Diffusion Probabilistic Modeling for Fine-Grained Urban Traffic Flow Inference With Relaxed Structural Constraint

Inferring the citywide urban traffic flows is critical for numerous smart city applications such as urban planning, traffic control, and transportation management. Urban traffic flow inference problem aims to generate fine-grained flow maps from the coarse-grained ones. It is still challenging due to the lack of handling uncertainties of flow distributions and complex external factors that affect the inference performance. In this work, we propose a diffusion probabilistic augmentation-based network for considering the uncertainties of urban flows with a relaxed structural constraint and a disentangled scheme for flow map and external factor learning. Experiments are conducted on four large-scale urban flow datasets, and the results show that our method achieves significant performance improvements over strong baselines.