Integrating Deep Learning And Ensemble Methods For Urban Traffic Forecasting

Abstract

Accurate urban traffic forecasting is essential for intelligent transportation systems, enabling efficient traffic control, route optimization, and congestion mitigation. This study presents a hybrid ensemble-based framework that integrates deep learning and machine learning algorithms to enhance the prediction of urban traffic flow. The proposed approach utilizes multiple ensemble techniques, including Bagging with Logistic Regression, Gradient Boosting using XGBoost and LightGBM, and Stacking with Multi-Layer Perceptron (MLP) and Support Vector Classifier (SVC) as base learners. The models were trained and tested on real-life traffic video data, which was processed to extract frame-wise vehicle attributes such as object coordinates, bounding box sizes, and vehicle types. Additional temporal and contextual features were incorporated to improve the robustness of the forecasting model. Experimental evaluations were conducted using performance metrics, including the Coefficient of Determination (R²), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). Results indicate that the stacking ensemble method combining MLP and SVC outperformed traditional single-model approaches, demonstrating superior accuracy and stability across dynamic traffic scenarios. The findings confirm that combining ensemble learning with deep neural networks significantly enhances urban traffic forecasting performance and provides scalable solutions for real-world intelligent transportation systems.