Traffic Flow Prediction Using Graph Convolution Neural Networks

Prediction of Large Scale Spatio-temporal Traffic Flow Data with ...

Yu et al. [19] proposed a spatiotemporal graph convolutional network (STGCN) for traffic flow velocity prediction on a multi-scale traffic network. The ST ...

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 ...

Traffic Flow Prediction Based on Interactive Dynamic Spatio ...

To examine the spatial-temporal properties between traffic flow nodes, the Structure Learning Convolutional Neural Network (SLCNN) (3) adapts dynamic graph ...

Traffic forecasting using graph neural networks and LSTM - Keras

Graph convolution layer · The nodes' representations are computed in self.compute_nodes_representation() by multiplying the input features by ...

[R] Unified Spatio-Temporal Modeling for Traffic Forecasting ... - Reddit

[R] Unified Spatio-Temporal Modeling for Traffic Forecasting using Graph Neural Network ... Abstract: Research in deep learning models to forecast ...

Spatio-Temporal Pivotal Graph Neural Networks for Traffic Flow ...

Traffic flow forecasting is a classical spatio-temporal data mining problem with many real-world applications. Recently, various methods based on Graph ...

Multi-Head Attention Spatial-Temporal Graph Neural Networks for ...

Wu et al. [13] proposed an adaptive learning graph adjacency matrix for traffic prediction, Bai et al. [14] proposed an adaptive graph convolutional network, ...

Graph Neural Networks and Open-Government Data to Forecast ...

This work uses two types of widely used and open-source GNN algorithms to forecast traffic flow, namely Temporal Graph Convolutional Network (TGCN) and ...

Artificial intelligence-based traffic flow prediction: a comprehensive ...

Thus, deep learning emerged, employing several layers to extract more complex properties from raw input. Convolutional Neural Networks (CNN) [17] ...

Graph Neural Networks for Traffic Pattern Recognition: An Overview

monitoring the transportation network. Flow prediction can also support the development of fully and partially autonomous vehicles. With the increasing amount ...

Spatial-temporal Graph Attention Networks for Traffic Flow Forecasting

learnt the traffic network as images and proposed a model based on convolutional neural network to predict network-wide traffic speed with higher accuracy ...

Traffic Flow Prediction Model Based on the Combination ... - Frontiers

Wu et al. (2018) proposed a DNN-based traffic flow prediction model (DNN-BTF) to improve the prediction accuracy, using convolutional neural networks to mine ...

【Review01】Diffusion Convolutional Recurrent Neural Network

Comments19 · Friendly Introduction to Temporal Graph Neural Networks (and some Traffic Forecasting) · Science Use Case 2 -Traffic Forecasting ǀ ...

A Graph Convolutional Network-Based Model for Traffic Flow ...

A Graph Convolutional Network-Based Model for Traffic Flow Prediction Using Multimodal Spatial and Temporal Data ; Publication: Journal of Highway and ...

Spatial-temporal hypergraph convolutional network for traffic ... - PeerJ

Accurate traffic forecasting plays a critical role in the construction of intelligent transportation systems. However, due to the across ...

Hierarchical Graph Convolution Network for Traffic Forecasting

Recently, the lat- est Graph Convolution Network (GCN) has been introduced to learn spatial features while the time neural networks are used to learn temporal ...

Dynamic Graph Convolutional Network for Long-Term Traffic Flow ...

With the development of deep learning technology, exploiting deep neural networks models for the task of traffic flow prediction has become ...

Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic ...

In recent years, deep learning methods have become a popu- lar choice for traffic flow prediction from high-dimensional. Page 2. DSTAGNN: Dynamic Spatial- ...

Traffic Forecasting with Pytorch Geometric Temporal - YouTube

Comments51 · Fraud Detection with Graph Neural Networks · Friendly Introduction to Temporal Graph Neural Networks (and some Traffic Forecasting).

Graph learning-based spatial-temporal graph convolutional neural ...

Graph Convolutional Neural Network (GCN) has been effectively used for traffic forecasting due to its excellent performance in modelling spatial ...