# A Review of Traffic Flow Prediction Methods in Intelligent Transportation System Construction
## 4 Deep Learning Models
### Graph Convolutional Neural Networks (GCNs)  
The CNN is only applicable to modeling tasks with Euclidean data, and the nodes in a traffic network usually present an irregular graph structure. The Euclidean and non-Euclidean data structures are shown in Figure 8. Therefore, the GCN [28], which models a non-Euclidean space, has gradually replaced the CNN and become a research hotspot in the field of short-term traffic flow prediction [29]. Since GCNs rely on graph convolution operations, they incur high computational costs. Moreover, due to the non-Euclidean nature of graph structures, the efficient parallelization of computations is challenging,  
Figure 7: GRU model diagram.  
which constrains their scalability in large-scale graph processing tasks. The core formula of the GCN is shown in Equation (11). Here, \(X\in R^{N\times C}\) is the signal matrix, including N signals with C-dimensional features; \(\Theta\in R^{C\times F}\) is the graph convolution parameter matrix; and \(Z\in R^{N\times F}\) is the signal matrix obtained after the convolution operation.  
\[Z=\overset{\sim-1/2}{\text{D}}^{\sim-1/2}A\overset{\sim-1/2}{\text{D}}X\Theta \tag{15}\]  
Graph convolution operations can be broadly classified into two categories: spatial domain-based and spectral domain-based graph convolutions. Spatial domain methods define graph convolution as the aggregation of feature information between adjacent nodes in the graph. Spectral domain methods, on the other hand, begin by processing graph signals and introduce filters to derive graph convolutions. This operation is interpreted as the process of removing noise from the graph signals. Both methods offer distinct advantages when handling graph data, and the most appropriate approach can be selected based on the specific requirements of the task.  
In most existing methods, graph convolutional networks (GCNs) use a fixed adjacency matrix to model spatial dependencies in traffic networks. However, in real-world scenarios, the spatial dependencies vary over time. [PERSON] et al. proposed a spatiotemporal graph convolutional network (GLSTGCN) for traffic prediction. They designed a graph learning module to capture dynamic spatial relationships in the traffic network and employed a gated mechanism with dilated causal convolution networks to capture long-term temporal correlations in traffic data. Experimental results demonstrated the superior performance of the GLSTGCN [30].  
To address the challenges of air traffic flow prediction, which involves complex spatial dependencies, nonlinear temporal dynamics, and weather influences, [PERSON] et al. proposed a cross-attention diffusion convolutional recurrent neural network (CA-DCRNN). This model utilizes diffusion convolution to capture spatial dependencies and an encoder-decoder architecture with predefined sampling to handle temporal dependencies [31]. [PERSON] et al. combined a GCN with GRU networks to capture spatiotemporal dependencies, and their experimental results on two public datasets showed that the GCN network exhibited strong spatial information capturing capabilities [32].