Pattern Sensitive Prediction of Traffic Flow Based on Generative Adversarial Framework

Traffic flow prediction is one of the most popular topics in the field of the intelligent transportation system due to its importance. Powered by advanced machine learning techniques, especially the deep learning method, prediction accuracy noticeably increases in recent years. However, most existing methods applied a data-driven paradigm and tend to ignore the outliers, which result in poor performance while handling burst phenomena in the traffic system. To overcome this problem, the prediction model needs to recognize different patterns and handle them in different ways. In this paper, we propose a new prediction model (called pattern sensitive network) that can handle different traffic patterns automatically. By using adversarial training, our model can make more accurate predictions in unusual states without compromising its performance in usual states. Experiments demonstrate that our method can work well in both usual traffic states and unusual traffic states.