Accounting for uncertainties in cost-benefit analyses of road projects: A procedure illustrated by real-world projects

A critical step in the road investment decision-making process is to conduct cost-benefit analyses (CBAs) to assess the net social benefit of project alternatives. CBA results are often presented to decision-makers as point estimates. However, such results rely on numerous forecasts, parameters, and unit values, which introduce uncertainty. This paper adds to the literature on transport CBAs by evaluating how their inherent uncertainty can be considered by analysts and presented to decision-makers. We develop a Monte Carlo simulation procedure that uses variations in the most critical factors related to CBAs. We apply this procedure to a set of projects to demonstrate how it could inform decision-makers in terms of the uncertainties involved in CBAs. We show that if analysts present ranges or confidence intervals, decision-makers may make other choices that differ from those based on point estimates. • Addresses uncertainty in transport CBAs via Monte Carlo simulations • Framework assesses uncertainties in traffic, costs, and model assumptions • Demonstrates a method to present CBA results as a range • Examines the probability of negative NPVs in road projects • Highlights limitations of single-point estimates in CBA metrics