Road agencies set quantitative targets and adopt related road safety strategics in accordance with each agency's priorities and available resources. Benefit-cost analyses are carried out to support the decision-making process, and alternative measures are ranked according to their expected benefits and benefit-cost ratios, calculated with a safety performance function (SPF) and crash modification factors (CMFs) as predictors of future safety performance. Because of the variance in CMFs and crash frequency, it is uncertain what the benefits of some future actions will be. The chance of making a wrong decision depends on the standard deviatioas of the probability distributions of the CMFs and the SPF. To deal with the inherent uncertainty in the decision-making process, a reliability-based assessment of the benefits must be performed to introduce a stochastic approach. In this paper, the variances in the CMFs and SPFs are taken into account in a reliability-based benefit-cost analysts to address the improvements and issues associated with an accurate probabilistic approach when compared with deterministic results or other approximated procedures. A case study is presented that compares safety countcrmcasurcs selected to reduce crash frequency and severity on sharp curves on freeways. These measures include delineation systems, shoulder rumble strips, and retrofitted safety barriers, individually and in combination. Monte Carlo simulations were applied to calculate the probability of the failure of the benefit-cost analysts statements. The results and comparisons with alternative approaches, such as the approach proposed in the Highway Safety Manual, arc presented and show remarkable differences in the evaluation outcomes

Reliability-based assessment of Benefits in roadway safety management

CAFISO, Salvatore;
2015

Abstract

Road agencies set quantitative targets and adopt related road safety strategics in accordance with each agency's priorities and available resources. Benefit-cost analyses are carried out to support the decision-making process, and alternative measures are ranked according to their expected benefits and benefit-cost ratios, calculated with a safety performance function (SPF) and crash modification factors (CMFs) as predictors of future safety performance. Because of the variance in CMFs and crash frequency, it is uncertain what the benefits of some future actions will be. The chance of making a wrong decision depends on the standard deviatioas of the probability distributions of the CMFs and the SPF. To deal with the inherent uncertainty in the decision-making process, a reliability-based assessment of the benefits must be performed to introduce a stochastic approach. In this paper, the variances in the CMFs and SPFs are taken into account in a reliability-based benefit-cost analysts to address the improvements and issues associated with an accurate probabilistic approach when compared with deterministic results or other approximated procedures. A case study is presented that compares safety countcrmcasurcs selected to reduce crash frequency and severity on sharp curves on freeways. These measures include delineation systems, shoulder rumble strips, and retrofitted safety barriers, individually and in combination. Monte Carlo simulations were applied to calculate the probability of the failure of the benefit-cost analysts statements. The results and comparisons with alternative approaches, such as the approach proposed in the Highway Safety Manual, arc presented and show remarkable differences in the evaluation outcomes
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11769/17911
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