Abstract
The issue of urban traffic congestion is becoming increasingly severe, highlighting the need for accurate traffic flow prediction to facilitate effective traffic management. Traditional prediction methods often struggle to address the complex spatiotemporal dependencies and external factors inherent in traffic data. This paper reviews existing deep learning models applied to traffic flow prediction, categorizing them into spatial and temporal types based on their dependencies. Spatial dependency models include those based on Euclidean space, such as CNN-based models, and non-Euclidean space models that combine Graph Convolutional Networks (GCN) with attention mechanism. These models effectively capture spatial variations in traffic flow data. Temporal dependency models, such as Recurrent Neural Network (RNN) and Temporal Graph Convolutional Network (T-GCN), focus on capturing long-term dependencies and time series dynamics. The selection of models depends on specific tasks and data characteristics. The article concludes with a summary and outlook on the entire discussion.
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