A heavy goods vehicle fleet forecast for South Africa

• Published in: Journal of transport and supply chain management Vol. 12; no. 1; pp. 1 - 12
• Main Authors: Le Roux, Philippus P.T., Simpson, Zane P., Havenga, Jan H.
• Format: Journal Article
• Language: English
• Published: Johannesburg AOSIS 2018; African Online Scientific Information Systems (Pty) Ltd t/a AOSIS; AOSIS (Pty) Ltd
• Subjects: Business logistics; collaboration; Context; Costs; Demand; Economic aspects; Economic forecasting; Economic growth; Environmental policy; Freight transportation; Freight transportation industry; GDP; Gross Domestic Product; heavy goods vehicle fleet; Infrastructure; Intermodal; Investments; Iron compounds; Logistics; Mathematical models; Methodology; modal shift; performance-based standards; Planning; Productivity; Public policy; Roads; Roads & highways; Seasonal variations; South Africa; Transportation corridors; Vehicles; South Africa; North America; Europe
• ISSN: 2310-8789; 1995-5235; 1995-5235
• DOI: 10.4102/jtscm.v12i0.342

Purpose: 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.