LAUSR

As a necessary component in intelligent transportation systems (ITS), traffic flow-based prediction can accurately estimate the traffic flow in a certain period and area in the future. However, despite the success of traditional research and current machine learning methods, traffic flow prediction models have limitations in terms of prediction accuracy and efficiency. In this work, we propose a novel traffic flow prediction model named Graph Convolution and Generative Adversative Neural Network (GCN-GAN), which leverages Graph Convolution Neural Network (GCN) module and Generative Adversative Neural Network (GAN) module to predict urban traffic flow. Firstly, the GCN module extracts historical traffic flow information in the graph structure. Secondly, the GAN module generates reliable traffic flow prediction results by adversative training. Additionally, GCN-GAN can parallelly generate prediction results rather than traditional one by one. Through experiments on the traffic flow dataset at multiple intersections, our GCN-GAN model outperforms the baseline methods by over 30.54% and has apparent advantages in multi-step prediction.