Self-Supervised Spatial-Temporal Bottleneck Attentive Network for Efficient Long-term Traffic Forecasting

In intelligent transportation systems, accurate long-term traffic forecasting is informative for administrators and travelers to make wise decisions in advance. Recently proposed spatial-temporal forecasting models perform well for short-term traffic forecasting, but two challenges hinder their applications for long-term forecasting in practice. Firstly, existing traffic forecasting models do not have satisfactory scalability on effectiveness and efficiency, i.e., as the prediction time spans extend, existing models either cannot capture the long-term spatial-temporal dynamics of traffic data or equip global receptive fields at the cost of quadratic computational complexity. Secondly, the dilemma between the models’ strong appetite for high-quality training data and their generalization ability is also a challenge we have to face. Thus how to improve data utilization efficiency deserves thoughtful thinking. Aiming at solving the long-term traffic forecasting problem and facilitating the deployment of traffic forecasting models in practice, this paper proposes an efficient and effective Self-supervised Spatial-Temporal Bottleneck Attentive Network (SSTBAN). Specifically, SSTBAN follows a multi-task framework by incorporating a self-supervised learner to produce robust latent representations for historical traffic data, so as to improve its generalization performance and robustness for forecasting. Besides, we design a spatial-temporal bottleneck attention mechanism, reducing the computational complexity meanwhile encoding global spatial-temporal dynamics. Extensive experiments on real-world long-term traffic forecasting tasks, including traffic speed forecasting and traffic flow forecasting under nine scenarios, demonstrate that SSTBAN not only achieves the overall best performance but also has good computation efficiency and data utilization efficiency.