Dynamic multi-graph spatio-temporal learning for citywide traffic flow prediction in transportation systems

The complexity and dynamic nature of urban traffic systems necessitate efficient resource management for accurate traffic flow forecasting, enabling real-time adaptation and optimized resource allocation. Recent advancements in multi-graph spatio-temporal graph neural networks (STGNN) have demonstrated their capability to capture spatio-temporal correlations at multiple scales, significantly improving prediction accuracy. However, a persistent challenge lies in effectively aggregating neighborhood information for node representation learning, particularly in scenarios with sparse connectivity. To address this limitation, we propose an Attention-based Dynamic Multi-Graph Module (ADMGM) for traffic prediction, integrating Federated Learning (FL) within a Multi-Access Edge Computing (MEC) architecture. Our approach incorporates an Adaptive Enhancement Module (AEM) deployed at the edge, pre-trained to process high-volume, heterogeneous data from IoT devices. The ADMGM model comprises four key components: closeness, daily, weekly, and an external branch, each contributing to a comprehensive spatio-temporal representation of traffic dynamics. The AEM leverages long-term historical data at each node, capturing inter-node dependencies to generate enriched feature representations while enhancing the model ability to generalize across diverse traffic patterns. Furthermore, we introduce a clustered feature correlation graph to uncover latent relationships within long-term time series data, thereby strengthening spatio-temporal modeling. Extensive experiments on the TaxiBJ and BikeNYC datasets demonstrate that our model significantly reduces prediction errors, achieving state-of-the-art performance in traffic forecasting.