A Novel Approach to Estimate Road Freight Distribution based on Commodity Types

There are significant challenges associated with forecasting distribution of freight traffic generated by large warehousing developments or rail/freight interchanges. This is partly related to lack of data and evidence on complex road freight trip chains at strategic level, and partly related to forecasting methods used that fail to reflect the underlying relationships between freight commodities and their distinct travel patterns. Goods arriving from international origins are expected to be transported first into a range of National Distribution Centres (NDCs), Regional Distribution Centres (RDCs), or directly to the end users (retail or domestic). The proportion of goods to be transported to each of these is likely to depend on the type of goods and commodities. For example, goods which are seasonal (such as outdoor/garden equipment, summer clothing, etc.) and those which are non-time sensitive and/or have long lead-times (e.g. toys, electrical, etc.) generally transported directly to NDCs for storage. On the other hand, goods which are time sensitive and/or have short lead-times (e.g. perishable groceries) generally go directly to RDCs (for fast turn-around and onward distribution to store). Hence, there is a profound relationship between the type of goods and road freight activity associated with each of the following demand segments: • NDC / Large Distribution Centres freight-related demand; • RDC / Small-Medium Distribution Centres freight-related demand; and • Retail / Domestic freight related movements. These demand segments, which are linked to commodity types and spatial distribution of freight distribution centres and retail locations, are expected to have distinct trip distributions on the road network. Using this relationship as the key principle, a novel approach is developed in this study to model Heavy Goods Vehicle (HGV)’s trip distribution by commodity type. The developed method was implemented and tested for a specific case study to forecast freight movements to/from a future significant freight generator in the UK; a warehousing employment development with a capacity of more than 800,000 m² of employment land. The forecasting methodology uses a ‘hybrid’ gravity model (see Psarras et. al., 2017) to establish trip distributions for the above segments; which are expected to be significantly different. For example, according to the Continuing Survey of Road Good Transport Great Britain (CSRGT GB) and combination of other independent data sources, the average distance travelled by HGVs associated with NDC, RDC and Retail is estimated to be 153, 107 and 35 km, respectively. The inputs used to calibrate parameters of the hybrid gravity model include: • total estimated number of freight trips from the development by commodity type; • observed trip length distributions by freight demand segments described above; • an estimate of distance between different zone pairs in the model; and • specific zonal weights, reflecting relative freight activity associated with each of the defined demand segments. A freight distribution model was developed using data from different sources including CSRGT GB, Valuation Office Agency (VOA) data, and various zonal planning data (i.e. employment and demographic) sourced from Leicestershire’s Pan Regional Transport Model (PRTM). PRTM is also used to provide additional data for model calibration, forecasting, and then to test the impact of the development-related freight traffic on the wider highway network in future. The new approach establishes the link between pattern of freight movements and commodities, providing the opportunity for a more informed forecasting of freight growth and greater flexibility for policy testing. The expected added accuracy resulting from the representation of different travel patterns by commodity types and use of zonal freight activity indicators is discussed in detail in the paper. The model outputs are verified by comparing forecast trip distributions with independent evidence on the expected key freight activities in the region. The findings from this study show that the developed methodology can capture the differences between travel patterns of various freight segments in the market and can be used flexibly for freight forecasting purposes. Recommendations are made on how to expand the methodology and use it to develop matrices of freight movement at a larger scale as well as other freight activity data that can be collected to improve the forecasting results.


  • English

Media Info

  • Media Type: Digital/other
  • Features: Bibliography; Figures; Tables;
  • Pagination: 17p
  • Monograph Title: European Transport Conference 2020

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Filing Info

  • Accession Number: 01765995
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Feb 4 2021 2:13PM