<![CDATA[Road traffic congestion remains a persistent and critical challenge in modern urban environments, adversely affecting travel times, fuel consumption, air quality, and overall urban livability. To address this issue, this study proposes a hybrid ensemble learning framework for accurate short-term traffic flow prediction across signalized urban intersections. The model integrates Random Forest, Gradient Boosting, and Multi-Layer Perceptron within a weighted voting ensemble mechanism, wherein model contributions are dynamically scaled based on individual validation performance. Benchmarking is performed against traditional and advanced baselines, including Linear Regression, Support Vector Regression, and Long Short-Term Memory (LSTM) networks. A real-world traffic dataset, comprising 56 consecutive days of readings from six intersections, is utilized to validate the approach. A robust preprocessing pipeline is implemented, encompassing anomaly detection, temporal feature engineering especially time-of-day and day-of-week normalization, and sliding window encoding to preserve temporal dependencies. Experimental evaluations on 4-intersection and 6-intersection scenarios reveal that the ensemble consistently outperforms all baselines, achieving a peak R² of 0.954 and an RMSE of 0.045. Statistical significance testing using Welch’s t-test confirms the reliability of these improvements. Furthermore, SHAP-based interpretability analysis reveals the dominant influence of temporal features during high-variance periods. While computational overhead and data sparsity during rare events remain limitations, the framework demonstrates strong applicability for deployment in smart traffic systems. Its predictive accuracy and model transparency make it a viable candidate for adaptive signal control, congestion mitigation, and urban mobility planning. Future work may explore real-time streaming adaptation, external event integration, and generalization across heterogeneous urban networks.]]>
