Traditionally, Danish transport investment decisions are based on cost-benefit analysis (CBA) converting the impacts into monetary units such as pollutants, accidents, time savings etc. The virtues (pros) of a project are set against the deficiencies (cons) of the project leading to a set of investment criteria that can be estimated. However, these deterministic single point output criteria are based upon “best guess” estimates of each input variable to the model. Thus, the CBA depicts more of a most likely value of the transport assessment scheme than the actual value. Therefore it is relevant to treat issues of uncertainty relating to CBA and transport evaluation.
In the recent five years two approaches have been developed which can deal with this task. One is the Reference Class Forecasting (RCF) technique, which deals with uncertainty issues by applying uplift factors based on defined reference classes as concerns different projects. The theoretical background is made up by prospect theory developed by Kahneman & Tversky (1979). Another approach has recently been developed (November 2008) as part of a PhD study by Kim Bang Salling at DTU applying Monte Carlo simulation and estimated probability distribution functions. On the basis of his recent stay at Oxford University with Professor David Banister a major development possibility has been identified by merging the two approaches. A satisfactory outcome would be of utmost importance for both theoretical and practical treatment of transport evaluation issues.