Optimizing Traffic Flow in Smart Cities: A Simulation-based Approach Using IoT and AI Integration

Abstract

Traffic congestion remains a major challenge in smart cities, affecting economic productivity, environmental sustainability, and quality of life. Integrating IoT and AI offers a promising solution for optimizing traffic flow. However, existing methods often rely on static infrastructure and reactive systems, which fail to address dynamic and real-time challenges such as sudden congestion, irregular traffic patterns, and inefficient resource allocation. This paper proposes a Traffic Flow based on Artificial Intelligence (TF-AI) framework, leveraging IoT-enabled sensors and AI-driven predictive models to dynamically monitor and manage traffic. The framework utilizes machine learning algorithms for real-time data analysis and adaptive traffic signal optimization to mitigate congestion effectively. Simulation results demonstrate significant improvements in traffic flow efficiency, reduced vehicle idle time, and minimized carbon emissions. The proposed approach proves to be a scalable and robust solution for enhancing urban mobility and fostering sustainable smart city development.