PatchST: A Patch Spatial-Temporal Network for Large-Scale Traffic Forecasting

Abstract

Traffic prediction plays a critical role in mitigating congestion, optimizing traffic flow, and enhancing urban mobility. Accurate predictions contribute directly to improved safety, reduced travel times, and a more efficient transportation system. For example, when a surge in traffic is anticipated, traffic signal timings can be adjusted in real-time to prevent gridlock and minimize accidents. However, the dynamic and complex nature of traffic patterns poses significant challenges for accurate forecasting. Recently, deep learning techniques like Spatial-Temporal Graph Neural Networks (STGNNs) have been employed to address these challenges. While these methods have shown promise, they often struggle with high computational complexity and difficulty in explicitly capturing contextual dependencies. In this paper, we introduce the Patch Spatial-Temporal Network (PatchST), an efficient network design tailored for time series forecasting. Our approach offers three primary contributions: 1) a quadratic reduction in computational and memory overhead for attention maps, particularly for large datasets; 2) enhanced forecasting accuracy through the incorporation of more extensive historical context; and 3) improved capability to capture both long-term and short-term spatial dependencies. Comprehensive experiments on well-established traffic forecasting datasets demonstrate that our model achieves state-of-the-art performance in both effectiveness and efficiency.