Graph Neural Networks for Traffic Forecasting

Graph Neural Network for Traffic Forecasting: A Survey - arXiv

This paper is the first comprehensive survey that explores the application of graph neural networks for traffic forecasting problems.

[2104.13096] Graph Neural Networks for Traffic Forecasting - arXiv

GNNs are a class of deep learning methods that directly process the input as graph data. This leverages more directly the spatial dependencies ...

Graph neural network for traffic forecasting: A survey - ScienceDirect

This paper is the first comprehensive survey that explores the application of graph neural networks for traffic forecasting problems.

Graph Neural Network for Traffic Forecasting: The Research Progress

This survey aims to introduce the research progress on graph neural networks for traffic forecasting and the research trends observed from the most recent ...

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 ...

Graph neural network for traffic forecasting: : A survey

This paper is the first comprehensive survey that explores the application of graph neural networks for traffic forecasting problems.

Traffic forecasting using graph neural networks and LSTM - Keras

In this example, we implement a neural network architecture which can process timeseries data over a graph. We first show how to process the ...

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 ...

Revisitng graph neural networks for traffic forecasting - OpenReview

Accurate traffic forecasting is crucial for a wide range of traffic management applications. In recent years, Graph Neural Networks (GNNs) ...

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 ...

Traffic Forecasting: The Power of Graph Convolutional Networks on ...

The researchers modeled the traffic network as a graph, with each road segment being a node and the connections between segments being the edges ...

Graph Neural Networks for Traffic Pattern Recognition: An Overview

In [10], the authors summarized the traffic flow prediction models based on graph neural network (GNN). However, there is no existing review that investigates ...

(PDF) Graph Neural Networks for Traffic Forecasting - ResearchGate

Graph neural network (GNN), as one of the most popular methods, capable of processing non-Euclidean graph-structured data, which makes it ...

Graph Neural Network predicts traffic - Towards Data Science

Graph neural network (GNN) is a novel deep-learning method for traffic speed/time prediction. We will build a GNN model to predict traffic speed in this ...

Graph Neural Networks for Traffic Forecasting

GNNs are a class of deep learning methods that directly process the input as graph data. This leverages more directly the spatial dependencies ...

Graph neural network in traffic forecasting: a review

Especially, graph neural networks (GNNs) are being applied in traffic forecasting in recent years. In this paper, I give a review of the related ...

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

An eXplainable AI system to elucidate short-term speed forecasts in traffic networks obtained by Spatio-Temporal Graph Neural Networks.

Spatial-temporal graph neural network for traffic forecasting

This study aims to provide an overview of ST-GNN models in terms of mathematical methods and main components for the traffic forecasting problem.

Graph Neural Networks for Traffic Forecasting - Semantic Scholar

This work addresses the different ways of modelling traffic forecasting as a (temporal) graph, the different approaches developed so far to combine the ...

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.