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Abstract

Traffic flow prediction is crucial for smart transportation systems, as it plays a key role in improving traffic management and planning infrastructure. While many machine learning techniques have been used for this purpose, ensemble methods have proven to be especially effective because they enhance prediction accuracy by combining the strengths of multiple models. This paper offers a detailed overview of how ensemble methods are applied to traffic flow prediction. We start by exploring the basics of traffic flow prediction, including common data sources, types, and performance metrics. Then, we categorize ensemble methods into bagging, boosting, and hybrid approaches, reviewing important studies that show how these methods work, the datasets they use, and their performance results. Real-world examples and case studies are included to highlight the practical effectiveness of these methods in various traffic situations. Finally, we discuss the current challenges and suggest future research directions, aiming to provide a valuable resource for researchers and practitioners interested in improving traffic flow prediction with ensemble techniques.