Navigating Urban Traffic Patterns: Ensemble Stacking for Improved Travel Time Prediction

Efficient travel time prediction is crucial for effective transportation planning, traffic management, and urban mobility. In this study, we present a novel approach to enhance travel time prediction accuracy in urban environments using ensemble stacking techniques. Leveraging gradient boosting algorithms as base models, our methodology integrates diverse predictive models to capture complex traffic patterns and improve generalization performance. We conduct empirical evaluations using real-world data to demonstrate the effectiveness of our approach, highlighting its superiority over individual models. Our findings underscore the potential of ensemble stacking in addressing the challenges of travel time prediction in urban settings, paving the way for more reliable and efficient transportation systems.