Multiple Information Spatial–Temporal Attention based Graph Convolution Network for traffic prediction

Abstract

Traffic prediction (forecasting) is a key problem in intelligent transportation. It helps engineers to obtain traffic trends in advance so that they can make favorable decisions quickly and accurately, or even improve the working mode of existing systems. However, it is very challenging to design a model for such problem that fully utilize the factors related to traffic. This paper investigates machine learning in traffic prediction and proposes Multiple Information Spatial–Temporal Attention based Graph Convolution Networks (MISTAGCN). The model consists of two parts. The first part utilizes combinations of different inputs and graph structures to compute the corresponding latent variables. In the second part of the model, the latent variables are comprehensively integrated and deeply mined. In particular, as the basic component of the model, a STBlock integrates mechanisms such as temporal attention, spatial attention, graph convolution, ordinary convolution and residual connections to fully explore the potential information contained in the data. Experiments on Haikou online car-hailing dataset and New York yellow taxi trip dataset illustrate that the proposed model outperforms state-of-art baseline models.