Traffic speed prediction is among the key problems in intelligent transportation system (ITS). Traffic patterns with complex spatial dependency make accurate prediction on traffic networks a challenging task. Recently, a deep learning approach named Spatio-Temporal Graph Convolutional Networks (STGCN) has achieved state-of-the-art results in traffic speed prediction by jointly exploiting the spatial and temporal features of traffic data. Nonetheless, applying STGCN to large-scale urban traffic network may develop degenerated results, which is due to redundant spatial information engaging in graph convolution. In this work, we propose a motif-based graph-clustering approach to apply STGCN to large-scale traffic networks. By using graph-clustering, we partition a large urban traffic network into smaller clusters to prompt the learning effect of graph convolution. The proposed approach is evaluated on two real-world datasets and is compared with its variants and baseline methods. The results show that graph-clustering approaches generally outperform the other methods, and the proposed approach obtains the best performance.