Short|Term Traffic Flow Prediction Based on Graph Convolutional ...

Short-Term Traffic Flow Prediction Based on Graph Convolutional ...

This study proposes a short-term traffic flow prediction model that combines community detection-based federated learning with a graph convolutional network ( ...

Short-Term Traffic Flow Prediction Based on Graph Convolutional ...

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS. 1. Short-Term Traffic Flow Prediction Based on Graph. Convolutional Networks and Federated Learning.

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.

Short-Term Traffic Flow Prediction Based on Graph Convolutional ...

Abstract. This study proposes a short-term traffic flow prediction model that combines community detection-based federated learning with a graph ...

Short-Term Traffic Flow Prediction Based on Graph Convolutional ...

GCN-LSTM will simultaneously capture the spatial and temporal characteristic of traffic flow by embedding GCN into the structure of LSTM.

Spatial–temporal short-term traffic flow prediction model based on ...

The graph convolution network (GCN) is widely used in traffic prediction models to efficiently handle the graphical structural data of road networks. However, ...

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. Since ...

A spatial-temporal short-term traffic flow prediction model based on ...

Graph convolution network (GCN) is widely used in traffic prediction models to better deal with the graphical structure data of road networks.

Short-term multi-step-ahead sector-based traffic flow prediction ...

To address this challenge, the attention-enhanced graph convolutional long short-term memory network (AGC-LSTM) model is applied to improve the ...

Traffic Flow Prediction Using Graph Convolution Neural Networks

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

Spatial-temporal graph convolution network model with traffic ...

Accurate and fine-grained traffic state prediction has always been an important research field. For long-term traffic flow prediction, ...

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.

Traffic Graph Convolutional Recurrent Neural Network - arXiv

Song, “Short-term traffic flow prediction in smart multimedia system for. Internet of Vehicles based on deep belief network,”. Futur. Gener. Comput. Syst ...

Road traffic flow prediction based on dynamic spatiotemporal graph ...

Then, the extracted features were input into the Long Short-Term Memory (LSTM) unit to learn the intra-day time evolution process of traffic ...

Traffic Flow Prediction Research Based on an Interactive Dynamic ...

Accurate traffic flow prediction requires a prediction model that can adequately capture long-term dependencies and dynamic spatial–temporal correlations. First ...

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 ...

lehaifeng/T-GCN: Temporal Graph Convolutional Network ... - GitHub

Temporal Graph Convolutional Network for Urban Traffic Flow Prediction ... Short Term Transformer-based Spatiotemporal Neural Network for Traffic Flow Forecasting.

Attention‐Based Gated Recurrent Graph Convolutional Network for ...

The prediction performance of most existing short-term traffic flow prediction methods deteriorates rapidly for longer time steps. In addition, ...

Short-time Traffic Flow Prediction Based on High-order Graph ...

These methods can accurately predict traffic flow by effectively capturing long-term dependencies and temporal evolution features in sequence data. Researchers ...

Traffic-Prediction-Open-Code-Summary/README.md at master

Deep learning models for traffic prediction ; Conv-GCN, Multi-Graph Convolutional Network for Short-Term Passenger Flow Forecasting in Urban Rail Transit ; TCC- ...