Overview of Traffic Flow Forecasting Techniques - Annarita Carianni; Andrea Gemma (IEEE Open Journal of Intelligent Transportation Systems)
Forecasting traffic conditions is critical for modern mobility management. With urbanization and motorization rates rising globally, accurate traffic flow prediction plays a vital role in mitigating congestion, optimizing traffic strategies, and reducing environmental impacts. This paper provides a comprehensive review of traffic forecasting methods, bridging traditional techniques and innovative approaches driven by computational intelligence and abundant data. The study classifies forecasting methods into four categories: naïve techniques, parametric methods, simulation-based approaches, and nonparametric models such as machine learning and deep learning. Each category is analyzed for its historical development, theoretical foundations, and practical applications, with special emphasis on artificial intelligence’s transformative role in enabling dynamic and accurate predictions. The review evaluates traditional models like ARIMA and Kalman filters, alongside nonparametric techniques such as neural networks, and explores hybrid approaches that integrate multiple forecasting methods. It also assesses the complementary role of traffic simulation, from macroscopic to microscopic scales, in capturing complex traffic dynamics. The methodology synthesizes insights from foundational works and recent influential studies, examining metrics for prediction accuracy and identifying contextual factors shaping method effectiveness. The paper highlights strengths, limitations, and opportunities for advancement across forecasting approaches. Concluding with a forward-looking perspective, the review underscores trends such as spatiotemporal modeling and real-time data integration, which promise smarter, more adaptive traffic management solutions. This survey serves as a valuable resource for researchers, policymakers, and practitioners in navigating the evolving field of traffic flow forecasting.