Papers › Spatial-Temporal Contrasting for Fine-Grained Urban Flow Inference
Spatial-Temporal Contrasting for Fine-Grained Urban Flow Inference
Xovee Xu, Zhiyuan Wang, Qiang Gao, Ting Zhong, Bei Hui, Fan Zhou, Goce Trajcevski
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. Existing models use techniques from image super-resolution and achieve good performance in FUFI. However, they often rely on supervised learning with a large amount of training data, and often lack generalization capability and face overfitting. We present a new solution: S patial- T emporal C ontrasting for Fine-Grained Urban F low Inference (STCF). It consists of (i) two pre-training networks for spatial-temporal contrasting between flow maps; and (ii) one coupled fine-tuning network for fusing learned features. By attracting spatial-temporally similar flow maps while distancing dissimilar ones within the representation space, STCF enhances efficiency and performance. Comprehensive experiments on two large-scale, real-world urban flow datasets reveal that STCF reduces inference error by up to 13.5%, requiring significantly fewer data and model parameters than prior arts.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Fine-Grained Urban Flow Inference | TaxiBJ-P1 | STCF | MSE | 14.9232 | #1 of 9 | Archive leaderboard | report |
| Fine-Grained Urban Flow Inference | TaxiBJ-P2 | STCF | MSE | 18.2566 | #1 of 3 | Archive leaderboard | report |
| Fine-Grained Urban Flow Inference | TaxiBJ-P3 | STCF | MSE | 19.4153 | #1 of 2 | Archive leaderboard | report |
| Fine-Grained Urban Flow Inference | TaxiBJ-P4 | STCF | MSE | 11.7718 | #1 of 3 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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