Summary of Related Works on Traffic Flow Prediction Models

A summary of related works on traffic flow prediction for Intelligent Transportation Systems. The document, authored by Karimeh Ibrahim Ata, was last updated on April 28, 2026. It is a 9.5 KB Excel file containing a literature review and performance metrics for models like STF-GGRU, ARIMA, GRU, and LSTM.

Use Cases

• Benchmarking new traffic prediction models against reported RMSE values of 27.18 and 11.1.
• Reviewing the evolution of spatiotemporal feature alignment techniques in ITS literature.
• Comparing the performance of traditional methods (ARIMA) against advanced neural models (GRU, LSTM, STF-GGRU).

Strengths

• Provides specific performance metrics (RMSE values of 27.18 and 11.1) for model comparison.
• Released under a permissive CC-BY-4.0 license for reuse and distribution.
• File is small (9.5 KB), ensuring quick download and inspection.

Limitations

• Row count is unknown, which may limit suitability assessment for quantitative analysis.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is a summary document, not raw traffic data, limiting direct application in model training.

Provenance

Source

figshare

Collection Method

Likely compiled as a literature review for a research study.

Time Range

Publication date suggests content is current as of 2026.

Freshness

Last updated 2026-04-28 17:37:40; freshness should be verified.

Geography

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