# UrbanFM: Inferring Fine-Grained Urban Flows

arXiv: https://arxiv.org/abs/1902.05377 · HTML: https://mlsanity.com/paper/1902.05377/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2019.

## Built on by (most cited first, 30 of 31)

- [UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Prediction](https://mlsanity.com/paper/2402.11838/) (2024)
- [UrbanGPT: Spatio-Temporal Large Language Models](https://mlsanity.com/paper/2403.00813/) (2024)
- [Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network](https://mlsanity.com/paper/2110.04038/) (2021)
- [Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Prediction](https://mlsanity.com/paper/2204.08587/) (2022)
- [Deep Learning for Cross-Domain Data Fusion in Urban Computing: Taxonomy, Advances, and Outlook](https://mlsanity.com/paper/2402.19348/) (2024)
- [Trajectory Data Management and Mining: A Survey from Deep Learning to the LLM Era](https://mlsanity.com/paper/2403.14151/) (2024)
- [Graph Neural Processes for Spatio-Temporal Extrapolation](https://mlsanity.com/paper/2305.18719/) (2023)
- [KITS: Inductive Spatio-Temporal Kriging with Increment Training Strategy](https://mlsanity.com/paper/2311.02565/) (2023)
- [Earthfarseer: Versatile Spatio-Temporal Dynamical Systems Modeling in One Model](https://mlsanity.com/paper/2312.08403/) (2023)
- [Road Network Representation Learning: A Dual Graph based Approach](https://mlsanity.com/paper/2304.07298/) (2023)
- [Efficient Traffic Prediction Through Spatio-Temporal Distillation](https://mlsanity.com/paper/2501.10459/) (2025)
- [Spatio-Temporal Meta Contrastive Learning](https://mlsanity.com/paper/2310.17678/) (2023)
- [Spatiotemporal Implicit Neural Representation as a Generalized Traffic Data Learner](https://mlsanity.com/paper/2405.03185/) (2024)
- [UniFlow: A Foundation Model for Unified Urban Spatio-Temporal Flow Prediction](https://mlsanity.com/paper/2411.12972/) (2024)
- [Road Network Guided Fine-Grained Urban Traffic Flow Inference](https://mlsanity.com/paper/2109.14251/) (2021)
- [Grab the Reins of Crowds: Estimating the Effects of Crowd Movement Guidance Using Causal Inference](https://mlsanity.com/paper/2102.03980/) (2021)
- [REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting](https://mlsanity.com/paper/2411.14046/) (2024)
- [Fine-Grained Traffic Inference from Road to Lane via Spatio-Temporal Graph Node Generation](https://mlsanity.com/paper/2507.19089/) (2025)
- [One-shot Transfer Learning for Population Mapping](https://mlsanity.com/paper/2108.06228/) (2021)
- [Periodic Residual Learning for Crowd Flow Forecasting](https://mlsanity.com/paper/2112.06132/) (2021)
- [Spatio-Temporal Contrastive Self-Supervised Learning for POI-level Crowd Flow Inference](https://mlsanity.com/paper/2309.03239/) (2023)
- [Test-Time Compensated Representation Learning for Extreme Traffic Forecasting](https://mlsanity.com/paper/2309.09074/) (2023)
- [Fine-Grained Urban Flow Inference with Multi-scale Representation Learning](https://mlsanity.com/paper/2406.09710/) (2024)
- [STContext: A Multifaceted Dataset for Developing Context-aware Spatio-temporal Crowd Mobility Prediction Models](https://mlsanity.com/paper/2501.03583/) (2025)
- [FlowNet: Modeling Dynamic Spatio-Temporal Systems via Flow Propagation](https://mlsanity.com/paper/2511.05595/) (2025)
- [PASTA: PArallel Spatio-Temporal Attention with spatial auto-correlation gating for fine-grained crowd flow prediction](https://mlsanity.com/paper/2310.02284/) (2023)
- [From Incomplete Coarse-Grained to Complete Fine-Grained: A Two-Stage Framework for Spatiotemporal Data Reconstruction](https://mlsanity.com/paper/2410.05323/) (2024)
- [Leakage-Free Evaluation and Distribution-Robust Spatio-Temporal Graph Learning for Inductive Kriging](https://mlsanity.com/paper/2509.23631/) (2025)
- [Boosting Fine-Grained Urban Flow Inference via Lightweight Architecture and Focalized Optimization](https://mlsanity.com/paper/2511.07098/) (2025)
- [A Hybrid Inductive-Transductive Network for Traffic Flow Imputation on Unsampled Locations](https://mlsanity.com/paper/2512.17984/) (2025)

## Abstract (arXiv)

Urban flow monitoring systems play important roles in smart city efforts around the world. However, the ubiquitous deployment of monitoring devices, such as CCTVs, induces a long-lasting and enormous cost for maintenance and operation. This suggests the need for a technology that can reduce the number of deployed devices, while preventing the degeneration of data accuracy and granularity. In this paper, we aim to infer the real-time and fine-grained crowd flows throughout a city based on coarse-grained observations. This task is challenging due to two reasons: the spatial correlations between coarse- and fine-grained urban flows, and the complexities of external impacts. To tackle these issues, we develop a method entitled UrbanFM based on deep neural networks. Our model consists of two major parts: 1) an inference network to generate fine-grained flow distributions from coarse-grained inputs by using a feature extraction module and a novel distributional upsampling module; 2) a general fusion subnet to further boost the performance by considering the influences of different external factors. Extensive experiments on two real-world datasets, namely TaxiBJ and HappyValley, validate the effectiveness and efficiency of our method compared to seven baselines, demonstrating the state-of-the-art performance of our approach on the fine-grained urban flow inference problem.