A spatio-temporal ensemble method for large-scale traffic state prediction

How to effectively ensemble multiple models while leveraging the spatio-temporal information is a challenging but practical problem. However, there is no existing ensemble method explicitly designed for spatio-temporal data. In this paper, a fully convolutional model based on semantic segmentation technology is proposed, termed as spatio-temporal ensemble net. The proposed method is suitable for grid-based spatio-temporal prediction in dense urban areas. Experiments demonstrate that through spatio-temporal ensemble net, multiple traffic state prediction base models can be combined to improve the prediction accuracy.