2A: Construction of an integrated database to permit the macro level modelling of road freight scenarios.

This project will aim to assemble numerous road freight and logistics data sets into a large integrated database than can be used to construct environmental scenarios for the road freight transport sector.  At the core of this database will be the freight transport decarbonisation framework developed during the EPSRC Green Logistics project.  This framework examines the combined effect of five sets of measures related to freight: modal split, supply chain structure, vehicle utilisation, energy efficiency and the carbon content of the energy used (i.e. scope for switching to alternative, lower carbon energy sources). 


1. combine all the available road freight and logistics datasets into a single integrated database

2. develop a software tool capable of interrogating this database to assess the economic environmental impact of road freight rationalisation measures that companies can apply, individually and in collaboration, taking account of future trends in energy costs, traffic congestion and climatic conditions

3. use the database and supporting tools to assess the macro-impact on the road freight  system of a range of new vehicle technology, regulatory, human factors, logistical and operational options introduced over various time scales

Integrating data from various sources would permit macro-level modelling of a range of scenarios, such as:

  • Rescheduling freight delivery to off-peak periods
  • Establishing regional consolidation centres through which less-than-truckloads (LTLs) would be channelled
  • Forming multi-lateral collaborative networks
  • Modifying truck size and weight limits and other truck design features
  • Altering regulations relating to drivers’ hours and delivery curfews
  • Introducing road user charging for trucks
  • Returning to more decentralised systems warehousing
  • Switching road vehicles to lower carbon energy source

Project tasks

  • Consult project partners to determine what modelling problems need solving. This will drive the data collection and prioritise data sources to be acquired.
  • Assembling, checking and, where necessary, reformatting the various databases. Exploring ways of combining them into an integrated framework.
  • Develop software tools to extract relevant data and (i) use it to assess the impact of individual rationalisation measures and (ii) synthesise it into particular scenarios comprising sets of measures.


Academic Impact: Assembling, for the first time, all the main road freight-related data sets and integrating them into a coherent  database will make it possible to develop and apply new research methodologies and gain insights.   This will supplement the macro-level road freight research that has traditionally been confined to The Continuing Survey of Road Goods Transport (CSRGT).

Commercial and Social Impact: The macro-level model can be used to forecast changes in the road freight market and trends in costs and externalities.  These outputs will be valuable to policy makers and company managers.  A virtual data centre may be established which external users could be able to access on a ‘query-response’ basis to obtain information on a range of road freight variables.


  1. Palmer, A. and M. Piecyk, Time, Cost and CO2 Effects of Rescheduling Freight Deliveries, in Proceedings of the Logistics Research Network Conference, A. Whiteing, Editor. 2010: University of Leeds.
  2. Palmer, A. and A.C. McKinnon, Analysis of Opportunities for Multi-lateral Collaboration in FMCG Supply Chains, in Proc  Logistics Research Network conference. 2011: Southampton.
  3. McKinnon, A.C., Improving the Sustainability of Road Freight Transport  by Relaxing Truck Size and Weight Restrictions, in Supply Chain Innovation for Competing in Highly Dynamic Markets: Challenges and Solutions, P. Evangelista, et al., Editors. 2011, IGI Global: New York.
  4. McKinnon, A.C., Government Plans for Lorry Road User Charging in the UK: A Critique and an Alternative. Transp Policy, 2006. 13(3): 204-216