COMPARATIVE ANALYSIS OF VEHICLE EMISSION PROFILE IN MIXED TRAFIC COMDITION A SUMO BASES STUDY

This study examines how mixed traffic conditions influence vehicle emissions in a growing urban area. The research uses the Simulation of Urban Mobility (SUMO) to model the flow and behavior of different vehicle types. The goal is to understand how traffic composition, speed changes, and density shape CO₂ output across daily travel periods. Field traffic counts from Gashua Road showed a system dominated by motorcycles and tricycles, with cars as the next major group. Heavy vehicles were few. Traffic volume peaked between late morning and early afternoon. These patterns shaped the emissions recorded on the corridor. The simulation used a real road network extracted from OpenStreetMap and a set of defined vehicle classes based on realistic parameters. Emission estimates were generated with the HBEFA model. The results show a clear trend. As traffic density increased, CO₂ emissions rose sharply. Cars and tricycles produced the highest CO₂ output in both the field and simulation stages. Motorcycles contributed less per unit but remained important because of their large numbers. Field CO₂ reached a maximum of 215.3 g during the afternoon peak. SUMO values were higher, with a simulated peak of 8,980.1 g around midday. The gap suggests that model assumptions, driving cycles, or emission factors may differ from local real-world patterns. The study concludes that mixed traffic, stop-and-go driving, and modal composition strongly influence emission levels. SUMO proved useful for testing scenarios and understanding emission behavior. The findings support the need for better traffic control, cleaner vehicle technologies, and improved public transport to reduce CO₂ emissions in urban environments. Keywords: Mixed traffic flow; Vehicle emission; CO2 emission; SUMO simulation; Urban traffic; Traffic composition