ARXIV:2606.09392 · UNCATEGORIZED · SUBMITTED 09 JUN · 03:25 UTC · FRESHNESS FRESH

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From Coarse to Fine: Managing Temporal Granularity in Spatio-Temporal Data for Fine-Grained Traffic Prediction

Shuhao Li · Weidong Yang · Yue Cui · Zizhuo Xu · Lipeng Ma · Fan Zhang · +1 at arXiv

ScienceToStartup currently rates this 0.0/10 on the public viability pass. STRP supports two practical prediction settings: window-based and duration-based, to handle different forms of granularity mismatch. Code availability is flagged…

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PROBLEM

Efficient acquisition, storage, and utilization of traffic data are critical challenges in spatio-temporal data management.

METHOD

Full abstract

Efficient acquisition, storage, and utilization of traffic data are critical challenges in spatio-temporal data management. Most traffic data systems collect and store observations at fixed, coarse-grained temporal intervals to reduce storage and computation costs. However, such coarse-grained data severely limits downstream applications that require predictions at a finer temporal granularity. Collecting and maintaining fine-grained traffic data across all locations and time periods would impose a substantial burden on database storage and preprocessing pipelines. To address this temporal granularity mismatch, we formulate a novel problem: predicting fine-grained future traffic using coarse-grained sampled data. We propose the Spatial-Temporal Refinement Predictor (STRP), a granularity-aware framework for spatio-temporal data systems. STRP integrates two components: Tree Convolution for efficient and interpretable spatial dependency modeling, and Inverse Dilated Convolution for progressive temporal extrapolation. STRP supports two practical prediction settings: window-based and duration-based, to handle different forms of granularity mismatch. Experiments on six benchmark datasets show that STRP significantly outperforms state-of-the-art baselines in both accuracy and efficiency. Our work offers a practical and interpretable approach to managing granularity mismatches in spatio-temporal traffic data systems.

RESULT

WHY NOW

Uncategorized moved forward this cycle; last verified June 2026. Public score 0.0/10. Production flags indicate code availability.

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From Coarse to Fine: Managing Temporal Granularity in Spatio-Temporal Data for Fine-Grained Traffic Prediction

Shuhao Li · Weidong Yang · Yue Cui · Zizhuo Xu · Lipeng Ma · Fan Zhang · +1 at arXiv

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From Coarse to Fine: Managing Temporal Granularity in Spatio-Temporal Data for Fine-Grained Traffic Prediction

From Coarse to Fine: Managing Temporal Granularity in Spatio-Temporal Data for Fine-Grained Traffic Prediction

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