research-article

DiffUFlow: Robust Fine-grained Urban Flow Inference with Denoising Diffusion Model

Authors:

Jianxin WangAuthors Info & Claims

CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

Pages 3505 - 3513

https://doi.org/10.1145/3583780.3614842

Published: 21 October 2023 Publication History

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 human/vehicle trajectory flows are usually rather unreliable, may contain various 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. Specifically, we propose an improved reverse diffusion strategy. A spatial-temporal feature extraction network called STFormer and a semantic features extraction network called ELFetcher are also proposed. Then, we overlay the spatial-temporal feature map extracted by STFormer 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 encompassing the entirety of urban data in fine-grained inference. Extensive experiments on two large real-world datasets validate the effectiveness of our method compared with the state-of-the-art baselines.

References

[1]

Yuxuan Liang, Kun Ouyang, Lin Jing, Sijie Ruan, Ye Liu, Junbo Zhang, David S Rosenblum, and Yu Zheng. Urbanfm: Inferring fine-grained urban flows. In Proceedings of KDD, 2019.

[2]

Wenbin Liu, Yongjian Yang, En Wang, and Jie Wu. Fine-grained urban prediction via sparse mobile crowdsensing. In Proceedings of MASS, 2020.

[3]

Mingxiao Li, Hengcai Zhang, and Jie Chen. Fine-grained dynamic population mapping method based on large-scale sparse mobile phone data. In Proceeding of MDM, 2019.

[4]

Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang. Image super-resolution using deep convolutional networks. IEEE transactions on pattern analysis and machine intelligence, 2015.

[5]

Jiwon Kim, Jung Kwon Lee, and Kyoung Mu Lee. Accurate image super-resolution using very deep convolutional networks. In Proceedings of CVPR, 2016.

[6]

Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et al. Photo-realistic single image super-resolution using a generative adversarial network. In Proceedings of CVPR, 2017.

[7]

Zhihao Wang, Jian Chen, and Steven CH Hoi. Deep learning for image super-resolution: A survey. IEEE transactions on pattern analysis and machine intelligence, 2020.

[8]

Kun Ouyang, Yuxuan Liang, Ye Liu, Zekun Tong, Sijie Ruan, Yu Zheng, and David S Rosenblum. Fine-grained urban flow inference. IEEE transactions on knowledge and data engineering, 2020.

[9]

Yuxuan Liang, Kun Ouyang, Yiwei Wang, Ye Liu, Junbo Zhang, Yu Zheng, and David S Rosenblum. Revisiting convolutional neural networks for citywide crowd flow analytics. In Proceeding of ECML-PKDD, 2021.

Digital Library

[10]

Jiyue Li, Senzhang Wang, Jiaqiang Zhang, Hao Miao, Junbo Zhang, and S Yu Philip. Fine-grained urban flow inference with incomplete data. IEEE Transactions on Knowledge and Data Engineering, 2022.

[11]

Xueyan Yin, Genze Wu, Jinze Wei, Yanming Shen, Heng Qi, and Baocai Yin. Deep learning on traffic prediction: Methods, analysis, and future directions. IEEE Transactions on Intelligent Transportation Systems, 2021.

[12]

Junbo Zhang, Yu Zheng, and Dekang Qi. Deep spatio-temporal residual networks for citywide crowd flows prediction. In Proceedings of AAAI, 2017.

[13]

Senzhang Wang, Jiannong Cao, Hao Chen, Hao Peng, and Zhiqiu Huang. Seqstgan: Seq2seq generative adversarial nets for multi-step urban crowd flow prediction. ACM Transactions on Spatial Algorithms and Systems (TSAS), 6(4):1--24, 2020.

[14]

Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121, 2019.

[15]

Chao Song, Youfang Lin, Shengnan Guo, and Huaiyu Wan. Spatial-temporal synchronous graph convolutional networks: A new framework for spatial-temporal network data forecasting. In Proceeding of AAAI, 2020.

[16]

Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In Proceedings of MICCAI, 2015.

[17]

Senzhang Wang, Jiannong Cao, and S Yu Philip. Deep learning for spatio-temporal data mining: A survey. IEEE transactions on knowledge and data engineering, 34(8):3681--3700, 2022.

[18]

Bowen Du, Hao Peng, Senzhang Wang, Md Zakirul Alam Bhuiyan, Lihong Wang, Qiran Gong, Lin Liu, and Jing Li. Deep irregular convolutional residual lstm for urban traffic passenger flows prediction. IEEE Transactions on Intelligent Transportation Systems, 21(3):972--985, 2019.

[19]

Junbo Zhang, Yu Zheng, and Dekang Qi. Deep spatio-temporal residual networks for citywide crowd flows prediction. In Proceeding of AAAI, 2017.

[20]

Haoxing Lin, Rufan Bai, Weijia Jia, Xinyu Yang, and Yongjian You. Preserving dynamic attention for long-term spatial-temporal prediction. In Proceedings of KDD, 2020.

Digital Library

[21]

Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555, 2014.

Digital Library

[22]

Zheng Zhao, Weihai Chen, Xingming Wu, Peter CY Chen, and Jingmeng Liu. Lstm network: a deep learning approach for short-term traffic forecast. IET Intelligent Transport Systems, 2017.

[23]

Weiqi Chen, Ling Chen, Yu Xie, Wei Cao, Yusong Gao, and Xiaojie Feng. Multi-range attentive bicomponent graph convolutional network for traffic forecasting. In Proceeding of AAAI, 2020.

[24]

Lei Bai, Lina Yao, Can Li, Xianzhi Wang, and Can Wang. Adaptive graph convolutional recurrent network for traffic forecasting. In Proceeding of NeurIPS, 2020.

[25]

Prafulla Dhariwal and Alexander Nichol. Diffusion models beat gans on image synthesis. In Proceeding of NeurIPS, 2021.

[26]

Haoying Li, Yifan Yang, Meng Chang, Shiqi Chen, Huajun Feng, Zhihai Xu, Qi Li, and Yueting Chen. Srdiff: Single image super-resolution with diffusion probabilistic models. Neurocomputing, 2022.

[27]

Jacob Austin, Daniel D Johnson, Jonathan Ho, Daniel Tarlow, and Rianne van den Berg. Structured denoising diffusion models in discrete state-spaces. In Proceedings of NeurIPS, 2021.

[28]

Nanxin Chen, Yu Zhang, Heiga Zen, Ron J Weiss, Mohammad Norouzi, and William Chan. Wavegrad: Estimating gradients for waveform generation. arXiv preprint arXiv:2009.00713, 2020.

[29]

Yusuke Tashiro, Jiaming Song, Yang Song, and Stefano Ermon. Csdi: Conditional score-based diffusion models for probabilistic time series imputation. In Proceedings of NeurIPS, 2021.

[30]

Omri Avrahami, Dani Lischinski, and Ohad Fried. Blended diffusion for text-driven editing of natural images. In Proceeding of CVPR, 2022.

[31]

Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceeding of CVPR, 2022.

[32]

Bahjat Kawar, Roy Ganz, and Michael Elad. Enhancing diffusion-based image synthesis with robust classifier guidance. arXiv preprint arXiv:2208.08664, 2022.

[33]

Namrata Anand and Tudor Achim. Protein structure and sequence generation with equivariant denoising diffusion probabilistic models. arXiv preprint arXiv:2205.15019, 2022.

[34]

Shitong Luo, Yufeng Su, Xingang Peng, Sheng Wang, Jian Peng, and Jianzhu Ma. Antigen-specific antibody design and optimization with diffusion-based generative models. bioRxiv, 2022.

[35]

Hyungjin Chung, Byeongsu Sim, and Jong Chul Ye. Come-closer-diffuse-faster: Accelerating conditional diffusion models for inverse problems through stochastic contraction. In Proceeding of CVPR, 2022.

[36]

Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. In Proceedings of NeurIPS, 2020.

[37]

Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. In Proceedings of ICLR, 2021.

[38]

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceeding of CVPR, 2016.

Cited By

Zou XYan YHao XHu YWen HLiu EZhang JLi YLi TZheng YLiang Y(2025)Deep learning for cross-domain data fusion in urban computing: Taxonomy, advances, and outlookInformation Fusion10.1016/j.inffus.2024.102606113(102606)Online publication date: Jan-2025
https://doi.org/10.1016/j.inffus.2024.102606
Zheng YWu JCai ZWang SWang JSerra ESpezzano F(2024)AdaTM: Fine-grained Urban Flow Inference with Adaptive Knowledge Transfer across Multiple CitiesProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679856(3424-3432)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679856
Zheng YCai YCai ZFan CWang SWang JSerra ESpezzano F(2024)FGITrans: Cross-City Transformer for Fine-grained Urban Flow InferenceProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679855(3415-3423)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679855

Index Terms

DiffUFlow: Robust Fine-grained Urban Flow Inference with Denoising Diffusion Model
1. Information systems
  1. Information systems applications
    1. Spatial-temporal systems
      1. Location based services

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cover image ACM Conferences

CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

October 2023

5508 pages

ISBN:9798400701245

DOI:10.1145/3583780

General Chairs:
Ingo Frommholz
University of Wolverhampton, UK
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University of Koblenz, Germany
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University of Birmingham, UK
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University of Birmingham, UK
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Program Chairs:
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Spotify, UK
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Min Zhang
Tsinghua University, China
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Rodrygo Santos
Federal University of Minas Gerais, Brazil

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Published: 21 October 2023

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Hunan Provincial Natural Science Foundation of China
National Natural Science Foundation of China
Open Project of Xiangjiang Laboratory
Beijing Natural Science Foundation
Beijing Nova Program

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CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management

October 21 - 25, 2023

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Cited By

Zou XYan YHao XHu YWen HLiu EZhang JLi YLi TZheng YLiang Y(2025)Deep learning for cross-domain data fusion in urban computing: Taxonomy, advances, and outlookInformation Fusion10.1016/j.inffus.2024.102606113(102606)Online publication date: Jan-2025
https://doi.org/10.1016/j.inffus.2024.102606
Zheng YWu JCai ZWang SWang JSerra ESpezzano F(2024)AdaTM: Fine-grained Urban Flow Inference with Adaptive Knowledge Transfer across Multiple CitiesProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679856(3424-3432)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679856
Zheng YCai YCai ZFan CWang SWang JSerra ESpezzano F(2024)FGITrans: Cross-City Transformer for Fine-grained Urban Flow InferenceProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679855(3415-3423)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679855

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