From Coarse to Fine: Managing Temporal Granularity in Spatio-Temporal Data for Fine-Grained Traffic Prediction
2026-06-08 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors address the problem of predicting detailed future traffic data when only less-detailed, coarse traffic data is available. They created a new method called STRP, which uses special techniques to better understand how traffic changes over time and space. STRP can make accurate and efficient fine-scale traffic predictions without needing to store huge amounts of detailed data. Their tests show STRP works better than existing methods on multiple datasets. This helps traffic systems manage data with different time resolutions more practically.
spatio-temporal datatraffic predictiontemporal granularitycoarse-grained datafine-grained dataTree ConvolutionInverse Dilated Convolutiondata extrapolationspatial dependencytraffic data systems
Authors
Shuhao Li, Weidong Yang, Yue Cui, Zizhuo Xu, Lipeng Ma, Fan Zhang, Xiaofang Zhou
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.