Knowledge Transfer Service for Multi-Granular Traffic Prediction in Smart Cities

Traffic prediction is essential for the efficient resource allocation and management of smart cities. However, many cities face challenges in accessing sufficient traffic data due to high collection costs and privacy concerns, which impacts the accuracy of predictions. To address the issue of data sparsity, transfer learning methods have been explored with promising outcomes. Nonetheless, key challenges remain in determining which knowledge to transfer and how to transfer it effectively. In this paper, we propose a Knowledge Transfer Service for multi-granular traffic prediction in smart cities, leveraging Multi-Granular Spatiotemporal Pattern Transfer Learning (MGSTPAT). MGSTPAT utilizes meta-learning to acquire rich, multi-granularity spatiotemporal knowledge from data-abundant source cities and initializes robust models. Additionally, our Embedded Adaptive Clustering module generates task-specific multi-granularity patterns, while the Granularity-Aware Pattern Attention mechanism enables target cities with limited data to select the most relevant patterns, enhancing prediction accuracy. Extensive experiments on real-world datasets demonstrate the effectiveness of our approach in addressing traffic prediction challenges across cities.