MBA-STNet: Bayes-Enhanced Discriminative Multi-Task Learning for Flow Prediction

Crowd flow prediction, which aims to predict the in/out flows of different areas of a city, plays a critically important role in many real-world applications including intelligent transportation systems and public safety. The challenges of this problem lie in both the dynamic mobility patterns of crowds and the complex spatial-temporal correlations. Meanwhile, crowd flow is highly correlated to and affected by the Origin-Destination (OD) locations of the flow trajectories, which is largely ignored by existing works. In this paper, we study the novel problem of predicting the crowd flow and flow OD simultaneously, and propose a Multi-task Bayes-enhanced Adversarial Spatial Temporal Network entitled MBA-STNet to effectively address it. MBA-STNet adopts a shared-private framework which contains private spatial-temporal encoders, a shared spatial-temporal encoder, and decoders to learn the task-specific features and shared features. To effectively extract discriminative shared features, an adversarial loss on shared feature extraction is incorporated to reduce information redundancy. A Bayesian heterogeneous Spatio-temporal Attention Network is designed to learn the complex spatio-temporal correlations and alleviate the problem of data uncertainty. We also design an attentive temporal queue to capture the complex temporal dependency automatically without the help of domain knowledge. Extensive evaluations are conducted over the bike and taxicab trip datasets in New York. The results demonstrate that the proposed MBA-STNet is superior to state-of-the-art methods.