STRP is a granularity-aware model that predicts fine-grained spatio-temporal traffic from coarse inputs via tree convolution and inverse dilated convolution, outperforming baselines on six datasets in window-based and duration-based settings.
Stg4traffic: A survey and bench- mark of spatial-temporal graph neural networks for traffic prediction,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Systematic experiments on four traffic datasets find that a 1-block STGCN achieves optimal short-term (10 min) prediction on three datasets with only marginal longer-horizon degradation and 61% lower CPU latency than the standard 2-block model.
citing papers explorer
-
From Coarse to Fine: Managing Temporal Granularity in Spatio-Temporal Data for Fine-Grained Traffic Prediction
STRP is a granularity-aware model that predicts fine-grained spatio-temporal traffic from coarse inputs via tree convolution and inverse dilated convolution, outperforming baselines on six datasets in window-based and duration-based settings.
-
Efficient Traffic Prediction at Scale: A Systematic Study of STGCN Architectural Depth
Systematic experiments on four traffic datasets find that a 1-block STGCN achieves optimal short-term (10 min) prediction on three datasets with only marginal longer-horizon degradation and 61% lower CPU latency than the standard 2-block model.