Urban mobility and CO₂ reduction in Maiduguri, Nigeria: Integrating random utility mode choice modelling with SUMO traffic simulation

Carbon dioxide (CO2) emissions from transport are rising significantly in low- and middle-income countries. However, empirical data on African secondary cities are scarce, and few studies integrate behavioural mode choice modelling with traffic simulation to evaluate emission outcomes. This study examines Maiduguri, Nigeria, a city where a long-standing motorcycle ban has fundamentally reshaped its urban mobility patterns, by employing a comprehensive analytical framework that combines Random Utility Theory (RUT)-based mode choice modelling with calibrated results from the Simulation of Urban Mobility (SUMO). Primary data from traffic assessments, commuter surveys ( n = 385), and fuel consumption evaluations were used to construct a multinomial logit model that associates trip duration, costs, comfort levels, income, and access time with mode-choice probabilities. SUMO simulations, validated against field-derived emissions estimates ( R 2 = 0.92), assessed carbon dioxide emissions across six distinct vehicle classes. The findings reveal that tricycles and private cars accounted for 82.23% of total emissions (173,371 kg CO₂ over a 10-h duration). Policy models suggest that implementing integrated policies could result in an 11.24% reduction in emissions, while deploying car-sharing schemes might yield a 25.32% decrease. Additional bus-focused operational scenarios indicate that reducing waiting times and improving passenger comfort could achieve emission reductions of up to 15.25%. Route-level analysis revealed that emissions intensity was more profoundly affected by modal distribution than by traffic volume. The findings suggest that improving public transport comfort and supporting active travel infrastructure offers a cost-effective strategy to accelerate low-carbon transitions in resource-constrained environments with informal mobility systems. This study presents a replicable framework for integrating behavioural and simulation models to inform sustainable transport policy in data-scarce cities of the Global South.