Structure-Aware Graph Multi-Task Learning for Dynamic Sparse OD Demand Prediction

arXiv:2606.21022v1 Announce Type: cross Abstract: Origin-Destination (OD) demand prediction is fundamental to intelligent transportation systems, yet real-world OD flows are often dynamically sparse, long-tailed, and characterized by heterogeneous zero-flow patterns. These properties make it difficult to distinguish whether an OD connection is active from how much demand it generates once activated. Many existing methods primarily treat OD prediction as a single flow regression task, which limit