Spatio-temporal Graph Normalizing Flow for Probabilistic Traffic Prediction

With the development of the Intelligent Transportation Systems, a great deal of work has been proposed to tackle traffic prediction tasks. Despite their good performance, most traffic prediction models are point estimation models, lacking the capability to estimate the uncertainties of future traffic data, which is crucial in practical traffic decision-making. Aiming at this problem, we combine the probabilistic estimation capabilities of conditional normalizing flows with the spatio-temporal relationship learning of spatio-temporal graphs, leading to a Spatio-Temporal Graph Normalizing Flow (STGNF) model to estimate the distribution of future traffic data. We are the first to employ the conditional normalizing flows as the backbone for probabilistic traffic prediction. Then we design a spatio-temporal graph conditional fusion network to learn the spatio-temporal relationships between future and historical traffic data, which are provided to the conditional normalizing flows as conditional information. Extensive experiments on two real-world traffic datasets demonstrate that our proposed model significantly outperforms the state-of-the-art baselines.