STCNN: A Spatio-Temporal Convolutional Neural Network for Long-Term Traffic Prediction

As many location-based applications provide services for users based on traffic conditions, an accurate traffic prediction model is very significant, particularly for long-term traffic predictions (e.g., one week in advance). As far, long-term traffic predictions are still very challenging due to the dynamic nature of traffic. In this paper, we propose a model, called Spatio-Temporal Convolutional Neural Network (STCNN) based on convolutional long short-term memory units to address this challenge. STCNN aims to learn the spatio-temporal correlations from historical traffic data for long-term traffic predictions. Specifically, STCNN captures the general spatio-temporal traffic dependencies and the periodic traffic pattern. Further, STCNN integrates both traffic dependencies and traffic patterns to predict the long-term traffic. Finally, we conduct extensive experiments to evaluate STCNN on two real-world traffic datasets. Experimental results show that STCNN is significantly better than other state-of-the-art models.