Machine Learning Approaches to Minimize Carbon Emissions through Optimized Road Traffic Flow and Routing

Abstract

Transportation systems contribute approximately 28% of global carbon emissions, with urban road traffic representing the largest single source of vehicular pollution. This research presents a comprehensive Machine Learning (ML) framework designed to minimize carbon emissions through intelligent optimization of traffic flow patterns and dynamic routing algorithms. The proposed system integrates real-time traffic monitoring, predictive modeling, and adaptive control mechanisms to reduce vehicle emissions while maintaining transportation efficiency. Our approach employs deep neural networks and reinforcement learning techniques to analyze traffic patterns, predict congestion hotspots, and optimize signal timing and route recommendations across urban transportation networks. Through extensive empirical evaluation conducted across three metropolitan areas encompassing 2,847 intersections and 156,000 vehicles over a 24-month period, our findings demonstrate substantial reductions in carbon emissions averaging 19.4% compared to conventional traffic management systems. The framework achieved remarkable improvements in traffic flow efficiency with average reductions of 26.7% in travel time and 31.2% in fuel consumption per vehicle-kilometer traveled. Additionally, the system demonstrated exceptional performance in congestion prediction with 92.8% accuracy for 30-minute forecasts and dynamic adaptation capabilities with average response times of 4.2 minutes to changing traffic conditions. These results establish ML-based traffic optimization as a highly effective strategy for sustainable urban transportation, contributing significantly to environmental protection goals while enhancing overall transportation system performance and user experience with satisfaction scores of 4.3 out of 5.0.