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Eight machine learning models, including Deep LSTM and Random Forest Regressor, were trained on traffic, weather, and event data from 2017 to 2023 to predict flow on Kandovan Road. The Random Forest Regressor achieved the highest accuracy with an R² score of 0.88. The work resulted in a traffic forecasting software system for real-time visualization and decision support.
File format is XLS (Excel), which may require specific software to open. License is CC-BY-4.0, requiring attribution.