Econometric Modelling and Forecasting of Freight Transport Demand in Great Britain

Empirically derived estimates of freight transport demand elasticities and accurate forecasts of future demand are important for freight planning and policy making. The sensitivity of freight transport demand to the changes of its determinants can help policy makers to evaluate alternative policy options in controlling future freight transportation growth, emissions reductions or modal shift. Accurate forecasts can provide information on future freight transport levels in the appraisal of freight transport related projects and transport policies. From the sustainability standpoint, it is important to be able to forecast future freight volumes, so that the impacts of any environmental policy initiatives can be compared against the do-nothing scenario. Econometric models can not only forecast future demand but can also explain economic or business phenomena and increase our understanding of relationships among variables. This study applies state of the art econometric models to the analysis of road plus rail freight transport demand in Great Britain (GB). The movement of goods around GB increased markedly over the period 1978- 2007, from 178 billion ton kilometers in 1978 to 255 billion ton kilometers in 2007. Road and rail have taken a substantial share of total freight movements. In 2007, 76 percent of freight was moved by road and rail, only 24 percent by water and pipelines. The authors will focus on the road plus rail freight demand in this study. Due to the dominant role of road and rail in the GB freight transport sector, modeling and forecasting GB road plus rail freight demand can provide useful information for both transportation planners and policy makers. Six econometric methods are applied to GB road plus rail freight transport demand modeling and forecasting, at both aggregate and disaggregate (commodity group) levels. The econometric models applied are: the traditional OLS regression model, the Partial Adjustment (PA) model, the reduced Autoregressive Distributed Lag model (ReADLM), the unrestricted Vector Autoregressive (VAR) model, the Time-Varying-Parameter (TVP) model, and the Structural Time Series model (STSM). Elasticity estimates with respect to measures of economic activity are provided and the relative forecasting accuracy of the alternative econometric models is evaluated.

• Record URL:
  https://aetransport.org/past-etc-papers/conference-papers-pre-2009/conference-papers-2009
• Corporate Authors:

  Association for European Transport (AET)

  1 Vernon Mews, Vernon Street, West Kensington
  London W14 0RL,    
• Authors:
  • Shen, Shujie
  • Fowkes, Tony
  • Whiteing, Tony
  • Johnson, Daniel
• Conference:
  • European Transport Conference, 2009
  • Location: Leiden Leeuwenhorst Conference Centre , Netherlands
  • Date: 2009-10-5 to 2009-10-7
• Publication Date: 2009

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: 21p
• Monograph Title: European Transport Conference, 2009 Proceedings

Subject/Index Terms

• TRT Terms: Demand; Econometric models; Elasticity (Economics); Forecasting; Freight traffic; Freight transportation; Time series analysis
• Geographic Terms: United Kingdom
• Subject Areas: Freight Transportation; Planning and Forecasting; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01345241
• Record Type: Publication
• Files: TRIS
• Created Date: Jul 21 2011 10:07AM