Automated Spatio-Temporal Synchronous Modeling with Multiple Graphs for Traffic Prediction

Abstract

Traffic prediction plays an important role in many intelligent transportation systems. Many existing works design static neural network architecture to capture complex spatio-temporal correlations, which is hard to adapt to different datasets. Although recent neural architecture search approaches have addressed this problem, it still adopts a coarse-grained search with pre-defined and fixed components in the search space for spatio-temporal modeling. In this paper, we propose a novel neural architecture search framework, entitled AutoSTS, for automated spatio-temporal synchronous modeling in traffic prediction. To be specific, we design a graph neural network (GNN) based architecture search module to capture localized spatio-temporal correlations, where multiple graphs built from different perspectives are jointly utilized to find a better message passing way for mining such correlations. Further, we propose a convolutional neural network (CNN) based architecture search module to capture temporal dependencies with various ranges, where gated temporal convolutions with different kernel sizes and convolution types are designed in search space. Extensive experiments on six public datasets demonstrate that our model can achieve 4%-10% improvements compared with other methods.