MGC-GAN: Multi-Graph Convolutional Generative Adversarial Networks for Accurate Citywide Traffic Flow Prediction

Lincan Li, Jichao Bi, Kaixiang Yang, Fengji Luo, Luxing Yang

Abstract: Accurate citywide traffic flow prediction is of great importance to intelligent transportation system. Existing methods typically assume the complete citywide traffic data can be obtained in real-time, which is impossible in applications. Furthermore, many recent works only consider one single kind of spatial correlation in traffic network when building graph representations. This work proposes an adversarial learning framework named Multi-Graph Convolutional Generative Adversarial Networks (MGC-GAN) to address the aforementioned challenges. To generate citywide traffic flow predictions using limited traffic data, we construct three kinds of graphs using easily accessed geographical and semantic information to model the complex spatial correlations in citywide transportation networks. Following that, a parallel GCN layer is designed to separately process multiple graphs. In addition, we design the Parallel Graph Convolution and Temporal Convolution Module (PGTCM) to effectively capture the heterogeneous spatial-temporal dependencies. Extensive experiments are carried out on two citywide traffic datasets, demonstrating that MGC-GAN outperforms several state-of-the-art baseline methods.