Multi-scale convolutional networks for traffic forecasting with spatial-temporal attention

Abstract

• Capturing complex spatial-temporal correlations for both short and long term traffic prediction. • Applying soft thresholding fusion to eliminate unimportant spatial-temporal features. • Designing a trainable module to automatically determine the spatial and temporal thresholds. • Constructing multiple convolutions networks and forming groups of units to extract multi-scale features. Traffic forecasting is a classical spatial-temporal data prediction problem with extensive applications, such as dynamic traffic management, route planning and traffic congestion alleviation. However, accurate traffic forecasting is challenging due to complex correlations of traffic data. Few approaches are satisfied with both long-term and short-term prediction tasks. In this paper, we propose a novel Multi-Scale Convolutional Networks (MSCN), an end-to-end solution to solve traffic forecasting problem. MSCN first employs an encoder with spatial-temporal attention mechanism to model both spatial and temporal correlations. Then, the decoder utilizes multiple convolutions and form groups of units to to extract spatial-temporal features for different resolutions. In particular, we propose a soft thresholding fusion mechanism to adaptively adjust the conditions of spatial and temporal correlations. Experiments on two real traffic datasets demonstrate that the proposed MSCN obtains improvements over the state-of-the-art baselines.