Fine-Grained Urban Flow Inference
4 papers with code • 4 benchmarks • 1 datasets
Fine-grained urban flow inference (FUFI) aims to infer the fine-grained urban flow map from the coarse-grained one.
TaxiBJ
Most implemented papers
UrbanFM: Inferring Fine-Grained Urban Flows
yoshall/UrbanFM
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In this paper, we aim to infer the real-time and fine-grained crowd flows throughout a city based on coarse-grained observations.
Fine-Grained Urban Flow Inference
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.
Fine-grained Urban Flow Inference with Incomplete Data
2023-MindSpore-1/ms-code-100
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IEEE Transactions on Knowledge and Data Engineering 2022
In this paper, we make the first attempt to infer fine-grained urban flows based on the incomplete coarse-grained urban flow observations, and propose a Multi-Task urban flow Completion and Super-Resolution network (MT-CSR for short) to simultaneously complete the coarse-grained urban flows and infer the fine-grained flows.
Spatial-Temporal Contrasting for Fine-Grained Urban Flow Inference
Xovee/stcf
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IEEE Transactions on Big Data 2023
Fine-grained urban flow inference (FUFI) problem aims to infer the fine-grained flow maps from coarse-grained ones, benefiting various smart-city applications by reducing electricity, maintenance, and operation costs.
