Predicting Los Angeles Traffic with Graph Neural Networks

Predicting Los Angeles Traffic with Graph Neural Networks - Medium

This post explores the use of GNNs for traffic forecasting, and in particular explores the ST-GAT model developed by Zhang et al in “Spatial-Temporal Graph ...

The dataset was collected from the highway of Los Angeles County...

... Los Angeles County (METR-LA) from 1 March 2012 to 30 June 2012 ... Graph convolutional neural networks for traffic forecasting and prediction: A review.

Dynamic spatial aware graph transformer for spatiotemporal traffic ...

In our network, we design two modules, i.e. graph latent information learning module and traffic prediction network module. We update the ...

Temporal Graph Neural Networks for Traffic Prediction - arXiv

Recent studies have shown that spatial-temporal graph neural networks exhibit great potential applied to traffic prediction, which combines ...

Spatio-temporal envolutional graph neural network for traffic flow ...

For instance, STGCN is an exemplar model that significantly enhances traffic speed prediction by combining spatial and temporal information ...

A random graph diffusion attention network for traffic prediction

Traffic Prediction based on graph structures is a challenging task given that road networks are typically complex structures and the data to be analyzed ...

STG4Traffic：A Benchmark Study of Using Spatial-Temporal Graph ...

... Graph Neural Networks for Traffic Prediction”. - trainingl/STG4Traffic. ... METR-LA: Los Angeles Metropolitan Traffic Conditions Data, which records traffic ...

Transfer Learning with Graph Neural Networks for Short-Term ...

The resulting trained model can be used to forecast traffic on unseen networks. We demonstrate that TL-DCRNN can learn from San Francisco regional traffic data ...

Graph Neural Network for Traffic Forecasting: The Research Progress

GNNs utilize graph structures, which are common in transportation infrastructure, such as road networks and subway systems. GNNs can effectively capture ...

Graph neural network for traffic forecasting: A survey - ResearchGate

In recent years, to model the graph structures in transportation systems as well as contextual information, graph neural networks have been ...

Graph Neural Networks for Traffic Forecasting

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.

Transfer Learning with Graph Neural Networks for Short-Term ...

Moreover, we demonstrate that TL-DCRNN can learn from San Francisco region traffic data and can forecast traffic on the Los Angeles region and ...

METR-LA Point Missing Dataset - Papers With Code

The original dataset from Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting contains traffic readings collected from 207 ...

Graph Neural Network predicts traffic | Towards Data Science

Graph neural network (GNN) is a novel deep-learning method for traffic speed/time prediction. We will build a GNN model to predict traffic speed in this ...

Transfer Learning with Graph Neural Networks for Short-Term ...

method can learn from SFO region data and forecast for the LA region and vice versa. II. PROBLEM SETUP. The short-term highway traffic forecasting problem can ...

RiccardoSpolaor/Verbal-Explanations-of-Spatio-Temporal-Graph ...

Key information justifying these predictions is extracted from the input traffic network in the form of a significant subgraph. The information of the subgraph ...

[PDF] Graph Neural Network for Traffic Forecasting: A Survey

Semantic Scholar extracted view of "Graph Neural Network for Traffic Forecasting: A Survey" by Weiwei Jiang et al.

Predicting Evolution of Dynamic Graphs | by Tassos Sapalidis

For this project, we will focus on traffic prediction in the Los Angeles metro area using the PeMS District 7 dataset. Traffic data are ...

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

In recent years, deep-learning models, such as graph neural networks (GNN), have shown great promise in traffic forecasting due to their ability ...

Graph Convolutional Networks with Kalman Filtering for Traffic ...

Graph Neural Networks, Kalman Filtering, Traffic Forecasting. ACM ... METR-LA is collected by 207 traffic sensors on the highways of Los Angeles ...