EVALUATING THE ENVIRONMENTAL IMPACT OF TRAFFIC FLOW SCENARIOS USING SUMO

This study examines how different traffic-flow conditions affect environmental emissions in an urban road network. The goal is to understand how changes in vehicle movement can reduce CO₂, NOₓ, CO, and fuel consumption. A microscopic simulation approach was used, and the SUMO platform served as the main tool for modelling traffic behavior and estimating emissions. A road network was created, and several scenarios were tested. These included normal flow, heavy congestion, and improved signal timing. Lane-management and flow-control strategies were also evaluated to see how they influence traffic performance. The results show clear differences across scenarios. Congestion produced the highest emissions. Vehicles spent more time idling and accelerating, which increased CO₂ and fuel consumption significantly. In contrast, the optimized signal-timing scenario recorded smoother flow and lower emissions. Vehicles maintained steadier speeds and spent less time stopping. This led to cleaner output values and better overall efficiency. Scenarios with controlled lane usage also showed improvements, although not as strong as signal optimization. The findings highlight the strong link between traffic management and environmental performance. Small changes in flow control can create measurable reductions in emissions. This shows that cities can lower pollution levels even without major infrastructure expansion. The study also confirms that SUMO is a useful tool for evaluating future mobility strategies and testing policy options before real-world implementation. Overall, the research demonstrates that improved traffic coordination, better lane discipline, and reduced congestion can contribute to cleaner and more efficient urban transport.