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 language | English (US) |
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Title of host publication | 2023 IEEE International Conference on Smart Mobility, SM 2023 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 110-115 |
Number of pages | 6 |
ISBN (Electronic) | 9798350312751 |
DOIs | |
State | Published - 2023 |
Event | 2023 IEEE International Conference on Smart Mobility, SM 2023 - Thuwal, Saudi Arabia Duration: Mar 19 2023 → Mar 21 2023 |
Publication series
Name | 2023 IEEE International Conference on Smart Mobility, SM 2023 |
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Conference
Conference | 2023 IEEE International Conference on Smart Mobility, SM 2023 |
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Country/Territory | Saudi Arabia |
City | Thuwal |
Period | 03/19/23 → 03/21/23 |
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