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Abstract
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The decision making process for safety interventions is complex, involving a number of actors (experts, public, politicians, etc.) and issues (environment, economy, mobility) competing for the scarce available resources. The risk of making poor decisions and the cost of making better decisions can be reduced by the use of reliable studies on how effective different safety measures are (OECD, 2012). In this framework, Road Agencies set specific quantitative safety targets and adopt related road safety strategies towards these targets, within the established priorities and the available resources. In particular, benefit-cost analyses are carried out in a more or less systematic way to maximize results within the limited funds that are available at a time of economic crisis.
Benefit-Cost analysis (BCA) aims at comparing the benefits and costs of different policy alternatives, measured in monetary units. Measures for which benefits are greater than costs are called cost-effective, and ranked according to their benefit-cost ratio.
The BCA requires basically 3 different estimates:
1. An estimate of the safety problem, i.e. crash number and severity basing on crash history and/or Safety Performance Function.
2. An estimate of the effectiveness, i.e. Crash Modification Factors (CMFs) of road safety measures identified for solving the safety problem.
3. An estimate of the life cycle cost of each measure.
The most important uncertainties involved in developing such assessment process concern the adoption of appropriate values for the safety effects of road safety measures.
Scientific accuracy is difficult to obtain in the field of CMFs, not only because several assumptions are necessary in the process but also because it is very difficult to separate the safety effect of a measure from the effect of several other microscopic or macroscopic measures and phenomena (including statistical randomness) occurring at the same place. Two main issues affect the reliability of CMFs: accuracy and transferability. The former factor pertains to the data quality, the small sample size, the bias and confounding factors not eliminated. The latter factor has to do with the fact that the CMF estimates come from studies conducted in differing circumstances which were not directly correlated to the CMF value by the way of a function. Hauer et al. (2012) described how important is the site-to-site variability inasmuch, along with the uncertainties inherent in the estimation, the site-to-site variability is able to considerably increase the value of the variance. Moreover, it is necessary to assess whether the studies can be generalized in time and space (external validity of research), e.g. from one country to another or from one decade to another, showing the consistency in time and space of studies that have evaluated the effects of road safety measures (Shadish et al., 2002).
A framework for interpreting road safety evaluation studies in theoretical terms has been proposed by Elvik, 2004. This framework is a conceptual scheme that can be used to develop arguments for or against the general validity of road safety studies. Cumulative meta-analysis is well suited for assessing external validity based on the range of replications (Elvik et al., 2009), but the applicability of the technique is likely to be limited and it can be applied to assess external validity when a large number of studies have been reported during a long period of time ...
Language: en |