STF-GGRU Model Scalability Performance on PeMSD4 and PeMSD8 Datasets

Scalability performance data for the STF-GGRU traffic prediction model. The dataset likely contains results from evaluating the model on the PeMSD4 and PeMSD8 traffic datasets, showing RMSE values of 27.18 and 11.1 respectively. The data was authored by Karimeh Ibrahim Ata and last updated on April 28, 2026.

Use Cases

• Benchmarking new traffic prediction models based on reported RMSE performance metrics.
• Analyzing the scalability of graph-based neural architectures for transportation data.
• Comparing the performance of the STF-GGRU model against traditional methods like ARIMA, GRU, and LSTM.

Strengths

• Includes specific, reported model performance metrics (RMSE of 27.18 and 11.1).
• Released under a permissive CC-BY-4.0 license.
• Data is very recent, last updated in April 2026.

Limitations

• Dataset is extremely small (5.5 KB), indicating limited scope.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

figshare

Collection Method

Likely contains experimental results from model evaluation.

Time Range

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Freshness

Last updated 2026-04-28 17:37:46

Geography

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