Graph Neural Networks for Traffic Pattern Recognition: An Overview

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.