A heavy goods vehicle fleet forecast for South Africa

dc.contributor.authorHavenga, Jan H.en_ZA
dc.contributor.authorLe Roux, Phillippus P. T.en_ZA
dc.contributor.authorSimpson, Zane P.en_ZA
dc.identifier.citationHavenga, J. H., Le Roux, P. P. T. & Simpson, Z. P. 2018. A heavy goods vehicle fleet forecast for South Africa. Journal of Transport and Supply Chain Management, 12:a342, doi:10.4102/jtscm.v12i0.342
dc.identifier.issn1995-5235 (online)
dc.identifier.issn2310-8789 (print)
dc.descriptionCITATION: Havenga, J. H., Le Roux, P. P. T. & Simpson, Z. P. 2018. A heavy goods vehicle fleet forecast for South Africa. Journal of Transport and Supply Chain Management, 12:a342, doi:10.4102/jtscm.v12i0.342.
dc.descriptionThe original publication is available at
dc.description.abstractPurpose: To develop and apply a methodology to calculate the heavy goods vehicle fleet required to meet South Africa’s projected road freight transport demand within the context of total surface freight transport demand. Methodology: Total freight flows are projected through the gravity modelling of a geographically disaggregated input–output model. Three modal shift scenarios, defined over a 15-year forecast period, combined with road efficiency improvements, inform the heavy goods vehicle fleet for different vehicle types to serve the estimated future road freight transport demand. Findings: The largest portion of South Africa’s high and growing transport demand will remain on long-distance road corridors. The impact can be moderated through the concurrent introduction of domestic intermodal solutions, performance-based standards in road freight transport and improved vehicle utilisation. This presupposes the prioritisation of collaborative initiatives between government, freight owners and logistics service providers. Research limitations: (1) The impact of short-distance urban movements on fleet numbers is not included yet. (2) Seasonality, which negatively influences bi-directional flows, is not taken into account owing to the annual nature of the macroeconomic data. (3) The methodology can be applied to other countries; the input data are however country-specific and findings can therefore not be generalised. (4) The future possibility of a reduction in absolute transport demand through, for example, reshoring have not been modelled yet. Practical implications: Provides impetus for the implementation of domestic intermodal solutions and road freight performance-based standards to mitigate the impact of growing freight transport demand. Societal implications: More efficient freight transport solutions will reduce national logistics costs and freight-related externalities. Originality: Develops a methodology for forecasting the heavy goods vehicle fleet within the context of total freight transport to inform government policy and industry actions.en_ZA
dc.format.extent12 pages
dc.subjectHeavy goods vehicle fleeten_ZA
dc.subjectTransportation, Automotive -- South Africaen_ZA
dc.subjectFreight and freightage -- Forecasting -- Mathematical modelsen_ZA
dc.subjectTransportation demand management -- South Africaen_ZA
dc.titleA heavy goods vehicle fleet forecast for South Africaen_ZA
dc.description.versionPublisher's version
dc.rights.holderAuthors retain copyright

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