Accurate traffic prediction is pivotal when constructing intelligent cities to enhance urban mobility and to efficiently manage traffic flows. Traditional deep learning-based traffic prediction models primarily focus on capturing spatial and temporal dependencies, thus overlooking the existence of spatial and temporal heterogeneities. Heterogeneity is a crucial inherent characteristic of traffic data for the practical applications of traffic prediction. Spatial heterogeneities refer to the differences in traffic patterns across different regions, e.g., variations in traffic flow between office and commercial areas. Temporal heterogeneities refer to the changes in traffic patterns across different time steps, e.g., from morning to evening. Although existing models attempt to capture heterogeneities through predefined handcrafted features, multiple sets of parameters, and the fusion of spatial–temporal graphs, there are still some limitations. We propose a self-supervised learning-based traffic prediction framework called Traffic Prediction with Self-Supervised Learning (TPSSL) to address this issue. This framework leverages a spatial–temporal encoder for the prediction task and introduces adaptive data masking to enhance the robustness of the model against noise disturbances. Moreover, we introduce two auxiliary self-supervised learning paradigms to capture spatial heterogeneities and temporal heterogeneities, which also enrich the embeddings of the primary prediction task. We conduct experiments on four widely used traffic flow datasets, and the results demonstrate that TPSSL achieves state-of-the-art performance in traffic prediction tasks.

Graph-embedding learning is the foundation of complex information network analysis, aiming to represent nodes in a graph network as low-dimensional dense real-valued vectors for the application in practical analysis tasks. In recent years, the study of graph network representation learning has received increasing attention from researchers, and, among them, graph neural networks (GNNs) based on deep learning are playing an increasingly important role in this field. However, the fact that higher-order neighborhood information cannot be used effectively is a problem of most existing graph neural networks. Moreover, it tends to ignore the influence of latent representation and structural properties on graph embedding. In hopes of solving these issues, we introduce centrality encoding to learn the node properties, add an attention mechanism consideration to better distinguish the significance of neighboring nodes, and introduce random walk regularization to make sample neighbors that consistently satisfy predetermined criteria. This allows us to learn a representation of a potential node. We tested the performance of our model on node-clustering and link prediction tasks using three widely recognized benchmark datasets. The outcomes of our experiments demonstrate that our model significantly surpasses the baseline method in both tasks, indicating that the graph embedding it generates is highly expressive.