In a number of European countries, national model systems have been developed that can be used for forecasting future freight transport volumes and/or vehicle flows. The paper contains a review and comparison of a number of such systems, including: the Swedish national freight model system (SAMGODS); the Norwegian national freight model system (NEMO). Both SAMGODS and NEMO use the STAN software for multi-modal assignment; the Walloon region freight model system in Belgium (WFTM), which uses the NODUS multi-modal assignment software; the Italian national model system, which for freight uses input-output models and disaggregate mode choice models; and the Dutch models TEM and SMILE (the former uses input-output methods, the latter has make-use tables and a logistic module for the location of distribution centres); models used in the UK for national freight transport forecasts (e.g. based on the STEMM project). Furthermore, a number of international model systems are included: the SCENES and NEAC models for Europe and models for specific international corridors (e.g. fixed link projects in Scandinavia, Alpine crossings). The material for these reviews has been collected in the course of model review projects for the European Commission, in Sweden and the UK. The second part of the paper focuses on ideas for future development of freight transport models that should increase the accuracy of the forecasts and the range of policy applications of the models. The development of two different types of models are recommended: a fast policy analysis model, for initial screening and comparison of policy alternatives; and a detailed network-based forecasting model, for predictions at the network level and to provide inputs for project evaluation. The former model can be developed as a system dynamics model and/or a model integrating outputs from other more detailed models. For the detailed model it is proposed that a number of interlinked modules be developed. At the national/international level: an input-output model for production/attraction and a distribution model; a disaggregate model for mode and shipment size choice, based on combined stated and revealed preference data; at the regional/urban level: a disaggregate model linked with the inputs and outputs of a disaggregate passenger model. At all geographical levels: an assignment module. The starting point for our ideas on the regional/urban model is the use of information from disaggregate passenger travel demand models to improve base year information and improve forecasts, including linking factors affecting demand for personal travel to factors affecting freight movements. In addition, this approach would offer a chance to improve the network supply/demand processes that should jointly affect both freight and passenger transport. For the covering abstract see ITRD E124693.
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