[2104.13096] Graph Neural Networks for Traffic Forecasting

[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 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 ...

(PDF) Graph Neural Networks for Traffic Forecasting - ResearchGate

In this work, we focus on the challenge of traffic forecasting and review the recent development and application of graph neural networks (GNN) to this problem.

Graph Neural Networks for Traffic Forecasting

In this work, we focus on the challenge of traffic forecasting and review the recent development and application of graph neural networks (GNN) to this problem.

Graph Neural Network for Traffic Forecasting: A Survey - arXiv

In recent years, to model the graph structures in transportation systems as well as contextual information, graph neural networks have been ...

Graph Neural Networks for Intelligent Transportation Systems

These applications cover a wide range, including but not limited to traffic forecasting, travel demand prediction, autonomous vehicles, and ...

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 ...

(PDF) Graph Neural Network for Traffic Forecasting: The Research ...

Recently, graph neural networks (GNNs) have emerged as state-of-the-art traffic forecasting solutions because they are well suited for traffic systems with ...

Graph Neural Networks for Intelligent Transportation Systems

Unlike previous surveys, which have been limited to traffic forecasting problems, we explore how GNN frameworks have evolved for different ITS applications, ...

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

Jiang W, Luo J, He M, Gu W. Graph Neural Network for Traffic Forecasting: The Research Progress[J]. ISPRS International Journal of Geo-Information, 2023. Link.

Traffic forecasting using graph neural networks and LSTM - Keras

This example shows how to forecast traffic condition using graph neural networks and LSTM. Specifically, we are interested in predicting the future values of ...

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 ...