Traffic Flow Prediction Using Graph Convolution Neural Networks

In this article, we analyze the architecture of the graph convolution network for traffic flow prediction.

The architecture of the graph convolution network takes into account daily and weekly patterns of traffic flow distributions and shows that the considered ...

Traffic Graph Convolutional Recurrent Neural Network - arXiv

Spectral-based graph convolution has been adopted and combined with RNN [20] and CNN [1] to forecast traffic states. These models successfully apply convolution ...

Urban road traffic flow prediction: A graph convolutional network ...

Inspired by the successful application of Graph Convolutional Network (GCN) on non-Euclidean data, a graph convolutional network model framework embedded with ...

traffic flow prediction based on graph convolutional networks and ...

In this paper, the approach is to use GCSTN, based on graph neural networks, to predict short-term traffic flow in highway transportation.

MF-CNN: Traffic Flow Prediction Using Convolutional Neural ...

To tackle this problem, in this paper, we propose a CNN-based multi-feature predictive model (MF-CNN) that collectively predicts network-scale traffic flow with ...

Long Short-Term Traffic Prediction with Graph Convolutional Networks

Each node records some traffic features, such as traffic flow, vehicle speed, and road occupancy, etc. Problem Definition. For a traffic network, let xi t ∈ R.

Traffic Flow Prediction Using Graph Convolution Neural Networks

At present, the deep learning methods commonly used in spatiotemporal traffic flow prediction, such as the recurrent neural network (RNN) [29][30][31], the ...

Survey on traffic flow prediction methods based on graph ...

Graph convolution is a deep learning method for graph data, which can process unstructured traffic network data, which is very suitable for traffic flow ...

RL-GCN: Traffic flow prediction based on graph convolution and ...

The model learns spatiotemporal dependencies in urban traffic networks and predicts future traffic flows using LSTM models. The advantage of this model is that ...

Traffic Flow Forecasting of Graph Convolutional Network Based on ...

Aiming at the difficulty of capturing and modelling the temporal and spatial correlation and dynamic features of traffic flow, this paper ...

Temporal Graph Neural Networks for Traffic Prediction - arXiv

MTGNN [22] designed a structure that combines adaptive graph learning with dilation convolution to capture spatial-temporal correlation. GMAN [ ...

A Graph Convolutional Method for Traffic Flow Prediction in ...

So, many studies such as [34–36] use CNN models to obtain spatial feature of the highway network to get better prediction for traffic flow. In ...

A multi-modal attention neural network for traffic flow prediction by ...

Then, the CNN is used to capture the temporal correlations in the historical traffic flow data and combine it with the spatiotemporal ...

Dynamic graph convolution recurrent neural network for traffic flow ...

To forecast the condition of traffic networks in the future, it is crucial to model the spatial and temporal correlation of traffic series.

Traffic Flow Prediction Using Graph Convolution Neural Networks

Experiments show that the considered model outperforms other baseline forecasting algorithms. Keywords—traffic prediction, graph neural network, ...

Graph Neural Network for Traffic Forecasting: The Research Progress

A GCN is a pioneer in transferring the concept of convolution operations from Euclidean image data to non-Euclidean image data and has achieved great success in ...

jwwthu/GNN4Traffic: This is the repository for the collection ... - GitHub

Jiang W, Luo J. Graph Neural Network for Traffic Forecasting: A Survey[J]. Expert Systems with Applications, 2022. Link; Jiang W, Luo J. Big Data ...

A Novel Approach for Learning Traffic Flow Patterns Using Graph ...

The proposed neural network-based framework, known as Graph Convolutional Neural Network (GCNN), represents the transportation network and OD demand in an ...

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.