Guidelines for the Development and Application of Crash Modification Factors (2022) / Chapter Skim
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Pages 361-398
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From page 361... ...
G-1 This appendix was contributed by Dr. Ezra Hauer upon request by the Project 17-63 team.
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G-2 Introduction Crash modification factors (CMFs) allow one to anticipate the safety effect of manipulations (interventions, design changes, etc.)
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G-3 Review of Past Research About the Safety Effect of Pavement Marking Retroreflectivity In 2007, the nationwide annual pavement marking expenditure was estimated to be approximately $2 billion (Carlson et al.
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G-4 Guidelines for the Development and Application of Crash Modification Factors In Stage 2, two PMR predictor variables were added to the ‘base model' as multipliers: exp(White PMR × β_White) and exp(Yellow PMR × β_Yellow)
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Developing Consensus in Research About the Safety Effect of Manipulations G-5 Data about retroreflectivity and crashes was used for two periods: April and May and September and October.4 Restriping in Michigan is done mostly during June and August. It follows that low PMR values occur mostly in the April–May period (before restriping)
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G-6 Guidelines for the Development and Application of Crash Modification Factors PMRYellow < 225 mcd/m2/lx) , the larger PMRWhite is, the more target crashes are predicted by the model.
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Developing Consensus in Research About the Safety Effect of Manipulations G-7 With model development concluded, Avelar and Carlson (2014) asserted that the part of the model equation contains the two PMR predictor variables; i.e., the product exp((Sum − 325)
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G-8 Guidelines for the Development and Application of Crash Modification Factors factors (traffic, climate, etc.) that are different in the April-May and the September-October periods.
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Developing Consensus in Research About the Safety Effect of Manipulations G-9 and relationships are given action-oriented causal meaning in the Conclusions section (p.
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G-10 Guidelines for the Development and Application of Crash Modification Factors Sum predictor variable was not statistically significant, its value was reported and, moreover, the same predictor variable and parameter estimate was then used in their CMF. In contrast, Carlson et al.
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Developing Consensus in Research About the Safety Effect of Manipulations G-11 PMRs a statistically significant positive β. Other researchers using data from different states also typically found a mixture of positive and negative β estimates.
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G-12 Guidelines for the Development and Application of Crash Modification Factors the PO framework is treatment of observational data as coming from a randomized experiment if for each unit (road segment in the PMR context) one knew the probability of its being treated or being left as a control.20 This probability is called the propensity score.21 The propensity score is assumed to depend on the traits of units and is estimated using their known traits (predictor variables)
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Developing Consensus in Research About the Safety Effect of Manipulations G-13 The PMR ranges for the three categories are in Footnote 2. The implication of the evident dose-response relationship is that the higher the PMR the lesser is the probability of target accident29 occurrence.
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G-14 Guidelines for the Development and Application of Crash Modification Factors In regression models, ADT usually accounts for about 80% of the variance. Researchers attempted to make the counterfactual safety prediction independent of the amount of traffic volume.
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Developing Consensus in Research About the Safety Effect of Manipulations G-15 differences in road safety (e.g., see Roess et al.
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G-16 Guidelines for the Development and Application of Crash Modification Factors requires various assumption-based imputations to be made. Here, in contrast, at each of the 19 sites (182 miles, 313 target crashes)
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Developing Consensus in Research About the Safety Effect of Manipulations G-17 only to PMRs larger than about 100 mcd/m2/lux. In this range, four of five regression parameters indicate the larger the PMR, the fewer the nighttime target crashes; the one remaining parameter indicates to the contrary.
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G-18 Guidelines for the Development and Application of Crash Modification Factors predictor variables: vehicle miles traveled,44 line type, direction, road type,45 route number, and crash information. These were used in a logistic regression46 model of the form P crash P crash exp x xn n1 .
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Developing Consensus in Research About the Safety Effect of Manipulations G-19 In Figure G7 (and in all other parameter estimate tabulations in Smadi et al.
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G-20 Guidelines for the Development and Application of Crash Modification Factors The question whether single-equation regressions built from cross-section data can be used to predict the effect of manipulations51 as discussed under Issue 4. However, the Smadi et al.
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Developing Consensus in Research About the Safety Effect of Manipulations G-21 When road type, VMT, and marking color were used but only records with retroreflectivity below 200 mcd/m2/lx were used, the results in Figure G8 were obtained. The corresponding estimates from Smadi et al.
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G-22 Guidelines for the Development and Application of Crash Modification Factors The β estimates in Figures G8 and G9, significant or not, are often different. Thus, with three years of data in Smadi et al.
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Developing Consensus in Research About the Safety Effect of Manipulations G-23 and finally, only single-vehicle loss of control/run off road type crashes will be considered" (p. 18)
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G-24 Guidelines for the Development and Application of Crash Modification Factors As in (a)
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Developing Consensus in Research About the Safety Effect of Manipulations G-25 target accidents55 and about how long it has been since the most recent repainting. Models were developed to predict the decline in PMR as a function of time since last repainting.56 The PMR (by color and function)
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G-26 Guidelines for the Development and Application of Crash Modification Factors Figure G10. Tables 61 and 62 from Bahar et al.
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Developing Consensus in Research About the Safety Effect of Manipulations G-27 input from participants of FHWA-sponsored workshops (as discussed later in this document) and developed proposed minimum maintained pavement marking retroreflectivity levels for the MUTCD." To document the No Effect finding by Bahar et al.
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G-28 Guidelines for the Development and Application of Crash Modification Factors The last limitation that CPA note is that in the California data, the PMR is seldom less than 100 mcd/m2/lx.63 For yellow markings, the leftmost bins contain retroreflectivities of 15–79 mcd/ m2/lx a. The reverse is also true.
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Developing Consensus in Research About the Safety Effect of Manipulations G-29 PMRYellow, increasing PMRWhite will cause an increase in crash frequency. Using the same data as Avelar and Carlson (2014)
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G-30 Ways to Determine the Effect of a Cause Chapter 1 is a review of several studies that tried to determine how changing the retroreflectivity of pavement markings is likely to affect the expected frequency of target accidents. The studies are of either the Observational Cross-Section or of the Observational Before-After kind.
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Developing Consensus in Research About the Safety Effect of Manipulations G-31 Figure G13. The essential comparison.
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G-32 Guidelines for the Development and Application of Crash Modification Factors The "at the same time" phrase makes it clear that PoI-related traits unaffected by the manipulation are the same when the What Was and the What Would Have Been are compared. The need to keep these traits unchanged or to correct for what change did occur is a nuisance.
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Developing Consensus in Research About the Safety Effect of Manipulations G-33 one way and another differently. These reasons cannot be easily considered in the construction of comparison groups nor the determination of propensity scores.
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Abbreviations and acronyms used without denitions in TRB publications: A4A Airlines for America AAAE American Association of Airport Executives AASHO American Association of State Highway Officials AASHTO American Association of State Highway and Transportation Officials ACI–NA Airports Council International–North America ACRP Airport Cooperative Research Program ADA Americans with Disabilities Act APTA American Public Transportation Association ASCE American Society of Civil Engineers ASME American Society of Mechanical Engineers ASTM American Society for Testing and Materials ATA American Trucking Associations CTAA Community Transportation Association of America CTBSSP Commercial Truck and Bus Safety Synthesis Program DHS Department of Homeland Security DOE Department of Energy EPA Environmental Protection Agency FAA Federal Aviation Administration FAST Fixing America's Surface Transportation Act (2015) FHWA Federal Highway Administration FMCSA Federal Motor Carrier Safety Administration FRA Federal Railroad Administration FTA Federal Transit Administration GHSA Governors Highway Safety Association HMCRP Hazardous Materials Cooperative Research Program IEEE Institute of Electrical and Electronics Engineers ISTEA Intermodal Surface Transportation Efficiency Act of 1991 ITE Institute of Transportation Engineers MAP-21 Moving Ahead for Progress in the 21st Century Act (2012)
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Transportation Research Board 500 Fifth Street, NW Washington, DC 20001 ADDRESS SERVICE REQUESTED ISBN 978-0-309-68686-0 9 7 8 0 3 0 9 6 8 6 8 6 0 9 0 0 0 0
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