Multi-stage attention spatial-temporal graph networks for traffic prediction

Accurate traffic prediction plays an important role in Intelligent Transportation System. This problem is very challenging due to the heterogeneity and dynamic spatio-temporal dependence of large-scale traffic data. Existing models often suffer two limitations: (1) They usually only consider one type of data in the input, or simply treat other collected time series data as features, ignoring the non-linear interactions among different series. In fact, heterogeneous data at a specific location has direct impacts on the predicted series. (2) The method based on graph convolutional network uses a fixed Laplacian matrix to model spatial correlation, without considering its dynamics. The aggregations also occur only in the neighborhood, making it difficult to capture long-range dependencies. In this paper, we propose a Multi-Stage Attention Spatial-Temporal Graph Networks (MASTGN). First, an internal attention mechanism is designed to capture the interactions among multiple time series collected by the same sensor. Second, to model the complex spatial correlations, we apply a dynamic neighborhood-based attention mechanism. Unlike the general attention-based methods that ignore the structure information of the road network, we use the adjacency relations as a prior to divide the nodes of a road network into different neighborhood sets. In this way, attention can capture spatial correlations both within the same order neighborhood, and among different neighborhoods dynamically. Furthermore, a temporal attention mechanism is used to extract the dynamic temporal dependencies. Experiments are conducted on two real traffic datasets, and the results verify the effectiveness of the proposed model. (c) 2020 Elsevier B.V. All rights reserved.

Automated summary

In this paper, we propose a Multi-Stage Attention Spatial-Temporal Graph Networks (MASTGN) model for traffic prediction, which captures interactions among multiple time series collected by the same sensor using internal attention mechanism, models complex spatial correlations with dynamic neighborhood-based attention mechanism, and extracts dynamic temporal dependencies using temporal attention mechanism. Experiments on real traffic datasets demonstrate the effectiveness of the proposed model.