Meteorological and Temporal Dynamics in Urban Traffic Density: A Comparative Evaluation of Machine Learning and Neural Network Models

Year 2025, Volume: 9 Issue: 2, 611 - 629, 31.12.2025

Busra Ozdenizci Kose , Farid Huseynov , Vildan Gülpınar Demirci , Nurullah Taş

https://doi.org/10.26650/acin.1627153

https://izlik.org/JA32LS84BL

Abstract

Understanding the interplay between meteorological and temporal factors and urban traffic density is vital for effective traffic management and sustainable urban planning. This study explores these dynamics using a dataset from Istanbul, integrating traffic metrics with weather variables as temperature, dew point, wind speed, and precipitation, alongside temporal indicators, such as time of day and weekday/weekend distinctions. A multi-model approach that combines traditional regression techniques, advanced ensemble models, and neural networks was applied. Ridge and Lasso Regression provided baseline comparisons, whereas Decision Tree, KNN Regression, and Random Forest captured nonlinear relationships. Advanced ensemble models, such as LightGBM and XGBoost, employ boosting techniques to enhance accuracy. A feedforward neural network complementing ensemble methods further analyzed intricate data patterns. Performance evaluation based on MSE, MAE, RMSE, and R² Scores highlighted the superiority of LightGBM and Random Forest, which achieved the highest accuracy. Feature importance analysis revealed traffic-specific metrics, such as average speed, as the most significant predictors, followed by meteorological variables, such as temperature and pressure. Temporal factors, including morning and working hours, also played a crucial role in shaping traffic density. The results confirm the significant influence of weather and temporal variables on traffic density and validate the effectiveness of advanced ensemble models and neural networks in predictive traffic modeling. By focusing on Istanbul, this study highlights the value of region-specific approaches and provides a foundation for developing data-driven traffic management strategies. Future research can build on these findings by expanding the scope of the dataset and incorporating dynamic interactions to further improve prediction accuracy and applicability.

Keywords

Urban Traffic Density , Meteorological Factors , Temporal Patterns , Machine Learning , Ensemble Models , Boosting Techniques , Neural Networks

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