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Title	Diffuflow: Robust Fine-Grained Urban Flow Inference With Denoising Diffusion Model
ID_Doc	19922
Authors	Zheng Y.; Zhong L.; Wang S.; Yang Y.; Gu W.; Zhang J.; Wang J.
Year	2023
Published	International Conference on Information and Knowledge Management, Proceedings
DOI	http://dx.doi.org/10.1145/3583780.3614842
Abstract	Inferring the fine-grained urban flows based on the coarse-grained flow observations is practically important to many smart city-related applications. However, the collected urban flows are usually rather unreliable, may contain noise and sometimes are incomplete, thus posing great challenges to existing approaches. In this paper, we present a pioneering study on robust fine-grained urban flow inference with noisy and incomplete urban flow observations, and propose a denoising diffusion model named DiffUFlow to effectively address it with an improved reverse diffusion strategy. Specifically, a spatial-temporal feature extraction network called STFormer and a semantic features extraction network called ELFetcher are proposed. Then, we overlay the extracted spatial-temporal feature map onto the coarse-grained flow map, serving as a conditional guidance for the reverse diffusion process. We further integrate the semantic features extracted by ELFetcher to cross-attention layers, enabling the comprehensive consideration of semantic information for fine-grained flow inference. Extensive experiments on two large real-world datasets validate the effectiveness of our method compared with the state-of-the-art baselines. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.
Author Keywords	Denoising diffusion model; Spatial-temporal data mining; Urban flow inference

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