ADMGNAS: Attention-based Dynamic Multiview Spatiotemporal Graph Neural Architecture Search for Traffic Prediction in Smart City

Spatiotemporal graph neural networks (STGNNs) have proven especially effective in traffic prediction tasks by modeling sensors or regions as nodes, with distances and correlations as edges. Their ability to capture complex spatiotemporal dependencies in road networks drives key applications in public safety, urban planning, and intelligent transportation. However, existing STGNNs often rely on manually designed architectures and typically process multiview graphs separately, requiring specialized expertise, thus limiting flexibility and overlooking intricate spatiotemporal interdependencies. To address these challenges, we propose an Attention-based Dynamic Multiview Spatiotemporal Graph Neural Architecture Search (ADMGNAS) framework, comprising three interconnected components. First, we introduce a unified Attention-based Dynamic Multiview Spatiotemporal Graph (ADMSTG) architecture that integrates spatiotemporal and view-aware attention mechanisms, enabling the effective capture of complex cross-view relationships. Building upon this architecture, we then develop a dedicated Multiview Attention Spatiotemporal (MVAS) search space, which systematically automates the selection of optimal attention operations. Then, a specialized differentiable search algorithm efficiently explores the MVAS space to dynamically identify architecture variations specifically tailored to dynamic multiview spatiotemporal graphs. Extensive experiments on benchmark datasets show that ADMGNAS consistently outperforms SOTA methods, proving its effectiveness and adaptability.