## 2 Related Work  
In the past decades, traffic prediction has been an essential component of ITS and has been extensively researched. Earlier research efforts were usually based on statistical methods for traffic prediction, ignoring the nonlinear characteristics and complex variations in traffic data [4]. Machine learning methods can capture the nonlinear dependencies in traffic data compared to statistical methods [5]. However, they rely on high-quality manual features, which can be time-consuming to extract.  
With the continuous development of traffic big data and artificial intelligence technologies [21], a growing body of research has proposed various spatio-temporal modeling methods to capture the spatio-temporal features within traffic data [7]. Existing approaches usually model traffic data's temporal and spatial dimensions separately [22]. Sequence models are typically used to extract temporal relationships, such as Long Short-Term Memory (LSTM) [23], Gated Recurrent Units (GRUs) [24], and Temporal Convolutional Networks (TCNs) [25]. As traffic road networks naturally possess a non-Euclidean structure, GNN-based approaches are well suited for capturing the non-Euclidean relationships between multiple traffic time series to model spatial dependencies [26]. For instance, the Temporal Graph Convolutional Network (T-GCN) leverages graph convolutional networks (GCNs) to capture spatial features and GRUs to capture temporal features, which can effectively model spatio-temporal correlations [27].  
Recent research has highlighted the limitations of predefined adjacency matrices in adequately capturing the spatial relationships and latent information between nodes when designing spatio-temporal GNNs. The Adaptive Graph Convolutional Recurrent Network (AGCRN) addresses this issue by learning specific node attributes and constructing adaptive graphs to explore latent spatial relationships further [13]. Similarly, [PERSON] et al. [28] adopted a macroscopic and microscopic perspective to learn global and local spatial structures, aiming to acquire an optimal graph structure. [PERSON] et al. [29] proposed a Meta-Graph Learner that relies solely on observed data to construct an adaptive adjacency matrix. The abovementioned methods have further improved prediction accuracy in traffic forecasting tasks, indicating that adaptive adjacency matrices can compensate for the limitations of predefined adjacency matrices in modeling spatial correlations. However, in traffic data, the spatial relationships among road segments are vary with time. [PERSON] et al. [17] designed a dynamic graph generator that extracts static, dynamic, and temporal information from traffic data to generate dynamic graphs. [PERSON] et al. [30] employed a channel attention mechanism to allocate dynamic weights to historical traffic sequences at different time steps to achieve dynamic adjustment of spatio-temporal correlation. In the work of [PERSON] et al. [31], dynamic graphs were generated by combining spatial heterogeneity information and geospatial proximity information at each time step. [PERSON] et al. [32] modeled dynamic spatial correlations by exploring fine-grained features between nodes. [PERSON] et al. [33] concatenated spatial information from recent time steps and each past time step to generate dynamic spatio-temporal graphs. Despite achieving promising predictive performance, the above methods rely on prior road spatial knowledge and fail to effectively extract dynamic features from traffic data and model dynamic spatial correlations.  
Motivated by the abovementioned research, we propose a novel traffic prediction model called DSTGFCN. This model addresses the challenges of complex and dynamic road networks by extracting dynamic spatial information from observed data and generating a dynamic adjacency matrix at each time step, all without relying on prior knowledge of the road spatial relationships. Therefore, DSTGFCN is not limited to a fixed spatial structure and applies to large-scale traffic road networks.