Traffic Flow Prediction with Parallel Data

Abstract

Traffic prediction is an elemental function of Intelligent Transportation Systems, and accurate and timely prediction is of great significance to both traffic management agencies and individual drivers. With the development of deep learning and big data, deep neural networks (DNN) achieve superior performances in traffic prediction. Developing DNN prediction models needs large scale and diverse data, however, it is costly to collect large volume of accurate traffic data. In this paper, we propose to use small volume of real traffic data and large volume of synthetic traffic data to developing traffic prediction models. The evolving of parallel system paradigm for traffic prediction and the algorithm to incrementally train traffic data generation models and traffic prediction models are presented. We use an improved generative adversarial networks to generate traffic data, and a stacked long short-term memory model for traffic prediction. Experimental results on a real traffic dataset demonstrate that our method can significantly improve the performance of traffic flow prediction.