Backcasting in freight transport demand modelling – chances and challenges

Abstract

Freight transport demand models are important tools to support policy decision-making by enabling decision makers to evaluate transport policies and correlated effects. This significance puts high pressure on freight models regarding their accuracy. In order to ensure model accuracy there are different methods within the wide area of quality assurance that can be applied. Although backcasting is such a method it is, however, often neglected or implemented insufficiently. The paper presents major challenges and chances occurring from backcasting that have been conducted using a state-of-the- practise freight model. It reveals that backcasting is not easy to handle – especially referring to data availability and quality. Serious challenges emerge regarding availability of consistent input and output data as well as goods classification. A guideline, deduced from the experience in application, is presented to assist practising backcasting in freight modelling.