Dynamic Traffic Density Prediction in Urban Areas Using Machine Learning Models: Comparative Analysis of Classification Performance - Mesut Ulu; Kılıç, Erdal (International Journal of Industrial Engineering: Theory, Applications and Practice, 2026)
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.