Application of Graph Neural Networks in Road Traffic Forecasting for ...

Application of Graph Neural Networks in Road Traffic. Forecasting for Intelligent Transportation Systems. Ana Clara Moreira Gadelho. Mestrado ...

Graph Neural Network for Traffic Forecasting: A Survey - arXiv

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

Graph neural network for traffic forecasting: A survey - ScienceDirect

The latest application of graph neural network in traffic forecasting is presented. ... The often-ignored implementation and reproducibility in ...

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 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.

(PDF) Graph Neural Networks for Traffic Forecasting - ResearchGate

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.

Graph Neural Network for Traffic Forecasting: The Research Progress

Abstract: Traffic forecasting has been regarded as the basis for many intelligent transportation system (ITS) applications, including but ...

Graph neural network for traffic forecasting: : A survey

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

Revisitng graph neural networks for traffic forecasting - OpenReview

Accurate traffic forecasting is crucial for a wide range of traffic management applications. In recent years, Graph Neural Networks (GNNs) ...

Explainability techniques applied to road traffic forecasting using ...

Within the field of deep learning, techniques based on Graph Neural Networks (GNNs) have been shown to be highly effective for many applications, such as ...

Graph Neural Networks for Traffic Forecasting - Semantic Scholar

... traffic forecasting and review the recent development and application of graph neural networks (GNN) to this problem. GNNs are a class of deep learning ...

Traffic Graph Convolutional Recurrent Neural Network - arXiv

Abstract— Traffic forecasting is a particularly challenging application of spatiotemporal forecasting, due to the time-varying.

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 ...

Traffic Forecasting: The Power of Graph Convolutional Networks on ...

The researchers modeled the traffic network as a graph, with each road segment being a node and the connections between segments being the edges ...

Advancements of Graph Neural Networks in Urban Traffic Prediction

It uses a graph representation of the road network, with nodes standing in for intersections and edges for roads. Each node and edge are timestamped ...

Graph Neural Networks for Traffic Pattern Recognition: An Overview

Social networks analysis, such as vehicular social networks, is one of the common application examples that applies node classification. 3) Link Prediction: ...

Graph neural network in traffic forecasting: a review

In this paper, I give a review of the related work and the applications of GNNs in different traffic forecasting problems, e.g., bike sharing, metro flow, road ...

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

Traffic forecasting has been an important area of research for several decades, with significant implications for urban traffic planning, ...

Traffic forecasting using graph neural networks and LSTM - Keras

This example shows how to forecast traffic condition using graph neural networks and LSTM. Specifically, we are interested in predicting the future values of ...

[CFP] Information Fusion Special Issue "Graph Neural Network for ...

Specific problems of GNNs for traffic forecasting problems, e.g. road traffic flow/speed forecasting, passenger flow forecasting in urban ...