Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting

TLDR

Spatial‑temporal network data forecasting is crucial for traffic management and urban planning, yet complex spatial‑temporal correlations and heterogeneities make it challenging. The authors propose Spatial‑Temporal Synchronous Graph Convolutional Networks (STSGCN) to forecast spatial‑temporal network data. STSGCN captures localized spatial‑temporal correlations via a synchronous modeling mechanism and includes modules for different time periods to handle heterogeneities. Experiments on four real‑world datasets show that STSGCN achieves state‑of‑the‑art performance, consistently outperforming baselines.

Abstract

Spatial-temporal network data forecasting is of great importance in a huge amount of applications for traffic management and urban planning. However, the underlying complex spatial-temporal correlations and heterogeneities make this problem challenging. Existing methods usually use separate components to capture spatial and temporal correlations and ignore the heterogeneities in spatial-temporal data. In this paper, we propose a novel model, named Spatial-Temporal Synchronous Graph Convolutional Networks (STSGCN), for spatial-temporal network data forecasting. The model is able to effectively capture the complex localized spatial-temporal correlations through an elaborately designed spatial-temporal synchronous modeling mechanism. Meanwhile, multiple modules for different time periods are designed in the model to effectively capture the heterogeneities in localized spatial-temporal graphs. Extensive experiments are conducted on four real-world datasets, which demonstrates that our method achieves the state-of-the-art performance and consistently outperforms other baselines.