STTF: An Efficient Transformer Model for Traffic Congestion Prediction

Abstract With the rapid development of economy, the sharp increase in the number of urban cars and the backwardness of urban road construction lead to serious traffic congestion of urban roads. Many scholars have tried their best to solve this problem by predicting traffic congestion. Some traditional models such as linear models and nonlinear models have been proved to have a good prediction effect. However, with the increasing complexity of urban traffic network, these models can no longer meet the higher demand of congestion prediction without considering more complex comprehensive factors, such as the spatio-temporal correlation information between roads. In this paper, we propose a traffic congestion index and devise a new traffic congestion prediction model spatio-temporal transformer (STTF) based on transformer, a deep learning model. The model comprehensively considers the traffic speed of road segments, road network structure, the spatio-temporal correlation between road sections and so on. We embed temporal and spatial information into the model through the embedding layer for learning, and use the spatio-temporal attention module to mine the hidden spatio-temporal information within the data to improve the accuracy of traffic congestion prediction. Experimental results based on real-world datasets demonstrate that the proposed model significantly outperforms state-of-the-art approaches.