Machine Learning for Sustainable Transportation and Traffic Flow Optimization

Abstract

Traffic congestion and transportation inefficiencies contribute significantly to carbon emissions and urban air pollution. This paper explores machine learning solutions for sustainable mobility, including AI-driven traffic forecasting, intelligent public transport scheduling, and adaptive signal control systems. Reinforcement learning, deep neural networks, and edge computing are applied to optimize traffic flow, reduce fuel consumption, and improve commuter experiences. The study presents real-world implementations of AI-driven smart transportation systems, emphasizing their role in reducing emissions and enhancing urban sustainability.