A heavy goods vehicle fleet forecast for South Africa
| dc.contributor.author | Havenga, Jan H. | en_ZA |
| dc.contributor.author | Le Roux, Phillippus P. T. | en_ZA |
| dc.contributor.author | Simpson, Zane P. | en_ZA |
| dc.date.accessioned | 2020-04-29T10:29:51Z | |
| dc.date.available | 2020-04-29T10:29:51Z | |
| dc.date.issued | 2018 | |
| dc.description | CITATION: 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.description | The original publication is available at http://www.jtscm.co.za | |
| dc.description.abstract | 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. | en_ZA |
| dc.description.uri | https://jtscm.co.za/index.php/jtscm/article/view/342 | |
| dc.description.version | Publisher's version | |
| dc.format.extent | 12 pages | |
| dc.identifier.citation | 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.identifier.issn | 1995-5235 (online) | |
| dc.identifier.issn | 2310-8789 (print) | |
| dc.identifier.other | doi:10.4102/jtscm.v12i0.342 | |
| dc.identifier.uri | http://hdl.handle.net/10019.1/108494 | |
| dc.language.iso | en_ZA | en_ZA |
| dc.publisher | AOSIS | |
| dc.rights.holder | Authors retain copyright | |
| dc.subject | Heavy goods vehicle fleet | en_ZA |
| dc.subject | Transportation, Automotive -- South Africa | en_ZA |
| dc.subject | Freight and freightage -- Forecasting -- Mathematical models | en_ZA |
| dc.subject | Containerization | en_ZA |
| dc.subject | Transportation demand management -- South Africa | en_ZA |
| dc.title | A heavy goods vehicle fleet forecast for South Africa | en_ZA |
| dc.type | Article | en_ZA |