Reference Scenario Forecasting: A New Approach to Transport Project Assessment

This paper presents a new approach to transport project assessment in terms of feasibility risk assessment and reference class forecasting. Normally, transport project assessment is based upon a cost-benefit approach where evaluation criteria such as net present values are obtained. Recent research has however proved that substantial inaccuracies are present when obtaining the monetary input to the cost-benefit analysis, particularly as concerns the construction costs and demand forecasts. This paper proposes a new approach in order to assess these biases in a so-called reference scenario forecasting (RSF) frame. The RSF is anchored in the cost-benefit analysis (CBA), thus, it provides decision-makers with a quantitative mean of assessing the transport infrastructure project. First, the RSF method introduces uncertainties within the CBA by applying Optimism Bias uplifts on the preliminary construction cost estimates. Hereafter, a quantitative risk analysis is provided making use of Monte Carlo simulation. This stochastic approach facilitates random input parameters based upon reference class forecasting, hence, a parameter data fit has been performed in order to obtain validated probability distribution functions. The latter have been placed and ultimately simulated on the inaccuracies of determining demand forecasts, i.e. leading to the travel time savings and ticket revenues of the project. Finally, RSF makes use of scenario forecasting where trend scenarios such as economic growth and level of cross-border integration are investigated. The latter was relevant as RSF is demonstrated by a case example concerning the fixed link between Elsinore (Denmark) and Helsingborg (Sweden) and calculations are performed in the CBA-DK decision support model.