## 1 Introduction
[20] proposed an effective model to forecast the traffic time series from the perspective of temporal dependence. A multi-feature forecasting model was also proposed in [21] based on CNN to include a variety of temporal and spatial features and external factors. Having taken the spatial-temporal correlation of taxi demand allocation into account, ref. [22] proposed a combination model of CNN and LSTM to forecast taxi demand allocation in cities, which was only applicable to grid data in European space and not suitable for graph structure data of traffic network in non-Euclidean space. In recent years, the appearance of the graph neural network (GNN) has attracted more attention from many scholars and has been applied to traffic flow forecasting in a non-Euclidean space. Ref. [23] proposed the diffusion convolutional recurrent neural network (DCRNN), which transformed the traffic flow model into a random walk on the graph structure and captured the time features through encoder-decoder architecture. A temporal graph convolution network (T-GCN) model was proposed in [24], in which GCN was employed to capture spatial dependence, and the combined modular of GRU and advanced RNN was used to capture the temporal dependence of traffic flow. Ref. [25] proposed a spatio-temporal graph convolutional networks (STGCN) method based on graph structure, which captures the temporal and spatial correlation of traffic flow using convolution structure. Ref. [26] proposed an integrated depth graph reinforcement learning network, which preprocessed the original traffic dataset as input, forecast with convolution network and attention network, and finally optimized the weight coefficients of the two forecasting using reinforcement learning to generate the final forecasting results. Ref. [27] proposed an integrated in-depth learning spatial-temporal forecasting model for urban hot spots, which combines geographic forecasting with semantic forecasting neural network for a joint forecasting. Ref. [28] proposed the attention-based spatial-grey-graph neural network-long short-term memory neural network (AST-GCN-LSTM) model. The model improves the graph convolution network and expands the neighborhood of convolution. Although the above GCN-based models have comprehensively considered the spatial-temporal correlation characteristics of traffic flow, the most existing deep-learning models often require the extraction of spatial features on the entire traffic network with fixed nodes and traffic network topology. However, the association relationships among the traffic nodes are not invariable with different forecasting intervals. The spatial-temporal correlation characteristics for forecasting traffic flow are shown in Figure 1.  
Motivated by integrating the deep learning method with multi-view subspace clustering algorithm, this paper proposes an ensemble learning model named EnGS-DGR based on a dynamic graph reconfiguration algorithm with a specific forecasting interval, and an integration model combining the GCN with the Seq2 Seq learning modular. The main contributions of this paper are described as follows. 1. The traffic graph topology with closely-correlated traffic nodes is formulated with a specific forecasting interval on the basis of the designed dynamic graph reconfiguration algorithm, in which the method of the low-rank tensor constrained multi-view subspace clustering is employed for a quantitative analysis of the correlation degree among the traffic nodes. 2. Taken the time series obtained from reconfigured graph as the input, a GCN-Seq2 Seq integration strategy is proposed to extract the spatio-temporal correlation characteristics, in which the integration information involved with the local information of traffic nodes and the overall spatial characteristics extracted by GCN modular deliver to Seq2 Seq modular for obtaining the traffic flow forecasting. The remainder of this paper is organized as follows: In Section 2, the proposed EnGS-DGR model is described in detail.