Attention based spatio-temporal generative adversarial network for sparse traffic forecasting

Abstract

Traffic forecasting plays an important role in intelligent traffic system. Forecasting traffic data in the future will provide great convenience in our daily life, such as avoiding congested roads in advance. In recent times, many methods for traffic prediction have been proposed, but most of these methods use complete data sets for prediction, and seldom pay attention to the sparse spatiotemporal data sets. Some recent studies mostly complete the data first before the prediction of sparse data. Therefore, this paper proposes Attention based Spatio-Temporal Generative Adversarial Network (ASTGAN) to solve this problem. ASTGAN uses the attention mechanism to preprocess the sparse data to utilize the temporal dependency to pre-complete the data, and then input the processed data into an mask graph convolutional recurrent network, which further complete the data with its spatial correlations and provide forecasting results. In order to ensure the accuracy and authenticity of the prediction, we also use generative adversarial network. Experiments on real data demonstrate the effectiveness of our method.