Estimating on-road vehicle fuel economy in Africa: a case study based on an urban transport survey in Nairobi, Kenya

To estimate in-use fuel economy of a vehicle fleet in a typical SSA city such as Nairobi, there is need for data to describe the fleet composition, characteristics and activity for in-use vehicles. Moreover, these data need to include the total number of vehicles dis-aggregated by vehicle type, fuel type, age, emission technology and annual mileage. Such data from official sources is often incomplete, inaccurate, inconsistent and outdated. Another challenge is from the growing use of informal transport in SSA such as the use of matatus, bodabodas and tuktuks. These vehicles tend to be unregistered (making it difficult to use standard fleet inventory methods to capture their contribution to urban traffic) as well as being old, poorly maintained and overloaded during use, all factors that will increase tail-pipe emissions resulting in enhanced air pollution. Therefore, in SSA the high composition of such vehicle fleets may be a source of uncertainties.

To develop a methodology for estimating fuel economy in African cities vehicle characteristics and activity data, for both the formal fleet (private cars, motorcycles, light and heavy trucks) and informal fleet—minibuses (matatus), three-wheelers (tuktuks), goods vehicles (AskforTransport) and two-wheelers (bodabodas)—were collected for the city of Nairobi. Using two empirical models, general linear modelling (GLM) and artificial neural network (ANN), the relationships between vehicle characteristics for this fleet and fuel economy were analyzed for the first time. Fuel economy for bodabodas (4.6 ± 0.4 L/100 km), tuktuks (8.7 ± 4.6 L/100 km), passenger cars (22.8 ± 3.0 L/100 km), and matatus (33.1 ± 2.5 L/100 km) was found to be 2–3 times worse than in the countries these vehicles are imported from. The GLM provided the better estimate of predicted fuel economy based on vehicle characteristics.

The analysis of survey data covering a large informal urban fleet helps meet the challenge of a lack of availability of vehicle data for emissions inventories. This may be useful to policy makers as emissions inventories underpin policy development to reduce emissions.