Multistep Traffic Speed Prediction from Spatial-Temporal Dependencies Using Graph Neural Networks

Accurate traffic forecasting on citywide networks is one of the crucial urban data mining applications that accurately provide congestion warning and transportation scheduling. While previous work has made significant efforts to learn traffic temporal dynamics and spatial dependencies, two key limitations exist in current models: (1) most existing approaches solely capture spatial correlations among neighbors on predefined graph structure, and genuine relation may be missing due to the incomplete graph connections; and (2) existing methods are defective to capture the temporal trends because the recurrent and stacking structure employed in these methods suffers from the long-range temporal dependency vanquish problem. To overcome the difficulty in multistep prediction and further capture the dynamic spatial-temporal dependencies of traffic flows, we propose a new traffic speed prediction framework for multiscale graph attention networks (MS-GATNs). In particular, MS-GATNs is a hierarchically structured graph neural architecture that learns not only the local region-wise geographical dependencies but also the spatial semantics from a global perspective. Furthermore, a multiheads attention mechanism is introduced to empower our model with the capability of capturing complex nonstationary temporal dynamics. Experiments on real-world traffic data sets demonstrate that MS-GATNs outperforms the state-of-the-art baselines in long-term forecasting.