Graph Neural Network for Traffic Forecasting

odinhg/Graph-Neural-Networks-INF367A: Traffic prediction ... - GitHub

Traffic prediction with graph neural network using PyTorch Geometric. The implementation uses the MetaLayer class to build the GNN which allows for separate ...

Advanced Graph Neural Networks: urban traffic forecasting

In the realm of urban traffic forecasting, advanced Graph Neural Networks (GNNs) incorporating attention mechanisms have emerged as a cutting-edge approach.

(PDF) Graph Neural Network for Traffic Forecasting: A Survey

Convolution neural networks and recurrent neural networks are two popular deep learning models used to describe spatial and temporal ...

Traffic prediction with advanced Graph Neural Networks

Researchers at DeepMind have partnered with the Google Maps team to improve the accuracy of real time ETAs by up to 50% in places like Berlin, Jakarta, São ...

Friendly Introduction to Temporal Graph Neural Networks ... - YouTube

Comments37 ; Traffic Forecasting with Pytorch Geometric Temporal. DeepFindr · 23K views ; Understanding Graph Attention Networks. DeepFindr · 82K ...

Long Short-Term Traffic Prediction with Graph Convolutional Networks

Neural Network, which combines graph con- volution with recurrent neural networks in an encoder- decoder manner. • STGCN [Yu et al., 2018]: Spatial-Temporal ...

Graph Neural Network for Traffic Forecasting: The Research Progress

Traffic forecasting has been regarded as the basis for many intelligent transportation system (ITS) applications, including but not limited ...

Dynamic Graph Neural Network for Traffic Forecasting in Wide Area ...

We propose a nonautoregressive graph-based neural network for multistep network traffic forecasting. Specifically, we develop a dynamic variant of diffusion ...

Spatio-temporal envolutional graph neural network for traffic flow ...

For instance, STGCN is an exemplar model that significantly enhances traffic speed prediction by combining spatial and temporal information ...

Multi-Head Spatiotemporal Attention Graph Convolutional Network ...

In a bid to improve neural networks' capability to predict traffic forecasts accurately, attention models are often developed to create a vector ...

Disentangled traffic forecasting via efficient graph neural network

We capture spatial correlation using a GNN optimized by an attention mechanism. Finally, we merge useful information from volatile events into predictable ...

Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic ...

Abstract. As a typical problem in time series analysis, traf- fic flow prediction is one of the most important application fields of machine learning. How-.

Decoupled Dynamic Spatial-Temporal Graph Neural Network for ...

Traffic forecasting is a crucial service in Intelligent Transportation. Systems (ITS) to predict future traffic conditions (e.g., traffic flow1) based on ...

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 ...

Spatio-Temporal Graph Neural Network for Traffic Prediction Based ...

The prediction performance of many existing traffic prediction models is limited by the fixed original graph structure and inappropriate spatio- ...

Leveraging Graph Neural Network with LSTM For Traffic Speed ...

Abstract: Accurate traffic forecasting plays an important role in the smart city and is of great significance for urban traffic planning, management, ...

Graph Neural Networks and Open-Government Data to Forecast ...

In recent years, deep-learning models, such as graph neural networks (GNN), have shown great promise in traffic forecasting due to their ability to capture ...

Graph Neural Controlled Differential Equations for Traffic Forecasting

A prevalent approach in the field is to combine graph convolutional networks and recurrent neural networks for the spatio-temporal processing. There has been ...

[PDF] SST-GNN: Simplified Spatio-temporal Traffic forecasting ...

SST-GNN: Simplified Spatio-temporal Traffic forecasting model using Graph Neural Network · Amit Roy, Kashob Kumar Roy, +2 authors. A. Rahman · Published in ...

Graph neural network variants in traffic forecasting: A review

One field in ITS is traffic prediction, where machine learning learns from historical data to forecast future traffic conditions. Natively, the ...