Location and time embedded feature representation for spatiotemporal traffic prediction

As a fundamental spatiotemporal sequence forecasting problem, traffic prediction is pivotal in transportation management and urban computing. Nonetheless, the intricate and dynamic nature of spatiotemporal correlations presents significant obstacles in acquiring precise forecasts. Existing techniques utilize graph convolutional networks in conjunction with temporal modules, such as recurrent neural networks or transformer-based structures, to effectively extract spatiotemporal features. Unfortunately, current approaches struggle with outliers and fail to capture potential global correlations between different timestamps. In this study, we propose an innovative Spatio-Temporal Graph Convolution Network with Embedded location and time features (STEGCN) for traffic prediction problems, which can generate precise and prompt predictions. STEGCN effectively captures the complex interdependencies among location, time, and traffic volume by leveraging the TransD algorithm to embed their representations. For each timestamp, a graph convolution module is exploited to capture the spatial features, merged with the embeddings of location and time that serve as global external information. Then, we leverage a temporal module composed of 1-D convolutions to capture the spatiotemporal patterns. The traffic volume embedding is employed to constrain predictions within a reasonable range. Extensive experiments and rigorous analysis show that our STEGCN model outperforms state-of-the-art baselines, demonstrating exceptional performance and potential for practical application.