XAI-Empowered Approach to Enhance Urban Traffic Flow

Urban traffic congestion is a widespread and challenging problem that city planners and citizens have to deal with. It results in longer travel times, greater pollution levels, and a reduction in monetary resources. This paper examines how explainable AI (XAI) techniques can be applied to improve urban traffic flow by utilizing a combination of the latest machine learning algorithms, data analysis techniques, and real-time information systems. We carry out this research by first reviewing the existing traffic management systems as well as their inefficiencies, and the paper then reviews recent AI-driven solutions developed for traffic optimization. These solutions are the ones that bring along the advantages of predictive modeling, adaptive traffic signal control, and smart routing applications. The success of these methods to derive efficient and less congested traffic patterns in addition to enhanced overall transportation systems are the reasons for the utilization of case studies conducted in various cities. Furthermore, we present the ethical issues and potential obstacles that the AI technology may face in urban settings. The findings show that the correct AI techniques applied in traffic management are the ideal solution to the transformation of urban mobility and the creation of smart and green cities. This study is conceived to substantially contribute to the smart city literature and also offer practical suggestions for decision-makers and city planners.