## 1 Introduction  
With the rapid growth of the number of vehicles in urban areas, cities face numerous challenges, such as traffic congestion and environmental pollution resulting from vehicle emissions. Intelligent Transportation Systems (ITS) can effectively alleviate the above problems and provide many conveniences to people's lives [1]. As a primary foundation in advancing ITS, precise traffic prediction plays a critical role in facilitating vehicle allocation, mitigating road congestion, reducing traffic accidents, and optimizing the operational capacity of urban road networks [2]. Traffic data often exhibit complex and dynamic spatio-temporal relationships [3], making accurate predictions challenging. In early studies [4, 5], traffic data were considered as linear time series for analysis, neglecting non-linear temporal relationships. With the advancement of deep learning, Recurrent Neural Networks (RNNs) have proven effective in capturing non-linear dependencies within time series, leading to their widespread application in traffic prediction [6]. Nevertheless, these methods focus solely on capturing traffic data's temporal aspects, failing to encompass the intrinsic spatial characteristics. A road's traffic state is influenced by its historical traffic state and its neighbors. That is to say, the traffic state of a road is significantly correlated with the spatial structure of the road network. Thus, it is necessary to model the spatial correlation among various road nodes in the traffic network [7]. Some early studies modeled road networks as grids and used Convolutional Neural Networks (CNNs) to capture spatial features [8]. However, due to the irregularity of traffic roads and the fact that CNN-based methods typically deal with Euclidean structured data, they cannot effectively capture the complex spatial features of urban road networks [9]. Recently, Graph Neural Networks (GNNs) have gained adequate traction in traffic prediction [10], providing a more suitable framework for modeling the spatial characteristics of road networks. GNN-based methods consider each road segment in the road network as a node in the graph [11; 12], while the relationships between different road segments are treated as edges. This way, the road network is constructed as a structured graph. While most GNN-based methods have shown promising results, they often rely on predefined static adjacency matrices that cannot effectively capture the complex and dynamically changing spatial dependencies in traffic data. Some methods also extract spatial features by constructing adaptive graph structures [12; 13], where the model generates the adjacency matrix through learning. However, the adaptive and predefined adjacency matrices remain static, limiting their ability to capture the dynamic spatial dependencies. In real-life scenarios, each road node in a road network can have varying effects on the traffic state of its neighboring road nodes over time. The correlations between road segments are dynamically changing. Figure 1a illustrates a traffic road network instance where sensors are strategically placed on the primary roads to record traffic speed data. Sensor 1 and Sensor 4 record the vehicle speeds on roads within residential areas, while Sensor 2 and Sensor 5 record the vehicle speeds on roads within the office area. Sensor 3 is located on a road that lies between these two areas. These sensors are abstracted as nodes in the graph, and the strength of spatial correlation between them is abstracted as edges. As time progresses, the traffic states on various road segments change, and the spatial correlation between road nodes also varies. Figure 1b illustrates the dynamic changes in correlation between nodes. During the morning peak hours, the traffic states in the residential area significantly affect Sensor 3, but the influence gradually diminishes over time. Furthermore, existing models commonly employ RNN-based methods or CNN-based methods to model temporal correlations [14; 15].