Road Agencies set quantitative targets and adopt related road safety strategies within the priorities and the available resources at the time of an economic crisis. In this framework, benefit-cost analyses (BCA) are carried out to support the decision making process and alternative measures are ranked according to their expected benefit and benefit-cost ratio calculated using a Safety Performance Function (SPF) and Crash Modification Factors (CMFs) as predictors of future safety performances. Due to the variance of CMFs and crash frequency we are uncertain what the benefits of some future actions will be. The chance of making wrong decisions depends on the size of the standard deviation of the probability distribution of the considered stochastic variables. To deal with the uncertainty inherent in the decision making process, a reliability based assessment of benefits must be performed introducing a stochastic approach. In the paper the variability of the CMFs, the predicted number of crashes and the crash costs are taken into account in a reliability based BCA to address improvements and issues of an accurate probabilistic approach when compared to the deterministic results or other approximated procedures. A case study is presented comparing different safety countermeasures selected to reduce crash frequency and severity on sharp curves in motorways. These measures include retrofitting of old safety barriers, delineation systems and shoulder rumble strips. The methodology was applied using the Monte Carlo simulations to calculate the probability of failure of BCA statements. Results and comparisons with alternative approaches, like the one proposed in the HSM, are presented showing remarkable differences in the evaluation of outcomes which can be achieved.
Keywords: Benefit-cost analysis; Crash modification factor; Montecarlo simulation; Reliability analysis; Road safety; Safety performance function; Stochastic analysis.
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