GraphSAGE-Based Generative Adversarial Network for Short-Term Traffic Speed Prediction Problem

Traffic speed prediction is a significant branch of the intelligent transportation system (ITS). A good prediction could alleviate the non-recurring congestion on the road and provide a strong decision-making basis for traffic management and control. However, it is always a challenging research problem due to the complexity of the road network and the dynamics of traffic conditions. Many deep learning-based methods have been applied to the traffic prediction problem, which could extract both spatial and temporal information efficiently. However, for some dataset that suffers from data paucity problem, the generalization ability of the model is not good and the performance degrades. To tackle this problem, we proposed a novel graph-based generative adversarial network for the traffic speed prediction problem. We design a generative network to generate some fake traffic data and use a discriminative network to distinguish between real and fake targets. The generator consists of a GraphSAGE and LSTM model to learn the representation of spatial-temporal traffic data. Several experiments have been conducted on several real-world traffic datasets, demonstrating that our proposed model outperforms other baseline models. The experiment results illustrate the importance of utilizing GAN in the training process, which improves the generalization ability of the prediction model.