Guidelines for the Development and Application of Crash Modification Factors (2022) / Chapter Skim
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Pages 339-360
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F-1 This appendix was contributed by Dr. Ezra Hauer upon request by the Project 17-63 team.
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F-2 Crash Modification Factors (CMFs) allow one to anticipate the safety effect of manipulations (interventions, design changes, etc.)
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F-3 CMFs are essential for evidence-based practice. Unfortunately, CMF estimates are at times not well trusted.
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F-4 Guidelines for the Development and Application of Crash Modification Factors tried to detect the safety effect of PMR in four ways, and one study used monthly time series data about crashes and of PMRs to determine how safety changes as retroreflectivity diminishes. Taken together these studies did not lead to consensus and the question asked was why.
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F-5 According to the Wikipedia entry on randomized experiments: "In science, randomized experiments are the experiments that allow the greatest reliability and validity of statistical estimates of treatment effects." Most will agree that if in road safety research experimentation and randomization were feasible, the resulting CMFs should be deemed trustworthy and, as a result, practitioners could make more cost-effective choices. But most are also inclined to think, perhaps out of habit and without reflection, that in our line of work there is no opportunity to randomize, no control over which unit gets what treatment.
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F-6 Guidelines for the Development and Application of Crash Modification Factors unpublished and useless data .
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Enhancing Future CMF Research F-7 "based on an anticipated reduction in accidents and fatalities" (p.
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F-8 Guidelines for the Development and Application of Crash Modification Factors I said earlier that randomized experiments should be conducted because they promise to lead to consensus about the safety effect of manipulations. Here I will argue that opportunities to conduct randomized experiments about the safety effect of manipulations still do exist.
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Enhancing Future CMF Research F-9 by experts, and I am not an expert. We can perhaps learn from medicine, where the conduct of randomized experiments is common and similar issues have already received thought and examination (Friedman et al.
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F-10 Guidelines for the Development and Application of Crash Modification Factors task is to determine how to get organized so that randomized experiments assume their proper role in road safety management. I noted earlier that while only an operating agency can organize randomized experiments, it is not its mandate to engage in research the results of which are of benefit to many other operating agencies.
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F-11 There are, of course, circumstances in which randomization is not practical or ethical and where information about the safety effect of manipulations must be extracted from observational data. While doing so may be difficult, it is possible; one has to be clever about it.
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F-12 Guidelines for the Development and Application of Crash Modification Factors to account for the effect on the PoI of the change nuisance influences. The second prototype was also a laboratory experiment except that the control over the source of data was diminished; because some nuisance influences did change, a correction had to be applied for the effect of this change on the PoI to be accounted for.
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Enhancing Future CMF Research F-13 direction) and therefore cannot be studied using before-after data.
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F-14 Guidelines for the Development and Application of Crash Modification Factors Exploiting Cross-Section Data II: Alternative Approaches to Modeling In road safety research the most common way to extract CMFs from cross-section data is by fitting to it a single equation. The limitations of this approach were discussed in the companion report.
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Enhancing Future CMF Research F-15 To tease out of such a causal diagram the safety effect PMR one might have to split "enforcement" from "maintenance" and link the latter by causal arrows directly to the crash frequency and severity node and to the speed distribution node. Should and could the direct and indirect influences by separated?
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F-16 The aim of CMF research is to determine what change in safety is caused by manipulations.22 To determine what change in safety is caused by the manipulation of some trait of interest, one must compare what safety was with manipulation to what safety would have been at the same time but without the manipulation. What safety was with the manipulation is based on observation, measurement, and statistical estimation.
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Enhancing Future CMF Research F-17 One might assume that the question of "how best to predicts what would have been" was, over the years, a prominent theme of road safety research. This does not seem to be the case.
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F-18 The task of NCHRP Project 17-63 was to suggest ways for research to produce trustworthy and transferable CMFs. The hope is that when CMFs are trustworthy and transferable, they will have an easier time to percolate into practice.
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F-19 In the companion report (NCHRP Research Report 991, Appendix G: Developing Consensus in Research about the Safety Effect of Manipulations) , I argue that continued attempts to extract reliable CMFs by fitting single-equation models to cross-section data are unlikely to bring about consensus, and that by attributing effect to the cause of interest, one strives to create conditions in which nuisance influences are minimized and well accounted for.
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F-20 Angrist, J
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Enhancing Future CMF Research F-21 Quaye, K
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