Efficient Traffic Forecasting on Large-Scale Road Network by Regularized Adaptive Graph Convolution

ArXi:2506.07179v2 Announce Type: replace-cross Traffic prediction is a critical task in spatial-temporal forecasting with broad applications in travel planning and urban management. To model the complex spatial-temporal dependencies in traffic data, Spatial-Temporal Graph Convolutional Networks (STGCNs) have been widely employed, achieving advanced performance. However, when applied to large-scale road networks, the quadratic computational complexity of traditional graph convolution operations severely limits their scalability.