Dynamic Traffic Density Prediction in Urban Areas Using Machine Learning Models: Comparative Analysis of Classification Performance
DOI:
https://doi.org/10.23055/ijietap.2026.33.2.11429Keywords:
Traffic density, random forest, LGBM, XGBoost, prediction, classification performanceAbstract
Accurate traffic density prediction is essential for reducing congestion, saving time and fuel, lowering emissions, and improving urban mobility. It also supports decision-making for drivers and traffic management authorities. Various dynamic factors influence traffic density and rely heavily on both historical and real-time data. This study explores the application of machine learning (ML) techniques in dynamic traffic density prediction and introduces a data-driven model. Three ML algorithms—Random Forest (RF), Light Gradient Boosting Machine (LGBM), and Extreme Gradient Boosting (XGBoost)—were evaluated using a real-world dataset. The RF algorithm demonstrated superior performance, especially in accurately classifying different levels of traffic congestion. The model's performance was assessed using comprehensive metrics beyond standard accuracy, including F1-score, Matthews Correlation Coefficient, and AUC. These findings highlight the potential of ML-based classification models to enhance traffic forecasting and support intelligent transportation systems in urban environments.
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