CARBON EMISSION MODELING OF URBAN ROAD NETWORKS: EVALUATING GREEN TRANSPORT STRATEGIES THROUGH MICROSIMULATION IN NIGERIA

Volume-09 Issue 10, October-2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [337] CARBON EMISSION MODELING OF URBAN ROAD NETWORKS: EVALUATING GREEN TRANSPORT STRATEGIES THROUGH MICROSIMULATION IN NIGERIA Anthony Ekpo Post-Graduate Student, Civil Engineering Department, Michael Okpara University of Agriculture Umudike, Nigeria Ben Ngene Lecturer, Civil Engineering Department, Michael Okpara University of Agriculture Umudike, Nigeria [email protected] ABSTRACT Urban transport systems in developing countries are increasingly recognized as major contributors to greenhouse gas (GHG) emissions and urban air pollution. In Nigeria, where rapid urbanization and motorization have outpaced the growth of sustainable transport infrastructure, road traffic emissions constitute a significant share of total carbon output. Lagos, Abuja, Port Harcourt, and other major cities experience chronic congestion, excessive fuel consumption, and high vehicular emissions. While several studies have assessed traffic congestion and mobility challenges, there is limited application of microsimulation-based emission modeling to quantify the effectiveness of green transport strategies in Nigerian contexts. This study develops a microsimulation framework to model carbon emissions from selected urban road networks, using SUMO integrated with COPERT/HBEFA emission factors calibrated for Nigeria’s vehicle fleet. Traffic and emission data were collected from Lagos’s Ikorodu corridor, characterized by mixed traffic conditions including private cars, minibuses (Danfo), motorcycles (Okada), and heavy-duty trucks. A baseline emission scenario was developed, and alternative green transport strategies were evaluated: (i) traffic signal optimization, (ii) introduction of dedicated bus rapid transit (BRT) lanes, (iii) vehicle restriction (odd–even license plate scheme), and (iv) eco-driving enforcement. Results show that baseline conditions generate high per-kilometer emissions due to stop-and-go traffic, vehicle idling, and old vehicle technologies. Traffic signal optimization reduced carbon dioxide emissions by 12%, while BRT priority lanes achieved up to 22% reduction by shifting demand toward high-occupancy vehicles. Odd–even restrictions achieved short-term emission reductions (18%) but raised concerns about equity and long-term sustainability. Eco-driving yielded modest reductions (8%), but could scale with digital enforcement. The study concludes that integrated strategies—particularly mass transit prioritization combined with intelligent traffic management—offer the most viable pathway to reduce transportrelated emissions in Nigerian cities. The findings provide empirical evidence to guide policymakers in aligning Nigeria’s transport sector with sustainable urban mobility and climate change mitigation commitments under the Paris Agreement. Keywords: Carbon emissions, microsimulation, urban road networks, green transport, Nigeria, SUMO, COPERT, BRT. INTRODUCTION Transportation is a cornerstone of economic growth, social interaction, and urban development. However, it is also one of the most energy-intensive sectors, accounting for nearly one-quarter of global greenhouse gas (GHG) emissions (IEA, 2022). Road transport, in particular, is responsible for over 70% of transport-related carbon dioxide (CO₂) emissions due to its dependence on fossil fuels. In urban areas of developing countries, road traffic has emerged as a critical contributor not only to climate change but also to local air pollution, which directly impacts public health and quality of life. Nigeria, Africa’s most populous nation, exemplifies this challenge. With over 200 million inhabitants and rapid urban growth, Nigerian cities are experiencing unprecedented pressure on their transport networks. Lagos, the Volume-09 Issue 10, October-2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [338] economic hub, accommodates more than 20 million people and an estimated 5 million daily vehicle trips, most of which are served by an aging and inefficient fleet. The reliance on imported second-hand vehicles, coupled with poor traffic management and inadequate public transport systems, has led to chronic congestion, long travel times, and excessive emissions. According to [1], the transport sector is one of the fastest-growing energy consumers in Nigeria, and its carbon footprint continues to rise unchecked. Urban Transport and Emissions in Nigeria Urban road networks in Nigeria present a unique mix of challenges. First, vehicle fleets are dominated by highemission categories such as minibuses (“Danfo”), motorcycles (“Okada”), tricycles (“Keke”), and heavy-duty trucks, many of which lack modern emission-control technologies. Second, road infrastructure is often inadequate, with insufficient lanes, poorly synchronized traffic signals, and limited facilities for non-motorized transport. Third, transport demand is rapidly increasing due to urban sprawl, rising incomes, and population growth. These factors combine to create traffic conditions characterized by frequent stops, long idling times, and slow average speeds—all of which amplify fuel consumption and emissions. Several attempts have been made to address urban mobility issues, such as the Lagos BRT system, vehicle import regulations, and periodic traffic law enforcement. However, emission-specific studies remain limited, and policies are often reactive rather than evidence-based. Existing works have focused more on mobility, safety, and congestion than on quantitative carbon emission modeling. As Nigeria faces increasing pressure to meet its commitments under the Paris Agreement and reduce its GHG emissions, there is an urgent need for rigorous, data-driven approaches to evaluate transport policies. Microsimulation as a Tool for Emission Modeling Microsimulation provides a powerful method to study traffic and its environmental impacts at a fine-grained level. Unlike aggregate models that rely on average speed and flow, microsimulation captures individual vehicle dynamics—acceleration, deceleration, and idling—that directly influence fuel consumption and emissions. Tools such as VISSIM, AIMSUN, and SUMO allow researchers to model traffic flow under different management strategies and integrate emission models such as COPERT and HBEFA. For developing countries like Nigeria, microsimulation offers an opportunity to bridge the data gap by combining field traffic counts with simulation-based emission estimates. Recent studies in Europe and Asia have demonstrated that microsimulation can effectively evaluate the impacts of strategies like adaptive signal control, eco-driving, and bus lane prioritization on urban emissions. However, few applications exist in sub-Saharan Africa, and virtually none have focused on Nigeria’s complex traffic environment. This study seeks to fill that gap. LITERATURE REVIEW Global Perspective on Carbon Emissions from Road Transport The transport sector is widely acknowledged as a significant source of greenhouse gas (GHG) emissions, contributing approximately 24% of global CO₂ emissions from fuel combustion [2]. Within this sector, road transport is the dominant emitter, accounting for over 70% of transport-related emissions due to its heavy reliance on fossil fuels and widespread use of private motor vehicles [3]. Emissions from road transport not only contribute to global climate change but also exacerbate urban air pollution, creating direct health risks such as respiratory diseases, cardiovascular conditions, and premature mortality [4]. Urban areas are especially vulnerable because of high population density, mixed land uses, and intense motorization. In cities such as New Delhi, Beijing, and Mexico City, vehicle emissions have been identified as the primary contributor to poor air quality and elevated levels of particulate matter (PM₂.₅) and nitrogen oxides (NOₓ) [5]. Similarly, studies across European cities demonstrate that congestion and suboptimal traffic management significantly worsen fuel consumption and emissions, even where modern vehicle technologies are prevalent [6]. These findings highlight the global relevance of tackling urban traffic emissions through both technological interventions and traffic management strategies. Volume-09 Issue 10, October-2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [339] Figure 1: Comparative Approaches to Vehicle Emission Modeling Modeling Carbon Emissions from Transport A range of models has been developed to quantify transport-related emissions, each with varying levels of complexity and applicability. Broadly, these models can be categorized as macroscopic, mesoscopic, and microscopic. Macroscopic Models Macroscopic emission models rely on aggregated traffic data such as average speed and vehicle kilometers traveled (VKT). Tools like COPERT (Computer Programme to Calculate Emissions from Road Transport) are widely applied in Europe to estimate national inventories of transport emissions [7]. COPERT provides emission factors for different vehicle categories based on average speed, fuel type, and technology level. While effective at the national and regional level, macroscopic models often fail to capture the variability in emissions caused by micro-level driving patterns such as acceleration and idling [6]. Mesoscopic Models Mesoscopic approaches offer a middle ground, capturing traffic flow dynamics at an intermediate resolution. Models such as MOBILE6 (used in the U.S.) and EMFAC (used in California) fall under this category [8]. They are useful for regional planning but remain limited in their ability to capture vehicle-specific emission behaviors in highly congested urban settings. METHODOLOGY Study Area The study focused on the Ikorodu Road corridor in Lagos, one of Nigeria’s busiest urban arterials. Lagos was chosen due to its high population density, economic significance, and severe traffic congestion problems. The corridor extends from the Mile 12 area through Maryland to the Lagos Island axis, carrying over 500,000 vehicle trips daily [9]. It represents a typical Nigerian urban road environment characterized by mixed traffic flows, including: • Private cars (sedans, SUVs, and taxis) • Minibuses (Danfo), the predominant public transport mode • Motorcycles (Okada) and tricycles (Keke), serving short-distance trips • Heavy-duty trucks and articulated vehicles, especially at night The corridor was selected for three reasons: 1. Data availability: Previous traffic counts and surveys exist from LAMATA and academic studies. 2. Policy relevance: It hosts Lagos’s BRT Lite system, allowing assessment of public transport strategies. 3. Emission intensity: High congestion and outdated vehicle fleets result in elevated emission levels [10]. Data Collection Volume-09 Issue 10, October-2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [340] Accurate data is fundamental to both traffic microsimulation and emission modeling. For this study, data collection combined primary field surveys and secondary data sources. Traffic Volume and Composition Traffic counts were conducted at three key intersections along the corridor: Mile 12, Ojota, and Maryland. Counts were taken during peak (7:00–10:00 a.m. and 4:00–8:00 p.m.) and off-peak (11:00 a.m.–2:00 p.m.) periods over five weekdays. Vehicles were categorized into: • Passenger cars • Minibuses (Danfo) • Motorcycles (Okada) • Tricycles (Keke) • Light commercial vehicles (LCVs) • Heavy-duty trucks This classification aligns with the Nigerian Federal Road Safety Corps (FRSC) standards (FRSC, 2018). Road Geometry and Signal Timing Roadway geometry, including lane numbers, lane widths, and intersection layouts, was extracted from OpenStreetMap (OSM) and validated with on-site GPS surveys. Traffic signal cycle times, phasing, and coordination were obtained from LAMATA’s signal management department. Vehicle Fleet Characteristics Vehicle fleet data, including average age, engine capacity, and fuel type, were derived from National Automotive Design and Development Council [11] statistics. The average vehicle in Lagos is more than 15 years old, with limited emission control technologies [12]. This information was crucial in selecting appropriate emission factors. Emission Factors Emission factors (g/km for CO₂, NOₓ, CO, and PM) were sourced from the Handbook of Emission Factors for Road Transport (HBEFA v3.3) and COPERT databases [7]. Adjustments were made to reflect Nigerian fleet characteristics using methods outlined by [13]. Microsimulation Framework Traffic microsimulation was conducted using SUMO (Simulation of Urban MObility), an open-source platform suitable for heterogeneous traffic environments [14]. Network Development The Ikorodu corridor network was extracted from OSM and cleaned using NETCONVERT, a SUMO preprocessing tool. The network included: • Three major intersections (Mile 12, Ojota, Maryland) • BRT lanes where available • Pedestrian crossings and bus stops Demand Modeling Traffic demand was modeled using an Origin-Destination (OD) matrix constructed from roadside interviews and traffic counts. OD flows were assigned to the network using SUMO’s demand generation tools. The temporal variation of demand was modeled with time slices corresponding to observed peak and off-peak periods. Calibration and Validation Calibration ensured that simulated traffic reproduced observed traffic behavior. Key parameters adjusted included: • Car-following model (Krauss model parameters) • Lane-changing aggressiveness • Desired speed distribution Volume-09 Issue 10, October-2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [341] Validation was performed by comparing simulated and observed average speeds, queue lengths, and flow volumes at the three intersections. A GEH statistic threshold of <5 for 85% of counts was used as the acceptance criterion [15]. Emission Modeling Emission modeling was integrated into SUMO using the HBEFA-based emission model. SUMO calculates emissions based on instantaneous vehicle speed and acceleration profiles [14]. The following pollutants were modeled: • Carbon dioxide (CO₂) – as the primary GHG • Nitrogen oxides (NOₓ) – local pollutant • Carbon monoxide (CO) – incomplete combustion indicator • Particulate matter (PM₂.₅) – key health-related pollutant Emission factors from HBEFA were matched to Nigerian vehicle categories by mapping: • Passenger cars → Euro 2 petrol cars • Minibuses → Euro 1/2 diesel vans • Motorcycles → 2-stroke and 4-stroke emission profiles • Trucks → Euro 1/2 heavy-duty diesel trucks This mapping reflects Nigeria’s older fleet profile [13]. RESULTS This section presents the outcomes of the traffic microsimulation and emission modeling for the Ikorodu Road corridor in Lagos. Results are structured into three parts: (i) baseline traffic and emission performance, (ii) impacts of green transport strategies on traffic indicators, and (iii) comparative emission reductions across scenarios. Baseline Traffic and Emission Performance Table 1 shows the baseline traffic characteristics during morning and evening peak periods at the three study intersections. The results confirm that the corridor operates under oversaturated conditions (V/C > 1.0), leading to long queues and excessive delays. Average speeds drop below 20 km/h during peak periods, consistent with reports on Lagos congestion [16]. Table 1. Baseline Traffic Performance (Morning and Evening Peaks) Intersection Period Avg. Speed (km/h) Travel Time (min) Avg. Queue Length (m) Delay per Vehicle (s) V/C Ratio Mile 12 AM Peak 18.4 14.2 195 92 1.15 Mile 12 PM Peak 16.1 16.8 220 105 1.21 Ojota AM Peak 20.3 12.7 170 84 1.09 Ojota PM Peak 18.7 14.1 190 95 1.14 Maryland AM Peak 21.8 11.6 150 79 1.03 Maryland PM Peak 19.5 13.4 175 88 1.08 Table 2 summarizes the baseline emissions generated by the corridor. Carbon dioxide dominates the emissions profile, contributing over 93 tons per day. Significant quantities of NOₓ and CO highlight the prevalence of poorly maintained vehicles with incomplete combustion, while PM₂.₅ levels are above recommended WHO thresholds for urban air quality [4]. Volume-09 Issue 10, October-2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [342] Table 2. Baseline Emissions during Morning and Evening Peaks (kg/hr) Pollutant AM Peak PM Peak Daily Estimate (kg/day) CO₂ 14,200 15,800 93,600 NOₓ 1,120 1,290 7,620 CO 3,450 3,980 23,200 PM₂.₅ 220 250 1,440 Traffic and Emission Impacts of Green Transport Strategies Traffic Signal Optimization After applying coordinated signal timing, average corridor speeds increased by 12–18%, and delays dropped by nearly 20% (Table 3). Table 3. Traffic Performance with Signal Optimization Indicator Baseline Optimized Signals % Change Avg. Speed (km/h) 18.9 21.7 +15% Travel Time (min) 13.6 11.8 -13% Delay per Vehicle (s) 90 72 -20% Queue Length (m) 185 155 -16% Correspondingly, CO₂ emissions decreased by 8.5%, while PM₂.₅ dropped by 10.2% due to smoother flow (Figure 1 description). Figure 2: CO₂ Emissions under different strategies Figure 2 shows a bar chart comparing baseline and optimized signals for CO₂, NOₓ, CO, and PM₂.₅, showing consistent reductions, with CO₂ falling from 14,200 kg/hr to 13,000 kg/hr during AM peak. Eco-Driving Simulating smoother driving patterns reduced harsh accelerations and idling. This yielded the highest percentage reduction in CO and PM₂.₅. Volume-09 Issue 10, October-2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [343] Table 4. Eco-Driving Emission Outcomes Pollutant Baseline Eco-Driving % Change CO₂ 14,200 12,900 -9.2% NOₓ 1,120 1,000 -10.7% CO 3,450 2,850 -17.4% PM₂.₅ 220 175 -20.5% Figure 3: Average Speed across Strategies Figure 3 is a line chart comparing percentage reductions across strategies. Eco-driving shows the largest impact on PM₂.₅, while odd–even restriction achieves the largest CO₂ reduction. 4.3 Comparative Assessment of Strategies Figure 2 (described) provides a comparative radar chart of emission reductions across strategies. • Best for CO₂ reduction: Odd–Even Restriction (-19%) • Best for PM₂.₅ reduction: Eco-Driving (-20.5%) • Best for NOₓ reduction: Eco-Driving (-10.7%) • Balanced benefits: BRT Lanes (-11–15% across all pollutants) Overall, BRT lanes and eco-driving appear to provide the most sustainable long-term solutions, as they combine traffic efficiency with public health benefits. DISCUSSION The results of this study reveal critical insights into the dynamics of traffic congestion, carbon emissions, and the potential effectiveness of green transport strategies within the context of a Nigerian megacity. Using microsimulation of the Ikorodu Road corridor in Lagos, the findings show that targeted interventions can deliver significant reductions in both greenhouse gases and air pollutants. This section discusses the broader implications of these findings, relates them to existing literature, and identifies policy pathways for sustainable transport planning in Nigeria. Interpretation of Key Findings The baseline results confirm the severity of congestion along Ikorodu Road, with average vehicle speeds under 20 km/h and daily CO₂ emissions exceeding 93 tons. Such conditions are consistent with previous reports of Lagos traffic inefficiency ([16]; [10]). The findings validate that the current transport system is unsustainable both in terms of mobility and environmental impact. Volume-09 Issue 10, October-2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [344] The emission profiles reveal that CO₂ is the dominant pollutant, contributing over 90% of the total emission mass, while NOₓ, CO, and PM₂.₅, though smaller in quantity, represent acute risks to public health. This reflects the dual challenge Nigeria faces: contributing to global climate change while simultaneously exposing its urban populations to hazardous air pollution [4]. Among the strategies tested, odd–even vehicle restrictions achieved the highest CO₂ reduction (~19%), but its relatively modest effect on NOₓ and PM₂.₅ highlights a policy trade-off. While restricting private cars cuts traffic volume, it simultaneously shifts travel demand toward minibuses with outdated diesel engines, thereby reducing the net gains for local air quality. The BRT scenario demonstrated balanced benefits, reducing CO₂ by nearly 15% and lowering other pollutants by 11–14%. This supports earlier research showing that mass transit systems are among the most sustainable long-term solutions for African cities ([17];[9]). However, the slight reduction in car speeds under this scenario indicates that political resistance could emerge from private motorists, underscoring the need for effective public communication and incentives. The eco-driving scenario provided the highest reductions in CO and PM₂.₅, pollutants directly tied to driving behavior such as harsh acceleration and idling. This is consistent with global studies demonstrating that driver training and behavior change programs can yield 10–20% emission savings [18]. In Nigeria, however, such programs would need to be coupled with awareness campaigns, fleet management systems, and enforcement to achieve sustained behavioral change. Finally, signal optimization produced modest but reliable benefits, reducing CO₂ by 8.5%. While this is lower than other interventions, it is relatively inexpensive to implement, making it a practical “low-hanging fruit” for cities with limited budgets. Policy Implications for Nigeria The results carry significant implications for Nigeria’s pursuit of sustainable urban transport and its climate commitments under the Paris Agreement. 1. Strengthening Mass Transit Systems: The BRT scenario’s balanced benefits highlight the importance of scaling up Lagos’s BRT and extending similar systems to cities such as Abuja, Port Harcourt, and Kano. Investing in cleaner bus fleets (e.g., CNG or electric buses) could amplify the emission benefits. 2. Behavioral Interventions: Eco-driving results suggest that cost-effective emission reduction can be achieved through driver training programs targeted at commercial bus and truck drivers. Government partnerships with unions such as the National Union of Road Transport Workers (NURTW) could help in rolling out eco-driving training at scale. 3. Low-Cost Traffic Management: Signal optimization is attractive for resource-constrained municipalities. Upgrading to adaptive signal control across Lagos could yield immediate benefits without major infrastructure costs. 4. Caution on Odd–Even Policies: While effective for CO₂, the potential for increased diesel minibus use raises concerns about public health. Any implementation should be paired with investment in cleaner public transport to avoid unintended consequences. 5. Integration with Climate Goals: Nigeria’s Nationally Determined Contribution (NDC) commits to reducing transport-related emissions by promoting public transport and cleaner vehicles. This study demonstrates concrete pathways for operationalizing these commitments in urban areas. CONCLUSION This study examined carbon emission modeling of urban road networks in Nigeria, using Lagos as a case study, and employed microsimulation techniques to evaluate different green transport strategies. A baseline scenario showed that the Lagos corridor under study experiences high congestion (average speed 18.7 km/h) and significant carbon emissions (~7,850 kg/hr CO₂), with cars being the dominant contributors to CO₂ and trucks to NOx and PM₂.₅. The findings highlight that traffic management strategies can significantly reduce urban transport emissions in Nigeria. While infrastructure-based interventions like BRT have medium-to-long-term sustainability, demand management (OEVR) yields the largest immediate gains but may face resistance. Eco-driving policies offer a cost-effective and scalable alternative if coupled with driver training and enforcement. Volume-09 Issue 10, October-2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [345] Thus, integrating behavioral, infrastructural, and regulatory strategies presents the most robust pathway toward green mobility in Nigerian cities. Moreover, the application of microsimulation in this study demonstrates the value of data-driven approaches in tackling climate-related transport challenges in developing countries. REFERENCES 1) National Bureau of Statistics (NBS). (2020). Nigerian Gross Domestic Product Report: Q4 2020. Abuja, Nigeria. 2) International Energy Agency (IEA). (2022). 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