New York City faces significant challenges in reducing carbon emissions caused by traffic congestion, inefficient routing, and vehicle idling. To address this, a data-driven traffic management solution powered by real-time analytics and Quantum Computing (QC) is proposed. The system integrates live traffic data, predictive analytics, and adaptive signal control to optimize vehicle routing, reduce congestion, and minimize emissions. QC algorithms, such as Quantum Annealing and Quantum Machine Learning, dynamically adjust traffic signals and reroute vehicles to prevent bottlenecks and lower fuel consumption. By leveraging cloud-based infrastructure, the solution ensures scalability and security, enabling expansion across urban areas.

The benefits include a potential reduction in commute times by 15-20%, a potential carbon emission decrease by 10-15%, and improved emergency response efficiency. This transformative approach not only enhances urban mobility but also promotes sustainability, public safety, and long-term resilience, positioning New York City as a model for smart, eco-friendly urban transportation.