Graph convolutional neural networks for traffic forecasting and prediction: A review

Traffic forecasting and prediction are crucial in urban planning, transportation management, and decision-making. Traditional methods often struggle to capture traffic data’s complex spatial dependencies and temporal dynamics. In recent years, Graph Convolutional Neural Networks (GCNs) have emerged...

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Bibliographic Details
Main Authors: Singh, Vandana, Sahana, Sudip Kumar, Bhattacharjee, Vandana
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Forecasting
Graph neural networks
Spatial dependencies
Traffic information
Transportation management
Transportation networks
Urban planning
Online Access:Get full text
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Summary:Traffic forecasting and prediction are crucial in urban planning, transportation management, and decision-making. Traditional methods often struggle to capture traffic data’s complex spatial dependencies and temporal dynamics. In recent years, Graph Convolutional Neural Networks (GCNs) have emerged as a powerful tool for addressing these challenges. GCNs leverage graph structures to model relationships between traffic nodes, such as road intersections or segments, and have shown promising results in various traffic forecasting and prediction tasks. Within this paper, an extensive exploration unfolds, delving into the utilization of GCNs in the realm of traffic forecasting and prediction. It meticulously illuminates their inherent strengths, navigates through limitations, and envisions potential future trajectories. Additionally, a pioneering framework for traffic forecasting takes centre stage within these pages.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0214616