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Road Pricing Context













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Urban Road User Charging Online Knowledge Base


This chapter has summarised the current state of the art for predicting the impacts of urban road pricing schemes. It has found that prediction is considered to be a crucial element of the decision-making process and has shown that models play a central role. It has also described a range of alternative modelling approaches that are available and has illustrated ways in which variations in both the approach adopted and the details of how models are applied to individual situations may affect outcomes. It has also told a story of increasing complexity of prediction work and associated models over time, in response to increasing awareness of the complex relationships that govern the outcomes of transport policy innovations and to increasing demands for accurate predictions to feed into political decision-making.

The role of politics in setting the background to prediction should not be overlooked. Although the aims of the prediction process, when viewed from within, are primarily technical, attempting to ensure that resources are used to best effect to improve the transport system, from the outside the role that prediction plays in decision-making is sometimes rather less clear cut. Demonstrating that a proposed scheme will broadly achieve the objectives used to justify its implementation is always likely to be a desirable prerequisite in a democratic society. But, the importance of being able to do so may vary dependent on issues such as the nature of the scheme, the ability of the prevailing approaches to provide accurate predictions and the range of other reasons that may influence the final decision on whether or not to implement. In the case of road pricing, a decision to commit resources to implementing a scheme is not irreversible to the extent that is the case when, for example, building a new road. If a road pricing scheme does not produce the desired benefits it can be modified or withdrawn. In addition, it is clear that there are known to be significant issues of uncertainty which may affect ability to produce accurate predictions. So, it follows that the main motivation for placing high emphasis on prediction (as shown by the UNAQ results), is the perception of policymakers that as much evidence as possible of the benefits of road pricing is required in order to gain sufficient acceptance from politicians and a sceptical public. The initial implementation of road pricing is essentially a short-term political risk rather than a long-term planning commitment. The differences that have been observed in approaches to prediction during the PRoGRESS project between UK and Italian cities, and the role that prediction has played in Stockholm, point to significant variations in the political decision-making culture within the EU that affect the prediction activities undertaken and attitudes towards outputs.

Questions that frequently arise when prediction is discussed include whether it may have become over-reliant on complex models, whether the nature of the outputs (including levels of coverage and accuracy) are fully understood by all those who use them and whether it is worth the cost in terms of time and money. There are obvious attractions in using simple models that are easy to understand and relatively cheap to implement. There is also likely to be an important relationship between the revealed accuracy of model predictions and the levels of trust that people are prepared to place in future modelling activities. Where well founded doubts exist over the ability of models (or other prediction approaches) to produce reliable outputs for particular indicators, it is potentially unwise to attempt to estimate them in that way as it may only reduce respect for the prediction process in the longer term. In Stockholm, model-based predictions for the road pricing trial were generally not trusted and turned out to be more accurate than expected. On the other hand, each proposal to implement a road pricing scheme represents an opportunity to acquire data to improve understanding of the processes at work leading to more accurate predictions. However, as already argued, external political factors may ultimately dictate the nature of prediction activities undertaken and expectations about the uses of outputs.