SIMULATION-BASED ON OPTIMIZATION OF TRAFFIC MANAGEMENT STRATEGIES FOR EMISSION REDUCION: INSIGHT FROM SUMO ANALYSIS

This study explores how mixed traffic affects vehicle emissions in an urban setting. The work focuses on a busy corridor where motorcycles, tricycles, cars, and a few heavy vehicles move together every day. Field data were collected through a ten-hour manual count, and a microscopic simulation was developed with the SUMO model. The aim was to understand how traffic volume, vehicle type, and changing speeds shape CO₂ emissions under real and simulated conditions. The traffic count showed that motorcycles and tricycles dominated the flow. Cars followed, while buses and trucks appeared in small numbers. Peak activity occurred between late morning and mid-afternoon. CO₂ emissions followed this pattern. Field measurements recorded a low of 142.6 g in the early morning and a peak of 215.3 g between 3:00 pm and 4:00 pm. These changes reflected higher traffic pressure and more stop-and-go movement. The SUMO simulation produced higher values than the field readings. The model estimated a CO₂ peak of 8,980.07 g around midday. This difference suggests that model assumptions, emission factors, or driving cycles may be more aggressive than actual local behavior. It also highlights the need for local calibration of emission models the study shows that CO₂ emissions rise sharply with traffic density and irregular movement. Small commercial vehicles, especially tricycles and cars, played the largest role in overall emissions. The results point to the need for better traffic control, cleaner vehicle technologies, and stronger public transport options. These steps can help reduce emissions and support healthier urban mobility. Keyword: carbon dioxide (Co2), Vehicle Emissions, SUMO Simulation and Urban Transport.