## 2 Development History of Traffic Prediction
### Deep Learning Methods
The relationships between data entities are not only based on physical spatial distances but also involve topology or network connectivity, which is challenging for CNNs. The introduction of Graph Neural Networks (GNNs) has provided better solutions for this aspect. The core advantage of GNNs lies in their ability to capture the complex relationships between nodes and the structural properties of networks. The basic idea of GNNs is to aggregate information from neighboring nodes through an iterative process to learn node representations. Each node's representation is based on an aggregation function of its features and neighbors' features. This aggregation mechanism allows information to flow in the graph, enabling each node to indirectly access and learn information about distant dependencies. GCNs have evolved from the GNN framework and are specifically designed to leverage the local connectivity patterns of graph-structured data to learn node features efficiently. GCNs provide a powerful way to process nodes and their relationships by applying convolutional operations on graph data. In 2018, in the realm of traffic prediction utilizing GCNs, the Diffusion Convolutional RNN (DCRNN) [39] and the Spatiotemporal GCN (STGCN) [40] represent early attempts to incorporate the structural graph of road networks. These models combine GCNs with RNNs to capture the spatiotemporal correlations inherent in traffic network structures. It is worth noting that in 2019, [PERSON] et al. [41] proposed the Attention-Based STGCN (ASTGCN), which integrates attention mechanisms into the STGCN framework. This enhances the model's ability to capture relationships between different nodes in the traffic network, thereby improving feature extraction accuracy. Another typical example is the Temporal Graph Convolutional Network (T-GCN) [42], which integrates the GRU and GCN in a unified manner, simultaneously optimizing spatial and temporal features during training. It demonstrates excellent performance in spatiotemporal traffic prediction tasks--considering that most studies model spatial dependencies based on fixed graph structures, assuming that the fundamental relationships between entities are predetermined, i.e., static graphs. However, this cannot fully reflect the true dependencies relationships, so some research has begun to explore adaptive graphs. A typical example is the Graph Wavelet Neural Network (Graph WaveNet) [43], which utilizes a novel adaptive dependency matrix and learns through node embeddings. The model framework consists of GCNs and TCNs. Considering the multi-modal and compound spatial dependencies in traffic road networks, much work has begun focusing on multi-graph research in subsequent developments. For example, the Temporal Multi-GCN (T-MGCN) [44] encodesspatial correlations between roads as multiple graphs, which are then processed by GCNs and combined with RNNs to capture dynamic traffic flow patterns over time. In recent years, many research methods have begun to explore the complex relationships between spatial and temporal data in more detail. For example, the Dynamic Spatial-Temporal Aware GNN (DSTAGNN) [45] proposed a method to measure the spatiotemporal distances between different nodes, effectively integrating multi-head attention mechanisms. In the evolution of deep learning for traffic prediction, RNNs, CNNs, and GNNs have played significant roles. However, these models still need to be improved in handling long-term dependencies and global information. Introducing the transformer [46] model provides a new and powerful tool for traffic prediction. Unlike previous RNNs, the transformer relies entirely on a method called attention mechanism to process sequence data. This attention mechanism lets the model focus on different parts of the input data sequence, effectively capturing long-range dependencies. Many studies integrate the transformer with traffic prediction. For example, in 2020, [PERSON] et al. [47] introduced a novel time-based positional encoding strategy in traffic flow.