Carbon Footprint Management Architecture in Urban Mobility Systems

The rapid urbanization and increasing reliance on private vehicles have led to significant carbon emissions and traffic congestion in urban areas. This study proposes a Multi-Layer Architecture for Carbon Emission Monitoring and Mitigation, integrating policy-driven interventions, AI-based route optimization, and public transport incentives to improve urban mobility sustainability. The proposed framework is validated through an iterative simulation-based approach using IDEF10, ensuring its effectiveness in reducing emissions, optimizing fuel consumption, and enhancing public transportation efficiency. Simulation results indicate a 49.22% reduction in carbon emissions, a 66.59% increase in public transport utilization, and a 17.47% reduction in traffic congestion. Fuel consumption decreased by 46.50%, while private vehicle dependency was reduced from 79.35% to 49.52%. These improvements demonstrate the effectiveness of integrating carbon taxation, electric vehicle subsidies, AI-based traffic management, and public transport incentives in achieving sustainable urban mobility. The findings emphasize the importance of iterative policy refinement, where targeted interventions are continuously evaluated and optimized to maximize their impact. The proposed framework provides a scalable and adaptable approach to managing carbon footprints in urban transportation systems. Future research should focus on real-world pilot implementations to further validate and refine this model across diverse metropolitan contexts.