Multi-Task Synchronous Graph Neural Networks for Traffic Spatial-Temporal Prediction

Abstract

Traffic spatial-temporal prediction is of great significance to traffic management and urban construction. In this paper, we propose a multi-task graph Synchronous neural network (MTSGNN) to synchronously predict the spatial-temporal data at the regions and transitions between regions. The method of constructing "multitask graph representation" is proposed to retain the information of regions and transitions that existing works can not reflect. Then our model synchronously captures multiple types of dynamic spatial correlations, models dynamic temporal dependencies and re-weights different time steps to solve the problem of long-term time modeling. In three real data sets, we verify the validity of the proposed model.