## 1 Introduction
A multilayered propagation structure may introduce ambiguity or the overfitting of information. How to effectively learn the global correlation of urban road networks has been a hot and challenging research topic [27; 28]. Figure 1: Spatial correlations. (**a**) Spatial relationship according to prior graph structure. (**b**) Pearson correlation coefficient for all nodes. As shown in Figure 2, the patterns of each node have not only certain commonalities but also heterogeneity. Specifically, node 1 has two traffic peaks, node 2 has only one traffic peak, and nodes 3 and 4 show strong similarity. Previous spatio-temporal studies of traffic prediction, whether they choose convolutional, recurrent neural networks, or advanced transformers, have only considered the nodes of the whole city as a whole to learn the temporal features, and they ignore the independence [29; 30]. A temporal module that can learn multinode heterogeneous features, while the loss of commonality is still lacking. To address the above challenges, we propose a local-global features fusion temporal convolutional network (LGTCN) for accurate spatial-temporal traffic flow prediction, which incorporates the bidirectional graph convolutional network, probabilistic sparse self-attention, and multichannel temporal convolutional network. Specifically, we employ bidirectional graph convolution to extract the bidirectional propagation of local road networks, as well as a probabilistic sparse self-attention mechanism to efficiently extract global features. Furthermore, we propose a multichannel temporal convolutional network to refine and extract temporal heterogeneity features without the loss of commonality; Finally, multilayer perceptron is used to integrate the hidden states and output predictions. Extensive experiments are conducted on four open access benchmark traffic datasets to evaluate the proposed model, and the experimental results demonstrate the effectiveness of our model. The main contributions of this paper are as follows:  
* We propose an innovative spatio-temporal traffic flow prediction scheme, which addresses the research difficulties in spatial and temporal dimensions. Figure 3: Feature distribution learned by self-attention. (**a**) The distribution of global attention values. (**b**) A node attention score in node order. (**c**) The attention score of a node is sorted from high to low. Figure 2: Heterogeneity in temporal dimension. * In the spatial local dimension, we propose bidirectional graph convolution to extract local traffic characteristics with bidirectional propagation. It is more in line with the reality of traffic flow compared to previous graph neural network studies. * In the spatial global dimension, we express spatial nodes as spatial sequences and employ a probabilistic sparse self-attention mechanism for node interaction computation. Our method can reduce the computational complexity while maintaining the effectiveness of feature extraction. * In the temporal dimension, we propose a multichannel temporal convolutional network to extract temporal heterogeneity features without the loss of commonality. We set the number of nodes equal to the number of channels to realize the fine extraction of multinode temporal features. Moreover, we utilize the dilated causal convolution, which can expand the sensory field to effectively extract the temporal features of long-term sequences while avoiding information leakage. The rest of this paper is organized as follows: Section 2 summarizes the literature review related to our topic; Section 3 introduces the problem studied in this paper; Section 4 introduces the proposed spatio-temporal traffic flow prediction method; Section 5 presents the experiments in four benchmark datasets, including descriptions of the datasets, results of the experiments, and findings. Finally, Section 6 concludes the paper and discusses possible future work.