Abstract: Predicting the future state of a transportation system is a challenging problem with very complex spatiotemporal dependencies. Our research ...

We have conducted extensive experiments on real- world datasets. The results show that our model performs the prediction task well and outperforms other graph ...

Overview of machine learning-based traffic flow prediction. Article · BLRGCN: A dynamic traffic flow prediction model based on spatiotemporal graph convolutional ...

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

Mar 23, 2024 · In this study, we aim to provide a comprehensive overview of the overall architecture of traffic forecasting, covering aspects such as traffic ...

The real-world traffic speed data, including the graph structure of the traffic network, used in this study is published via a publicly available website1 to ...

A novel Hierarchical Graph Convolution Network based on pooling is proposed for traffic forecasting considering both the road segments and regions feature ...

A generic and dynamic graph convolutional network named GDGCN is proposed. · It is the first to explore the parameter-sharing mechanism in traffic forecasting.

Aug 10, 2023 · To address the problem, we propose TransGTR, a transferable structure learning framework for traffic forecasting that jointly learns and.

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.