Estimating on-road vehicle fuel economy in Africa: A Case Study Based on an Urban Transport Survey in Nairobi, Kenya

Aderiana Mutheu Mbandi, Jan Boehnke, Dietrich Schwela, Harry Vallack, Mike R Ashmore, Lisa Emberson

    Research output: Contribution to journalArticlepeer-review

    14 Citations (Scopus)
    206 Downloads (Pure)


    In African cities, like Nairobi, policies to improve vehicle fuel economy help to reduce greenhouse gas emissions and improve air quality, but lack of data is a major challenge. We present a methodology for estimating fuel economy in such 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 and used to estimate fuel economy. Using two empirical models, general linear modelling (GLM) and artificial neural network (ANN) models, relationship between vehicle characteristics for this fleet was for the first time analyzed. 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 model provided the better estimate of predicted fuel economy based on vehicle characteristics. The analysis of the survey data with 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.
    Original languageEnglish
    Article number1177
    Pages (from-to)1-28
    Number of pages28
    Issue number6
    Publication statusPublished - 26 Mar 2019


    • Africa
    • Air pollution
    • Bodaboda
    • Fuel economy
    • GHGs
    • In-use vehicle
    • Informal transport
    • Matatu

    ASJC Scopus subject areas

    • Renewable Energy, Sustainability and the Environment
    • Energy Engineering and Power Technology
    • Energy (miscellaneous)
    • Control and Optimization
    • Electrical and Electronic Engineering


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