## 1 Introduction  
The growing number of automobiles has created many challenges for urban transportation, including traffic congestion and traffic environment pollution [1, 2]. The failure to divert congested traffic results in a lot of wasted travelers' time and fuel consumption, which has been troubling urban transportation managers [3]. The development of Intelligent Transportation Systems (ITSs) offers promising solutions to this challenge [4]. In particular, high-precision spatial and temporal prediction of regional traffic flow prediction is important reference information of the ITS, which can provide support for traffic control and management and improve the efficiency of urban traffic operation [5]. In the past few decades, scholars have proposed a large number of traffic flow prediction models and achieved excellent prediction results. These models mainly include statistical methods, traditional machine learning, and deep learning methods. Statistical methods include the history average (HA) [6], autoregressive integrated moving average (ARIMA) [7], seasonal autoregressive integrated moving average (SARIMA) [8], Kalman filter model [9], etc. These models are simple, efficient, and easy to compute, but it is difficult to capture the nonlinear temporal correlation of traffic flow and the traffic spatial correlation in the city. To solve this problem, machine learning methods have been proposed and widely used, including k-nearest neighbor (KNN), support vector regression (SVR) [10], gradient boosting machine (GBDT) [11], etc. These methods can be applied to nonlinear traffic flow data, but they rely on manually setting parameters, and the feature extraction capability is still insufficient. Deep learning methods solve the above problems well. For example, Long Short-Term Memory Networks (LSTMs) and Gated Recurrent Units (GRUs) can learn long-term temporal dependencies [12; 13], and Temporal Convolutional Networks (TCNs) can realize a larger sensory field by expanding the convolution [14]. Furthermore, the topology of the road network can be modeled by advanced graph neural networks [15]. These models have achieved good results in traffic flow prediction, but it is always difficult to be satisfied with the improvement brought by simple models [16; 17]. Focusing on the spatial characteristics of urban transportation networks is considered to be a significant contribution to the improvement of prediction accuracy [18; 19; 20]. Early research project constructed traffic information as a spatio-temporal matrix and achieved good prediction results using convolutional neural networks (CNNs) [21; 22; 23]. However, it is applicable to standard Euclidean data, while real traffic spatial relationships (i.e., road networks) are usually non-Euclidean structured. The Graph Convolutional Network (GCN) proposed later introduces a spatially structured graph to represent road network nodes and connectivity relationships, and it shows excellent performance in predicting spatio-temporal traffic [2; 24; 25]. However, previous studies on traffic network graphs have overlooked the important fact that the nodal impacts of traffic flows can propagate not only in the forward direction, but also in the reverse direction. Specifically, a congested traffic flow not only affects subsequent moments at the next node of the roadway, but it also spreads to the previous node. In addition, nodes with the same land use attributes may have similar change patterns due to commuting behavior, even though these nodes are non-adjacent in terms of road topology, as shown in Figure 1. Note that Figures 1-3 illustrate the observational or experimental results in our dataset. Previous CNN and GCN studies could only extract long-distance features by deepening the number of layers, but they are prone to overfitting and difficult to obtain global features [26]. For classical CNNs and GCNs, information can only be propagated in neighboring vectors or nodes on the graph.