In this paper we propose a novel graph neural network based traffic speed forecasting model, the graph Long short term Memory (GLSTM) model.

In order to accurately predict the traffic speed, several studies on GNNs (Graph Neural Networks) [8, 9] and GCNNs (Graph CNNs) [10] have generated novel ...

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

A novel graph neural network based traffic speed forecasting model, the graph Long short term Memory (GLSTM) model which consists Graph neural network (GNN) ...

For capturing spatial and temporal dependencies simultaneously, in this paper we propose a novel graph neural network based traffic speed forecasting model, the ...

In this paper, we propose a spatio-temporal ensemble network named GT-LSTM, it can efficiently and effectively obtain the intrinsic patterns of traffic flow.

May 27, 2024 · 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.

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

In the following we demo how to forecast speeds on road segments through a graph convolution and LSTM hybrid model. ... Graph Convolutional Network for Traffic ...

A Two-Tower Spatial-Temporal Graph Neural Network for Traffic Speed Prediction ... Leveraging Graph Neural Network with LSTM For Traffic Speed Prediction[C] ...