This paper describes research which investigates (on the basis of a Dutch case study) how good elasticity models are able to make long-term predictions concerning mobility. The results of an (input) analysis showed that most (often social-economical) variables are already difficult to predict for the long term. This has a negative influence on mobility predictions, because these variables are used as input for elasticity models. When the correct input values are used in the model, there remains a difference in output between the actual and predicted mobility. The remaining difference may be caused by wrong or incomplete model parametes and model assumptions. Especially (in the time of making the forecasts) unexpected developments have a big influence (since it is not possible to model them). Examples of these developments in the recent past are the effect of car pooling, the introduction of public transport annual season tickets for students and the use of a second car. As the prediction term grows, the error margin will grow as well. On the one hand, the question remains whether long-term mobility forecasts can be predicted at all with an acceptable error margin. On the other hand, it remains to be seen if expansion or differentiation of the model, in elasticities and/or variables, will decrease this error. A more complex model will increase the chance on errors (in input values). (A)
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