# Dynamic Graph Convolutional Crowd Flow Prediction Model Based on Residual Network Structure
## 1 Introduction
[7] proposed an attention-based spatial-temporal graph convolutional network (ASTGCN) for traffic flow forecasting, which first inputs the traffic data into the attention mechanism to capture spatio-temporal relationships and then inputs them into the graph convolution structure to enhance the ability to model the dynamic spatio-temporal correlation of data. The model has a significant improvement in computational efficiency compared to the RNN model; however, these models share a common drawback. They train the model through static spatial graphs, ignoring the dynamic spatial changes  
Figure 1: Crowd flow and OD flow diagram. of the crowd traffic network, and there is an obvious deficiency in the learning ability of spatial dependence. [PERSON] et al. [8] argued that existing models typically use separate components to capture spatial and temporal correlations, and ignore the heterogeneity in spatio-temporal data. Therefore, they proposed to incorporate a synchronization mechanism based on previous studies and generated a new spatio-temporal synchronized graph convolutional network (STSGCN). [PERSON] and [PERSON] et al. [9] argued that the prioritized representation of a given spatial graph structure with imperfect adjacency connections may limit the effective spatio-temporal correlation learning of the model, the spatio-temporal fusion graph neural network (STFGNN), which can fuse different spatio-temporal graphs to improve the model prediction accuracy. [PERSON] et al. [10] proposed the use of spatial graph convolution to create a multi-view graph convolution network (MVGCN) for the crowd flow problem by describing the crowd flow prediction in irregular regions as a spatio-temporal graph (STG) prediction problem, where each node represents a region with the time-varying flow. [PERSON] et al. [11] proposed a three-dimensional graph convolution network (3D-DGCN) with point-of-interest (POIs) distribution as an additional feature, and used the K-mean algorithm to cluster N regions into K partitions for crowd flow prediction of irregular regions with POIs as features. Because similar functions in urban areas frequently have similar crowd flow patterns, similar functions are usually determined by similar POIs, which can significantly increase forecast accuracy [12]. Although urban area functions influence population flow patterns, POI features cannot adequately reflect urban area functions since they are inherently complex and dynamic. Although existing GCN methods have been used for crowd flow prediction with excellent results, they ignore the global spatial dependence. This is because it only focuses on the effect of direct neighboring nodes and ignores multi-order neighboring nodes [13]. Therefore, there is a deficiency in the model's expressiveness due to interactions between the inflow and outflow of nodes. Moreover, there is a correlation between the flows in the adjacent time slots of the target node in the crowd flow data. A dense crowd flow at 9:00 a.m. in a location will affect the crowd flow at 10:00 a.m. because the flow in the previous time slice will correlate with the flow in the subsequent time slice. In view of the above challenges and research implications, we propose a residual structure-based dynamic graph convolution model (Res-DGCN) on the basis of a deep understanding of crowd traffic data using the New York shared bicycle dataset and the New York yellow taxicab dataset, and improving the original model 3D-DGCN [11]. The following is a summary of this paper's contributions:  
* A new spatial attention module (SA) has been designed and integrated into the model. It obtains the dependencies between the current node and its indirect neighbors by learning the spatial correlations in the dynamic spatio-temporal graph in order to enable the coverage of the model by the indirect neighbors of the current node. * In this paper, we propose an improved conditional convolution module (SCondConv), which can perform deep convolution to extract noise-uncorrelated features with high efficiency.