A Systematic Review on Data-Driven Traffic Management for Sustainable Urban Transport

Abstract

The rapid growth of urban areas has intensified challenges in traffic management, including congestion, air pollution, and high energy consumption. To address these issues, cities must adopt sustainable transport solutions that balance environmental, economic, and social factors while leveraging data-driven innovations. This review examines recent studies on optimizing traffic management through artificial intelligence and machine learning techniques. By applying a structured search strategy and strict inclusion criteria, we synthesize key findings from relevant academic sources. The results indicate that AI-driven approaches can significantly improve traffic flow, reduce congestion, and enhance transportation efficiency. Techniques such as machine learning and deep reinforcement learning show strong potential in predicting traffic patterns and optimizing signal control systems. However, challenges remain, particularly in ensuring data quality, integrating diverse data sources, processing information in real time, and scaling these solutions effectively. While data-driven traffic management strategies are promising, further research is needed to develop robust integration frameworks, refine scalable AI models, and enhance real-time analytics. Additionally, a deeper assessment of long-term sustainability impacts will be crucial in shaping the future of intelligent urban traffic management. This study provides a foundation for future research aimed at optimizing urban mobility through advanced data-driven methodologies.