Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
102 tokens/sec
GPT-4o
59 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
6 tokens/sec
GPT-4.1 Pro
50 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Towards Generative Modeling of Urban Flow through Knowledge-enhanced Denoising Diffusion (2309.10547v1)

Published 19 Sep 2023 in cs.AI and cs.SI

Abstract: Although generative AI has been successful in many areas, its ability to model geospatial data is still underexplored. Urban flow, a typical kind of geospatial data, is critical for a wide range of urban applications. Existing studies mostly focus on predictive modeling of urban flow that predicts the future flow based on historical flow data, which may be unavailable in data-sparse areas or newly planned regions. Some other studies aim to predict OD flow among regions but they fail to model dynamic changes of urban flow over time. In this work, we study a new problem of urban flow generation that generates dynamic urban flow for regions without historical flow data. To capture the effect of multiple factors on urban flow, such as region features and urban environment, we employ diffusion model to generate urban flow for regions under different conditions. We first construct an urban knowledge graph (UKG) to model the urban environment and relationships between regions, based on which we design a knowledge-enhanced spatio-temporal diffusion model (KSTDiff) to generate urban flow for each region. Specifically, to accurately generate urban flow for regions with different flow volumes, we design a novel diffusion process guided by a volume estimator, which is learnable and customized for each region. Moreover, we propose a knowledge-enhanced denoising network to capture the spatio-temporal dependencies of urban flow as well as the impact of urban environment in the denoising process. Extensive experiments on four real-world datasets validate the superiority of our model over state-of-the-art baselines in urban flow generation. Further in-depth studies demonstrate the utility of generated urban flow data and the ability of our model for long-term flow generation and urban flow prediction. Our code is released at: https://github.com/tsinghua-fib-lab/KSTDiff-Urban-flow-generation.

PDF HTML Abstract
Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Bookmark Chat Bubble Oval Streamline Icon: https://streamlinehq.com Chat (Pro)
Definition Search Book Streamline Icon: https://streamlinehq.com
References (49)
  1. Juan Miguel Lopez Alcaraz and Nils Strodthoff. 2023. Diffusion-based Conditional ECG Generation with Structured State Space Models. arXiv preprint arXiv:2301.08227 (2023).
  2. Towards a generic benchmarking platform for origin–destination flows estimation/updating algorithms: Design, demonstration and validation. Transportation Research Part C: Emerging Technologies 66 (2016), 79–98.
  3. Tucker: Tensor factorization for knowledge graph completion. arXiv preprint arXiv:1901.09590 (2019).
  4. A data-driven market simulator for small data environments. arXiv preprint arXiv:2006.14498 (2020).
  5. Real-valued (medical) time series generation with recurrent conditional gans. arXiv preprint arXiv:1706.02633 (2017).
  6. CARD: Classification and Regression Diffusion Models. In Thirty-Sixth Conference on Neural Information Processing Systems.
  7. MedDiff: Generating Electronic Health Records using Accelerated Denoising Diffusion Model. arXiv preprint arXiv:2302.04355 (2023).
  8. Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems 33 (2020), 6840–6851.
  9. Knowledge Enhanced GAN for IoT Traffic Generation. In Proceedings of the ACM Web Conference 2022. 3336–3346.
  10. Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction. arXiv preprint arXiv:2212.04475 (2022).
  11. Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey. arXiv preprint arXiv:2303.14483 (2023).
  12. Diffwave: A versatile diffusion model for audio synthesis. arXiv preprint arXiv:2009.09761 (2020).
  13. A Lightweight and Accurate Spatial-Temporal Transformer for Traffic Forecasting. IEEE Transactions on Knowledge and Data Engineering (2022).
  14. Deepstn+: Context-aware spatial-temporal neural network for crowd flow prediction in metropolis. In Proceedings of the AAAI conference on artificial intelligence, Vol. 33. 1020–1027.
  15. Using gans for sharing networked time series data: Challenges, initial promise, and open questions. In Proceedings of the ACM Internet Measurement Conference. 464–483.
  16. Improving Location Recommendation with Urban Knowledge Graph. arXiv preprint arXiv:2111.01013 (2021).
  17. Msdr: Multi-step dependency relation networks for spatial temporal forecasting. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 1042–1050.
  18. UrbanKG: An Urban Knowledge Graph System. ACM Transactions on Intelligent Systems and Technology (2023).
  19. Knowledge-driven site selection via urban knowledge graph. arXiv preprint arXiv:2111.00787 (2021).
  20. Developing knowledge graph based system for urban computing. In Proceedings of the 1st ACM SIGSPATIAL International Workshop on Geospatial Knowledge Graphs. 3–7.
  21. Learning geo-contextual embeddings for commuting flow prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 34. 808–816.
  22. Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps. arXiv preprint arXiv:2206.00927 (2022).
  23. Towards a foundation model for geospatial artificial intelligence (vision paper). In Proceedings of the 30th International Conference on Advances in Geographic Information Systems. 1–4.
  24. Mehdi Mirza and Simon Osindero. 2014. Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014).
  25. Wavenet: A generative model for raw audio. arXiv preprint arXiv:1609.03499 (2016).
  26. OpenAI. 2023. GPT-4 Technical Report. arXiv:2303.08774 [cs.CL]
  27. Autoregressive denoising diffusion models for multivariate probabilistic time series forecasting. In International Conference on Machine Learning. PMLR, 8857–8868.
  28. Jiangtao Ren and Qiwei Xie. 2017. Efficient od trip matrix prediction based on tensor decomposition. In 2017 18th IEEE International Conference on Mobile Data Management (MDM). IEEE, 180–185.
  29. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 10684–10695.
  30. Modeling relational data with graph convolutional networks. In The Semantic Web: 15th International Conference, ESWC 2018, Heraklion, Crete, Greece, June 3–7, 2018, Proceedings 15. Springer, 593–607.
  31. A deep gravity model for mobility flows generation. Nature communications 12, 1 (2021), 6576.
  32. CSDI: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34 (2021), 24804–24816.
  33. Ledyard R Tucker. 1966. Some mathematical notes on three-mode factor analysis. Psychometrika 31, 3 (1966), 279–311.
  34. Attention is all you need. Advances in neural information processing systems 30 (2017).
  35. Spatio-Temporal Urban Knowledge Graph Enabled Mobility Prediction. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, 4 (2021), 1–24.
  36. Generative-Free Urban Flow Imputation. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 2028–2037.
  37. Sheng Wei and Lei Wang. 2020. Examining the population flow network in China and its implications for epidemic control based on Baidu migration data. Humanities and Social Sciences Communications 7, 1 (2020).
  38. DiffSTG: Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion Models. arXiv preprint arXiv:2301.13629 (2023).
  39. 3dgcn: 3-dimensional dynamic graph convolutional network for citywide crowd flow prediction. ACM Transactions on Knowledge Discovery from Data (TKDD) 15, 6 (2021), 1–21.
  40. Multisize patched spatial-temporal transformer network for short-and long-term crowd flow prediction. IEEE Transactions on Intelligent Transportation Systems 23, 11 (2022), 21548–21568.
  41. Context-aware real-time population estimation for metropolis. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. 1064–1075.
  42. Coupled layer-wise graph convolution for transportation demand prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 4617–4625.
  43. Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. In Proceedings of the 27th International Joint Conference on Artificial Intelligence. 3634–3640.
  44. EHRDiff: Exploring Realistic EHR Synthesis with Diffusion Models. arXiv preprint arXiv:2303.05656 (2023).
  45. Spatio-temporal Diffusion Point Processes. arXiv preprint arXiv:2305.12403 (2023).
  46. Deep spatio-temporal residual networks for citywide crowd flows prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 31.
  47. Curb-GAN: Conditional urban traffic estimation through spatio-temporal generative adversarial networks. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 842–852.
  48. Hierarchical Knowledge Graph Learning Enabled Socioeconomic Indicator Prediction in Location-Based Social Network. In Proceedings of the ACM Web Conference 2023. 122–132.
  49. George Kingsley Zipf. 1946. The P 1 P 2/D hypothesis: on the intercity movement of persons. American sociological review 11, 6 (1946), 677–686.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (5)
  1. Zhilun Zhou (8 papers)
  2. Jingtao Ding (50 papers)
  3. Yu Liu (786 papers)
  4. Depeng Jin (72 papers)
  5. Yong Li (628 papers)
Citations (14)
View on Semantic Scholar