  Accurate traffic forecasting, the foundation of intelligent transportation
systems (ITS), has never been more significant than nowadays due to the
prosperity of smart cities and urban computing. Recently, Graph Neural Network
truly outperforms the traditional methods. Nevertheless, the most conventional
GNN-based model works well while given a pre-defined graph structure. And the
existing methods of defining the graph structures focus purely on spatial
dependencies and ignore the temporal correlation. Besides, the semantics of the
static pre-defined graph adjacency applied during the whole training progress
is always incomplete, thus overlooking the latent topologies that may fine-tune
the model. To tackle these challenges, we propose a new traffic forecasting
framework -- Spatio-Temporal Latent Graph Structure Learning networks
(ST-LGSL). More specifically, the model employs a graph generator based on
Multilayer perceptron and K-Nearest Neighbor, which learns the latent graph
topological information from the entire data considering both spatial and
temporal dynamics. Furthermore, with the initialization of MLP-kNN based on
ground-truth adjacency matrix and similarity metric in kNN, ST-LGSL aggregates
the topologies focusing on geography and node similarity. Additionally, the
generated graphs act as the input of the Spatio-temporal prediction module
combined with the Diffusion Graph Convolutions and Gated Temporal Convolutions
Networks. Experimental results on two benchmarking datasets in real world
demonstrate that ST-LGSL outperforms various types of state-of-art baselines.
