This paper explores the scalability of Graph Neural Networks (GNNs) in the context of traffic forecasting, a critical area for improving urban mobility and reducing congestion. Despite GNNs’ demonstrated effectiveness in handling complex spatiotemporal dependencies in traffic data, scaling them to large road networks remains challenging due to increased computational requirements. This study aims to evaluate how the accuracy and computational cost of a state-of-the-art traffic forecasting GNN, the Decoupled Dynamic Spatio-Temporal Graph Neural Network, change with varying road network sizes and complexities (i.e., sensor density). Using two real-world datasets, three experiments are conducted: scaling map area, scaling graph complexity, and testing the geographic location effect. Findings show that larger graphs generally improve accuracy and GPU efficiency. Moreover, geographic location affects accuracy, whereas sensor density has minimal impact.