Graph Neural Networks Empowered Origin-Destination Learning for Urban Traffic Prediction

Urban traffic prediction with high precision is always the unremitting pursuit of intelligent transportation systems and is instrumental in bringing smart cities into reality. The fundamental challenges for traffic prediction lie in the accurate modelling of spatial and temporal traffic dynamics. Existing approaches mainly focus on modelling the traffic data itself, but do not explore the traffic correlations implicit in origin-destination (OD) data. In this paper, we propose STOD-Net, a dynamic spatial-temporal OD feature-enhanced deep network, to simultaneously predict the in-traffic and out-traffic for each and every region of a city. We model the OD data as dynamic graphs and adopt graph neural networks in STOD-Net to learn a low-dimensional representation for each region. As per the region feature, we design a gating mechanism and operate it on the traffic feature learning to explicitly capture spatial correlations. To further capture the complicated spatial and temporal dependencies among different regions, we propose a novel joint feature, learning block in STOD-Net and transfer the hybrid OD features to each block to make the learning process spatiotemporal-aware. We evaluate the effectiveness of STOD-Net on two benchmark datasets, and experimental results demonstrate that it outperforms the state-of-the-art by approximately 5% in terms of prediction accuracy and considerably improves prediction stability up to 80% in terms of standard deviation.