graph.

Spatio-Temporal Pivotal Graph Neural Networks for Traffic Flow ...

Traffic flow forecasting is a classical spatio-temporal data mining problem with many real-world applications. Recently, various methods based ...

RiccardoSpolaor/Verbal-Explanations-of-Spatio-Temporal-Graph ...

Key information justifying these predictions is extracted from the input traffic network in the form of a significant subgraph. The information of the subgraph ...

a spatial–temporal graph neural network with adaptive fusion features

We propose a knowledge-actuated model for traffic flow prediction, where to utilize external knowledge to update traffic information, and we ...

Integrating Spatio-Temporal Graph Convolutional Networks with ...

In brief, we propose a new hybrid approach by integrating the spatio-temporal graph neural network (STGCN) and CNN for short-term traffic speed prediction.

Multi-Head Attention Spatial-Temporal Graph Neural Networks for ...

To improve the prediction accuracy, a multi-head attention spatial-temporal graph neural network (MSTNet) is proposed in this paper. First, the traffic data is ...

Adaptive Spatio-temporal Graph Neural Network for traffic forecasting

In this paper, we propose an Adaptive Spatio-Temporal graph neural Network, namely Ada-STNet, to first derive optimal graph structure with the guidance of node ...

Traffic Flow Prediction via Spatial Temporal Graph Neural Network

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

SimST: A GNN-Free Spatio-Temporal Learning Framework for Traffic...

Traffic forecasting is a crucial and challenging problem in smart city efforts. Spatio-Temporal Graph Neural Networks (STGNNs) have ...

Channel attention-based spatial-temporal graph neural networks for ...

The traffic prediction task is generally used to predict the future traffic flow according to the collected traffic flow through N sensor sites ...

Graph Neural Network for Traffic Forecasting: The Research Progress

For traffic forecasting problems in graph format, traffic data are aggregated by specific locations or stations, which are regarded as nodes in a traffic graph.

[PDF] Adaptive Hybrid Spatial-Temporal Graph Neural Network for ...

Cellular traffic prediction is an indispensable part for intelligent telecommunication networks ... Adaptive Hybrid Spatial-Temporal Graph Neural Network for ...

Multi-View Spatial–Temporal Graph Neural Network for Traffic ...

Spatial–temporal graph neural network, as a effective deep learning tool to deal with dynamic graph data, has gained more and more attention and ...

Predicting Los Angeles Traffic with Graph Neural Networks - Medium

As a result, graph neural networks (GNNs) are being developed and experimented with for the purpose of traffic forecasting. This post explores ...