Economic Evaluation of Smart Traffic Management Systems in Reducing Carbon Emissions

Abstract

This study evaluates the economic and environmental implications of implementing smart traffic management systems in urban areas. Leveraging advanced technologies such as machine learning, artificial intelligence, and the Internet of Things, these systems aim to optimize traffic flow and reduce carbon emissions. The methodology combines economic modeling with case study analysis to assess costs, benefits, and real-world outcomes. Findings reveal significant reductions in fuel consumption, travel times, and greenhouse gas emissions across diverse urban contexts. Despite initial investment challenges, positive net present values and high benefit-cost ratios underscore the financial viability of these systems. The study concludes that supportive policies, collaborative governance, and continued research are essential for successful deployment and integration. Recommendations include investment in infrastructure, supportive policies, public awareness campaigns, and addressing technical challenges. By following these recommendations, cities can maximize the economic and environmental benefits of smart traffic management systems, contributing to more sustainable and efficient urban transportation networks globally.