Graph Neural Networks for Traffic Pattern Recognition: An Overview

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

This survey aims to provide an overview of the recent developments and applications of Graph Neural Networks (GNNs) in the field of traffic patterns recognition. The focus is on the utilization of GNNs to model and analyze traffic data and their effectiveness in solving various traffic-related tasks such as traffic flow prediction, congestion detection, and forecasting. The paper covers the latest literature on GNNs for traffic pattern recognition and provides insights into the strengths and limitations of these models. The results of this survey suggest that GNNs have the potential to significantly improve the accuracy and efficiency of traffic pattern recognition and can play a key role in revolutionizing the field of traffic management and prediction.

Original languageEnglish (US)
Title of host publication2023 IEEE International Conference on Smart Mobility, SM 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages110-115
Number of pages6
ISBN (Electronic)9798350312751
DOIs
StatePublished - 2023
Event2023 IEEE International Conference on Smart Mobility, SM 2023 - Thuwal, Saudi Arabia
Duration: Mar 19 2023Mar 21 2023

Publication series

Name2023 IEEE International Conference on Smart Mobility, SM 2023

Conference

Conference2023 IEEE International Conference on Smart Mobility, SM 2023
Country/TerritorySaudi Arabia
CityThuwal
Period03/19/2303/21/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • Graph neural networks
  • intelligent transportation systems
  • smart mobility
  • traffic pattern recognition

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Control and Optimization
  • Transportation

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