In recent years, graph structures have been widely used in traffic flow prediction. Methods that construct adaptive graphs for data have been shown to outperform models that rely on a single static graph structure. However, most of the current adaptive graphs are static in nature and are constructed in the training set. Since traffic data often suffers from irregular changes in time series, such model architectures are usually unable to reliably, accurately, and timely learn the complex dependencies in traffic data. This paper proposes a new traffic prediction framework - Progressive Multifaceted Dynamic Graph Convolutional Network. Specifically, a progressive adjacency matrix is constructed by learning the trend similarity between graph nodes, and a set of graphs are constructed by gradually adapting to online input data during training and testing phases. Finally, asymptotically dynamic graph convolution is used to capture the complex spatiotemporal relationships in the asymptotic graph to improve traffic prediction accuracy. We apply the model to 4 different real traffic datasets and achieve the best consistency on all datasets.

In recent years, Contrastive learning has shown great potential in traffic flow prediction tasks. However, existing contrastive learning methods have difficulties in dealing with missing data and noise, and it is difficult to fully capture local and global correlations by relying on a single contrast method. In this paper, a Decreasing Mask Spatio-Temporal Graph Comparison Learning Model (DMSTGCL) is proposed. The model dynamically adjusts the mask ratio through the adaptive mask reduction technique to effectively deal with the problem of missing data and noise. Meanwhile, the projection head is further combined with the TripleAttention mechanism in the spatio-temporal contrast learning process, which overcomes the limitations of a single contrast method and captures the complex relationships in local and global space more effectively. Experiments on three real-world datasets demonstrate that DMSTGCL achieves significantly higher prediction accuracy than existing methods.

Traffic flow prediction presents significant challenges due to complex spatio-temporal dependencies. Conventional static road network models fail to adequately capture dynamic traffic patterns and struggle with multi-scale feature extraction, limiting prediction accuracy. To address these problems, we present DMAGCN (Dynamic Multi-scale Adaptive Graph Convolutional Network), an innovative architecture combining MGTCN (Multi-scale Gated Temporal Convolution Network) and ADMGCN (Adaptive Dynamic Multi-Graph Convolutional Network) Modules. MGTCN extracts multi-scale temporal features by combining temporal attention mechanisms with gated convolutional networks, while ADMGCN enhances spatial representation through graph convolutions with spatial attention layers and adaptive adjacency matrices. Comprehensive experimental evaluations conducted on the PEMS04 and PEMS08 datasets demonstrate that DMAGCN consistently outperforms existing state-of-the-art methods in traffic flow prediction tasks.

Traffic flow prediction is a crucial research area that has been extensively studied using graph-based prediction methods. However, existing approaches often rely on static or dynamic graphs to model spatial dependencies, which may not capture diverse spatial correlations and dependencies arising from intricate traffic patterns. In this paper, we propose a novel Spatiotemporal Adaptive Hybrid Dynamic Graph Convolutional Network (STAHDGCN) to enhance traffic prediction accuracy. Specifically, we propose a hybrid spatial graph learning module designed to capture diverse spatial stability and contingency in the road network at different times. This module incorporates both a static adaptive learning module and a dynamic learning module. Following this, a spatial gate fusion module is employed to conduct feature fusion, effectively simulating the complex spatiotemporal dependence within road networks. Finally, a proposed adaptive spatiotemporal module utilizes an attention mechanism to effectively capture potential dependence patterns in both time and space, addressing the impact of spatial heterogeneity. Experimental evaluations on two public datasets, METRLA and PEMS-BAY, demonstrate the superior performance of our model.