By the same authors

From the same journal

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

Research output: Contribution to journalArticle

Full text download(s)

Published copy (DOI)

Author(s)

Department/unit(s)

Publication details

JournalEnergies
DateAccepted/In press - 15 Mar 2019
DatePublished (current) - 26 Mar 2019
Issue number6
Volume12
Number of pages28
Original languageEnglish

Abstract

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 amajor 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), 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.

Bibliographical note

© 2019 by the authors.

    Research areas

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

Discover related content

Find related publications, people, projects, datasets and more using interactive charts.

View graph of relations