With a growing amount of commercial vehicle transport in urban areas, the agencies have an interest in assessing infrastructure investments and policy measures, such as regulations and taxes, in their mission to decrease negative societal effects related to the transports. In order to do so, e.g. the Swedish Transport Administration and regional and local authorities have an interest in employing forecast models. However, a functional regional model for freight and/or commercial vehicles is lacking in the Swedish planning system. The freight transport and logistics system is a complex system characterized by many stakeholders and decision-makers with sometimes conflicting interests, and a large variety in characteristics of shipments and trips. This makes the system hard to model using the traditional methods that are often used for passenger transport forecasts. Further, regional or urban freight transport is tightly connected to surrounding systems such as the economic system including business models and consumption patterns and freight transport on the larger scale. Models that are used for public sector decision-making must meet certain requirements in terms of transparency and quality. These requirements form a framework of different parameters that need to be considered when developing the models (see Figure 1 below). This study aims to recommend actions for Swedish agencies, primarily the Swedish Transport Administration, in order to enable future high-quality analyses and forecasts of regional and urban freight and commercial transport. Previous model approaches made in Sweden and other countries are described through a literature review, which has been complemented and facilitated by interviews with model experts in Sweden, Norway and Denmark. The focus of the literature review was on model reviews, descriptions of models that are implemented and applied as part of the agencies’ planning process and especially promising approaches made in a research context. These considerations are motivated by the relevance for model implementation in the near future. Model approaches are briefly described in terms of model categories, going from simple forecasts based on historic vehicle count data for single links, to more advanced supply-chain based or vehicle tour based models. The more advanced and detailed the model category, the larger share of the agencies’ analysis needs is met. However, the amount of necessary input data to feed the models also increases on the same scale, leading to larger investments of resources for model development and longer implementation times.