A Multi|Adaptive Graph Convolutional Network for Traffic Forecasting

A Multi-Adaptive Graph Convolutional Network for Traffic Forecasting

MAGCN: A Multi-Adaptive Graph Convolutional Network for Traffic Forecasting. Abstract: Traffic forecasting is one of the most fundamental components in many ...

Adaptive Graph Convolutional Recurrent Network for Traffic ...

We evaluate AGCRN on two real-world datasets for the multi-step traffic prediction task and com- pare it with several representative traffic forecasting models.

Adaptive Graph Convolutional Recurrent Network for Traffic ... - arXiv

In this paper, we argue that learning node-specific patterns is essential for traffic forecasting while the pre-defined graph is avoidable. To ...

Multi-graph convolutional network for spatiotemporal traffic forecasting

In this paper, we identify two essential spatial dependencies in traffic forecasting in addition to distance, direction and positional relationship.

Adaptive graph convolutional recurrent network for traffic forecasting

We further propose an Adaptive Graph Convolutional Recurrent Network (AGCRN) to capture fine-grained spatial and temporal correlations in traffic series ...

Adaptive Graph Convolution Networks for Traffic Flow Forecasting

The AGC-net is constructed by the Adaptive Graph Convolution (AGC) based on a novel context attention mechanism, which consists of a set of ...

Multi-Range Attentive Bicomponent Graph Convolutional Network ...

In this paper, we propose the Multi-Range Attentive Bicomponent GCN (MRA-BGCN), a novel deep learning model for traffic forecasting.

Multi-View Spatial-Temporal Adaptive Graph Convolutional ...

In this paper, we propose a novel Multi-View Spatial-Temporal Adaptive Graph Convolutional Network (MVST-AGCN) for traffic flow prediction.

LeiBAI/AGCRN: Adaptive Graph Convolutional Recurrent ... - GitHub

This folder concludes the code and data of our AGCRN model: Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting, which has been accepted to ...

[PDF] Adaptive Graph Convolutional Recurrent Network for Traffic ...

It is argued that learning node-specific patterns is essential for traffic forecasting while the pre-defined graph is avoidable, and two adaptive modules ...

PMGCN: Progressive Multi-Graph Convolutional Network for Traffic ...

Traffic forecasting has always been an important part of intelligent transportation systems. At present, spatiotemporal graph neural networks are widely ...

Time-adaptive graph convolutional network for traffic prediction

The TAGCN applies graph convolution with a time-adaptive graph adjacency matrix to capture the dynamic spatial relationship in the traffic data, and designs a ...

ST-DAGCN: A Spatiotemporal Dual Adaptive Graph Convolutional ...

Emergence of artificial intelligence has led to the surge of deep learning methods for short-term traffic forecast. Notably, Graph Convolutional Neural Networks ...

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

... Graph Convolutional Network for Traffic Forecasting. While considering the ... the adaptive locality ability of GNNs by leveraging the structural properties of ...

Multi-View Spatial-Temporal Adaptive Graph Convolutional ...

Request PDF | On Nov 26, 2021, Zhirong Duan and others published Multi-View Spatial-Temporal Adaptive Graph Convolutional Networks for Traffic Forecasting ...

ADSTGCN: A Dynamic Adaptive Deeper Spatio-Temporal Graph ...

Multi-step traffic forecasting has always been extremely challenging due to constantly changing traffic conditions. Advanced Graph Convolutional Networks ...

Adaptive Graph Convolutional Recurrent Network for Traffic ...

Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting. LEI BAI, Lina Yao, Can Li, Xianzhi Wang, Can Wang. Keywords: Abstract Paper Similar ...

[PDF] Adaptive Multi-receptive Field Spatial-Temporal Graph ...

Adaptive Multi-receptive Field Spatial-Temporal Graph Convolutional Network for Traffic Forecasting · Figures and Tables · Topics · 8 Citations · 18 References ...

Spatio‐temporal adaptive graph convolutional networks for traffic ...

Accurate forecasting of traffic flow is crucial for intelligent traffic control and guidance. It is very challenging to forecast the traffic ...

TEA-GCN: Transformer-Enhanced Adaptive Graph Convolutional ...

Benefiting from the powerful structure capture ability of Graph Neural Network (GNNs) [7,8,9,10], a series of GNN-based traffic flow forecasting ...