Queue Length Prediction Using Traffic-theory-based Deep Learning

Abstract

Intelligent Transport Systems (ITS) play an important role in achieving smooth and safe travel on urban road networks. ITS provide software-based traffic management based on traffic prediction, so they can’t manage traffic properly without accurate traffic prediction. Recently, spatio-temporal graph neural networks (STGNNs) have achieved significant improvements in traffic prediction by taking into account spatial and temporal dependencies in traffic data. However, although the length of congestion queues is one of the most important statistics in ITS because it can be used for proactive signal control and information providing, it has not been a prediction target in existing studies. In addition, the relationships between multimodal traffic variables have been ignored. Moreover, due to the significant impact of ITS on the real world, ITS tend to prefer explainable methods over black-box methods. In this study, we propose a Queueing-theory-based Neural Network (QTNN) for queue length prediction. QTNN combines data-driven STGNN methods with queueing-theory-based traffic engineering domain knowledge to make predictions accurate and explainable. Our experiments on queue length prediction using real-world data showed that QTNN outperformed the baseline methods, including state-of-the-art STGNNs, by 12.6% and 9.9% in RMSE and MAE, respectively.