Compared to overall greenhouse gas emissions which have decreased, the emissions of the transport sector have increased by 25\;% for the period 1990 to 2005. To meet the global EU target of 8\;%, it is therefore necessary for the transport sector to reduce greenhouse gas emissions substantially. The focus of new urgently required policies to achieve this target is set on the use of more environmentally friendly transport modes. To support the implementation of these measures, knowledge of the maximum potential for a shift from road to rail and ship is necessary. The Think-up project aimed to draw together transport demand modelling and scenario building, and compare the methodologies used and results obtained. In particular, the project considered the passenger transport market. Highly complex, the researchers used segments to subdivide the market into groups such as trip distance, mode and purpose. The segmentation approach very much resembles the parameters influencing the transport demand models. The potential for a modal shift in passenger transport can be evaluated through demand modelling by changing different factors, influencing the passenger's choice, such as transport costs or time of trip. In the European Union, there are a range of models for determining passenger transport demand. According to the Think-up project, these models can be subdivided into different categories. Representations can be used to determine short distance mobility according to household income and the characteristics of different transport means and models that provide an estimate of the probability of making one or another choice of transport mode. Transport demand can also be estimated by comparing the volume of transport between regions based on the regions' economic data. The problem in using this brand of information, for which a high level of detail is needed, is that the connection between transport and the economy is usually missing. These methods for calculating the potential for a modal shift constitute the best approaches for achieving results that are as reliable as possible, because they are based on data that reflect the 'real' behaviour of passengers. They also allow the inclusion of factors for which no data is available. For example, the trip purpose, socio-demographic and socio-economic factors are indirectly considered. The outcome of different passenger transport models were integrated into the meta-model developed during the subsequent Expedite project. Not intended to replace the detailed models, this meta-model offers the possibility of a quick scan for the effects of a large number of policy options. Detailed studies for promising measures could then be done on specific segments of the transport market.