# Fine-Grained Urban Flow Inference

arXiv: https://arxiv.org/abs/2002.02318 · HTML: https://mlsanity.com/paper/2002.02318/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2020.

## Built on by (most cited first, 10 of 10)

- [Deep Learning for Cross-Domain Data Fusion in Urban Computing: Taxonomy, Advances, and Outlook](https://mlsanity.com/paper/2402.19348/) (2024)
- [FDTI: Fine-grained Deep Traffic Inference with Roadnet-enriched Graph](https://mlsanity.com/paper/2306.10945/) (2023)
- [Road Network Guided Fine-Grained Urban Traffic Flow Inference](https://mlsanity.com/paper/2109.14251/) (2021)
- [Fine-Grained Traffic Inference from Road to Lane via Spatio-Temporal Graph Node Generation](https://mlsanity.com/paper/2507.19089/) (2025)
- [GraphSparseNet: a Novel Method for Large Scale Traffic Flow Prediction](https://mlsanity.com/paper/2502.19823/) (2025)
- [Periodic Residual Learning for Crowd Flow Forecasting](https://mlsanity.com/paper/2112.06132/) (2021)
- [SARN: Structurally-Aware Recurrent Network for Spatio-Temporal Disaggregation](https://mlsanity.com/paper/2306.07292/) (2023)
- [Fine-Grained Urban Flow Inference with Multi-scale Representation Learning](https://mlsanity.com/paper/2406.09710/) (2024)
- [From Incomplete Coarse-Grained to Complete Fine-Grained: A Two-Stage Framework for Spatiotemporal Data Reconstruction](https://mlsanity.com/paper/2410.05323/) (2024)
- [Boosting Fine-Grained Urban Flow Inference via Lightweight Architecture and Focalized Optimization](https://mlsanity.com/paper/2511.07098/) (2025)

## Abstract (arXiv)

The ubiquitous deployment of monitoring devices in urban flow monitoring systems induces a significant cost for maintenance and operation. A technique is required to reduce the number of deployed devices, while preventing the degeneration of data accuracy and granularity. In this paper, we present an approach for inferring the real-time and fine-grained crowd flows throughout a city based on coarse-grained observations. This task exhibits two challenges: the spatial correlations between coarse- and fine-grained urban flows, and the complexities of external impacts. To tackle these issues, we develop a model entitled UrbanFM which consists of two major parts: 1) an inference network to generate fine-grained flow distributions from coarse-grained inputs that uses a feature extraction module and a novel distributional upsampling module; 2) a general fusion subnet to further boost the performance by considering the influence of different external factors. This structure provides outstanding effectiveness and efficiency for small scale upsampling. However, the single-pass upsampling used by UrbanFM is insufficient at higher upscaling rates. Therefore, we further present UrbanPy, a cascading model for progressive inference of fine-grained urban flows by decomposing the original tasks into multiple subtasks. Compared to UrbanFM, such an enhanced structure demonstrates favorable performance for larger-scale inference tasks.