Research, Development, and Technology

Turner-Fairbank Highway Research Center

6300 Georgetown Pike

McLean, VA 22101-2296

Safety Evaluation of Wet Reflective 

Pavement Markers

PUBLICATION NO. FHWA-HRT-15-065 OCTOBER 2015
FOREWORD

The research documented in this report was conducted as part of the Federal Highway 

Administration’s (FHWA) Evaluation of Low-Cost Safety Improvements Pooled Fund Study 

(ELCSI–PFS). FHWA established this pooled fund study in 2005 to conduct research on the 

effectiveness of the safety improvements identified by the National Cooperative Highway 

Research Program Report 500 guides as part of implementation of the American Association of 

State Highway and Transportation Officials Strategic Highway Safety Plan. The ELCSI-PFS 

research provides a crash modification factor and benefit-cost economic analysis for each of the 

targeted safety strategies identified as priorities by the pooled fund member States.

The wet-reflective pavement markings evaluated in this study are intended to reduce the 

frequency of crashes by improving the level of retroreflectivity during wet-road conditions. 

Geometric, traffic, and crash data were obtained for freeway sections in Minnesota, North 

Carolina, and Wisconsin; treated two-lane rural road locations in Minnesota; and treated 

multilane road sections in Wisconsin. For freeways, the combined results for all States indicate 

reductions in crashes for injury and wet-road crashes. For multilane roads, significant reductions 

were estimated for total crashes, injury crashes, run-off-road crashes, wet-road crashes, and 

nighttime crashes. The results suggest that the treatment, even with conservative assumptions 

about cost, service life, and the value of a statistical life, can be cost effective.

Monique R. Evans, P.E.

Director, Office of Safety

Research and Development

Notice

This document is disseminated under the sponsorship of the U.S. Department of Transportation 

in the interest of information exchange. The U.S. Government assumes no liability for the use of 

the information contained in this document.

The U.S. Government does not endorse products or manufacturers. Trademarks or 

manufacturers’ names appear in this report only because they are considered essential to the 

objective of the document.

Quality Assurance Statement

The Federal Highway Administration (FHWA) provides high-quality information to serve 

government, industry, and the public in a manner that promotes public understanding. Standards 

and policies are used to ensure and maximize the quality, objectivity, utility, and integrity of its 

information. FHWA periodically reviews quality issues and adjusts its programs and processes to 

ensure continuous quality improvement.
TECHNICAL DOCUMENTATION PAGE

1. Report No.

FHWA-HRT-15-065

2. Government Accession No. 3. Recipient’s Catalog No.

4. Title and Subtitle

Safety Evaluation of Wet-Reflective Pavement Markings

5. Report Date

September 2015

6. Performing Organization Code

7. Author(s) 

Craig Lyon, Bhagwant Persaud, and Kimberly Eccles

8. Performing Organization Report No.

9. Performing Organization Name and Address 10. Work Unit No.

Vanasse Hangen Brustlin, Inc. (VHB)

8300 Boone Blvd., Ste. 700

Vienna, VA 22182-2626

Persaud Lyon, Inc.

87 Elmcrest Road

Toronto, Ontario M9C 3R7

11. Contract or Grant No.

DTFH61-13-D-00001

12. Sponsoring Agency Name and Address

U.S. Department of Transportation

Federal Highway Administration 

1200 New Jersey Avenue, SE

Washington, DC 20590

13. Type of Report and Period

Safety Evaluation

14. Sponsoring Agency Code: FHWA

15. Supplementary Notes. The Federal Highway Administration Office of Safety Research and Development 

Contract Task Order Manager was Roya Amjadi. The project team members were Craig Lyon, Dr. Bhagwant 

Persaud, and Kimberly Eccles.

16. Abstract

The Federal Highway Administration organized a pooled fund study of 38 States to evaluate low-cost safety 

strategies as part of its strategic highway safety effort. One of the strategies selected for evaluation was the 

application of wet-reflective pavement markings. This strategy involves upgrading existing markings from standard 

marking materials to wet-reflective markings applied as a paint, tape, or thermoplastic material. The purpose was to 

provide an improved level of retroreflectivity in wet-road conditions. Geometric, traffic, and crash data were 

obtained for treated freeway sections in Minnesota, North Carolina, and Wisconsin; treated two-lane rural road 

locations in Minnesota; and treated multilane road sections in Wisconsin. To account for potential selection bias 

owing to regression-to-the-mean, an Empirical Bayes (EB) before–after analysis was conducted. The analysis also 

controlled for changes in traffic volumes over time and time trends in crash counts unrelated to the treatment. 

Intersection-related, snow/slush ice, and animal crashes were excluded from the analysis. For freeways, the 

combined results for all States indicated reductions in crashes that are statistically significant at the 95-percent

confidence level for injury and wet-road crashes, with estimated crash modification factors (CMFs) of 0.881 and 

0.861, respectively. For multilane roads, statistically significant reductions were estimated for total crashes 

(CMF = 0.825), injury crashes (CMF = 0.595), run-off-road crashes (CMF = 0.538), wet-road crashes

(CMF = 0.751), and nighttime crashes (CMF = 0.696). For two-lane roads, the sample of crashes was too small to 

detect an effect with statistical significance for any of the crash types, but there were indications that the treatment 

had a safety benefit for wet-road crashes. Benefit–cost ratios estimated with conservative cost and service life 

assumptions were 1.45 for freeways and 5.44 for multilane roads. The results suggest that the treatment—even with 

conservative assumptions on cost, service life, and value of a statistical life—can be cost effective, especially for 

multilane roads.

17. Key Words: Wet-reflective pavement markings,

Low-cost, Safety improvements, Safety evaluations, 

Empirical Bayesian

18. Distribution Statement

No restrictions. This document is available through the 

National Technical Information Service

Springfield, VA 22161.

http://www.ntis.gov/about/contact.aspx

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this page)

Unclassified

21. No. of Pages: 

49

22. Price

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized.
ii
iii

TABLE OF CONTENTS

EXECUTIVE SUMMARY .......................................................................................................... 1

CHAPTER 1. INTRODUCTION................................................................................................ 3

BACKGROUND ON STRATEGY ......................................................................................... 3

BACKGROUND ON STUDY.................................................................................................. 3

LITERATURE REVIEW ........................................................................................................ 3

CHAPTER 2. OBJECTIVE......................................................................................................... 7

CHAPTER 3. METHODOLOGY............................................................................................... 9

CHAPTER 4. DATA COLLECTION....................................................................................... 13

MINNESOTA.......................................................................................................................... 13

Installation Data .................................................................................................................... 13

Reference Sites...................................................................................................................... 13

Roadway Data....................................................................................................................... 13

Traffic Data ........................................................................................................................... 13

Crash Data............................................................................................................................. 14

Treatment Cost Data ............................................................................................................. 14

NORTH CAROLINA............................................................................................................. 14

Installation Data .................................................................................................................... 14

Reference Sites...................................................................................................................... 14

Roadway Data....................................................................................................................... 14

Traffic Data ........................................................................................................................... 15

Crash Data............................................................................................................................. 15

Treatment Cost Data ............................................................................................................. 15

WISCONSIN........................................................................................................................... 15

Installation Data .................................................................................................................... 15

Reference Sites...................................................................................................................... 16

Roadway Data....................................................................................................................... 16

Traffic Data ........................................................................................................................... 16

Crash Data............................................................................................................................. 16

Treatment Cost Data ............................................................................................................. 17

DATA CHARACTERISTICS AND SUMMARY ............................................................... 17

CHAPTER 5. DEVELOPMENT OF SAFETY PERFORMANCE FUNCTIONS .............. 23

NORTH CAROLINA............................................................................................................. 23

WISCONSIN........................................................................................................................... 24

MINNESOTA.......................................................................................................................... 25

CHAPTER 6. BEFORE–AFTER EVALUATION RESULTS............................................... 29

AGGREGATE ANALYSIS ................................................................................................... 29

DISAGGREGATE ANALYSIS............................................................................................. 32

CHAPTER 7. ECONOMIC ANALYSIS.................................................................................. 35

CHAPTER 8. SUMMARY AND CONCLUSIONS................................................................. 37
iv

APPENDIX. ADDITIONAL INSTALLATION DETAILS FROM STATES...................... 39

RESPONSES FROM MINNESOTA .................................................................................... 39

RESPONSES FROM NORTH CAROLINA ....................................................................... 39

RESPONSES FROM WISCONSIN...................................................................................... 40

ACKNOWLEDGEMENTS ....................................................................................................... 41

REFERENCES............................................................................................................................ 43
v

LIST OF FIGURES

Figure 1. Equation. Estimated change in safety...............................................................................9

Figure 2. Equation. Empirical Bayes estimate of expected crashes ..............................................10

Figure 3. Equation. Empirical Bayes weight .................................................................................10

Figure 4. Equation. Index of effectiveness ....................................................................................10

Figure 5. Equation. Standard deviation of index of effectiveness.................................................10

Figure 6. Equation. Calculation for treatment costs in North Carolina .........................................15

Figure 7. Equation. Form of SPFs for North Carolina...................................................................23

Figure 8. Equation. Form of SPFs for Wisconsin..........................................................................24

Figure 9. Equation. Form of SPFs for Minnesota..........................................................................25

Figure 10. Equation. Freeway calculation .....................................................................................35

Figure 11. Equation. Multilane calculation....................................................................................36

LIST OF TABLES

Table 1. Definitions of crash types by State ..................................................................................18

Table 2. Data summary for treatment sites....................................................................................19

Table 3. Data summary for reference sites ....................................................................................21

Table 4. North Carolina freeway SPFs..........................................................................................24

Table 5. Wisconsin freeway SPFs .................................................................................................25

Table 6. Wisconsin multilane divided SPFs..................................................................................25

Table 7. Minnesota freeway SPFs .................................................................................................26

Table 8. Minnesota two-lane undivided SPFs ...............................................................................26

Table 9. Results for North Carolina freeways ...............................................................................29

Table 10. Results for Wisconsin freeways.....................................................................................30

Table 11. Results for Wisconsin multilane roads ..........................................................................30

Table 12. Results for Minnesota two-lane roads ...........................................................................31

Table 13. Results for Minnesota freeways.....................................................................................31

Table 14. Results for combined States freeways...........................................................................32

Table 15. Economic analysis results..............................................................................................36

Table 16. Recommended CMFs and standard errors.....................................................................37
vi

LIST OF ABBREVIATIONS

AADT Average Annual Daily Traffic 

B/C Benefit–Cost

CMF Crash Modification Factor

DCMF Development of Crash Modification Factors

EB Empirical Bayes 

ELCSI–PFS Evaluation of Low-Cost Safety Improvements Pooled Fund Study

FHWA Federal Highway Administration 

HSIS Highway Safety Information Service

KABCO Scale used to represent injury severity in crash reporting (K is fatal injury, A is 

incapacitating injury, B is non-incapacitating injury, C is possible injury, and O is 

property damage only)

MnDOT Minnesota Department of Transportation

NCDOT North Carolina Department of Transportation

PDO Property Damage Only

SPF Safety Performance Function 

USDOT U.S. Department of Transportation

WisDOT Wisconsin Department of Transportation
1

EXECUTIVE SUMMARY

The Federal Highway Administration (FHWA) established the Development of Crash 

Modification Factors (DCMF) program in 2012 to address highway safety research needs for 

evaluating new and innovative safety strategies (improvements) by developing reliable 

quantitative estimates of their effectiveness in reducing crashes. The ultimate goal of the DCMF 

program is to save lives by identifying new safety strategies that effectively reduce crashes and to 

promote those strategies for nationwide implementation by providing measures of their safety 

effectiveness and benefit–cost (B/C) ratios through research. State transportation departments and 

other transportation agencies need to have objective measures for safety effectiveness and B/C 

ratios before investing in new strategies for statewide safety improvements. Thirty-eight State 

transportation departments provide technical feedback on safety improvements to the DCMF 

program and implement new safety improvements to facilitate evaluations. These States are 

members of the Evaluation of Low-Cost Safety Improvements Pooled Fund Study (ELCSI-PFS), 

which functions under the DCMF program.

One of the strategies selected for evaluation for this study was the application of wet-reflective 

pavement markings. This strategy involves upgrading existing markings from standard marking 

materials to wet-reflective markings applied as a paint, tape, or thermoplastic material. These 

markings are designed to provide an improved level of retroreflectivity during wet-road surface 

conditions. A literature review found that although there was some cross-sectional research 

relating retroreflectivity levels to crashes, there was no published research evaluating the effect 

on crashes after applying wet-reflective markings. 

Geometric, traffic, and crash data were obtained for treated freeway sections in Minnesota, North 

Carolina, and Wisconsin; treated two-lane rural road locations in Minnesota; and treated 

multilane road sections in Wisconsin. To account for potential selection bias owing to regressionto-the-mean, an Empirical Bayes (EB) before–after analysis was conducted using reference 

groups of untreated road sections with similar characteristics to the treated sites. The analysis also 

controlled for changes in traffic volumes over time and time trends in crash counts unrelated to 

the treatment. The evaluation was done for the following crash types: total, injury, side-swipe 

same direction, run-off-road, wet-road, wet-road nighttime crashes, and all nighttime crashes.

None of these crash types considered intersection-related, snow/slush ice, or animal crashes.

For freeways, the combined results for all States indicated reductions in crashes that were

statistically significant at the 95-percent confidence level for injury and wet-road crashes, with 

estimated crash modification factors (CMFs) of 0.881 and 0.861, respectively. For multilane 

roads, statistically significant reductions were estimated for total crashes (CMF = 0.825), injury 

crashes (CMF = 0.595), run-off-road crashes (CMF = 0.538), wet-road crashes (CMF = 0.751), 

and nighttime crashes (CMF = 0.696). For two-lane roads, the sample of crashes was too small to 

detect an effect with statistical significance for any of the crash types, but there were indications 

that the treatment had a safety benefit for wet-road crashes and overall. 

B/C ratios estimated with conservative cost and service life assumptions were 1.45 for freeways 

and 5.44 for multilane roads. With the U.S. Department of Transportation (USDOT)

recommended sensitivity analysis, these values could range from 0.83 to 2.04 for freeways and 
2

3.10 to 7.67 for multilane roads.(1) These results suggest that the treatment—even when making

conservative assumptions on cost, service life, and value of a statistical life—can be cost 

effective, especially for multilane roads.
3

CHAPTER 1. INTRODUCTION

BACKGROUND ON STRATEGY

Although policies vary by jurisdiction, most roadways with any significant volume of traffic have

edge lines, center lines, and in the case of multilane roadways, lane lines. These markings provide 

guidance to drivers on the intended vehicle path. The strategy investigated in this study involves 

upgrading these existing markings from standard marking materials to wet-reflective markings. 

Wet-reflective markings are designed to provide an improved level of retroreflectivity during wet 

road surface conditions. Wet-reflective markings can be applied as a paint, tape, or thermoplastic 

material.

Glass beads are normally used in pavement markings to reflect light from the headlights back to 

the driver. They work well when the road surface is dry, but when the surface is wet, the water 

can act like a mirror, reflecting light in a different direction and often creating glare. New

innovative pavement markings include both glass beads and ceramic elements that better reflect 

light back toward motorists to help them determine locations of driving lanes, edge lines, and 

merge indicators when they are dry or covered by a thin film of water.

(2)

BACKGROUND ON STUDY

FHWA established the DCMF program in 2012 to address highway safety research needs for 

evaluating new and innovative safety strategies (improvements) by developing reliable 

quantitative estimates of their effectiveness in reducing crashes. The ultimate goal of the DCMF 

program is to save lives by identifying new safety strategies that effectively reduce crashes and 

promote those strategies for nationwide implementation by providing measures of their safety 

effectiveness and B/C ratios through research. State transportations departments and other 

transportation agencies need to have objective measures for safety effectiveness and B/C ratios 

before investing in new strategies for statewide safety improvements. Thirty-eight State 

transportation departments provide technical feedback on safety improvements to the DCMF 

program and implement new safety improvements to facilitate evaluations. These States are 

members of the ELCSI-PFS, which functions under the DCMF program.

The use of wet-reflective pavement markings was selected as a strategy for evaluation as part of 

this effort. 

LITERATURE REVIEW

A literature review found no published research evaluating the effect on crashes of applying wetreflective markings. Limited research was available on the relationship between retroreflectivity 

and crashes in general.

Carlson et al. studied the relationship between crashes and the retroreflectivity readings of edge 

lines, lane lines, and center lines.(3) Crash types considered were nighttime crashes occurring 

outside of intersections or interchanges in the non-winter months of April to October. 

Wet/ice/snow-involved crashes were also excluded. Geometric and retroreflectivity data from 

Michigan spanning 2002 to 2008 for two-lane roads and freeways were matched to the crash 
4

data. The data were analyzed using negative binomial generalized linear models with blank 

retroreflectivity readings filled in using average degradation rates and/or information from the 

closest segment with a reading. For two-lane roads, the effect of yellow line retroreflectivity on 

nighttime crashes was significant at levels under 150 mcd/m2

/lx and showed crashes decreased as 

retroreflectivity increased. At higher values, there was no indication of further reduction in 

crashes. The effect of white edge lines also showed a statistically significant relationship with 

nighttime crashes and a reduction in crashes with increased retroreflectivity that was not 

dependent on the range of the level of retroreflectivity used in developing the model. For 

freeways, the results showed decreases in nighttime crashes when yellow or white edge 

reflectivity increased. For white lane lines, decreases in nighttime crashes were found when the 

data were limited to segments with readings less than 200 mcd/m2/lx.

Smadi et al. investigated the relationship between the retroreflectivity of pavement markings and 

nighttime run-off-road and cross center line crashes.

(4) Only rural roadways were considered. 

Logistic regression modeling was applied to assess the increased probability of a crash when 

retroreflectivity values were lower. The analysis did not find a correlation between 

retroreflectivity and crashes except when the level of retroreflectivity was below 200 mcd/m2

/lx. 

Below that level, an increased probability of a crash was found.

Donnell et al. investigated the relationship between pavement marking retroreflectivity and 

crashes in North Carolina.(5) First, an artificial neural network model was developed to predict the 

degradation of retroreflectivity over time. Then, the estimated retroreflectivity was combined 

with road inventory and crash data to estimate target crashes on a monthly basis using crosssectional regression models. Target crashes were those nighttime crashes related to visibility, 

including non-work-zone related, non-alcohol related, dry roadway, no roadway contributing 

circumstance, ran-off-road fixed-object off road, and sideswipe crashes. The results found that on 

multilane roads with increased retroreflectivity levels for white edge and lane lines, fewer crashes

occurred. Where yellow center lines were used, however, an increase in crashes resulted with 

higher levels of retroreflectivity. For two-lane roads, a decrease in crashes was found for both 

white edge lines and yellow center lines at higher levels of retroreflectivity, but these results were 

not statistically significant at the 90-percent confidence level. The findings also indicate that a 

measureable impact on crashes requires a significant change in retroreflectivity. For instance, a 

50-unit increase in the pavement marking retroreflectivity of white edge lines on multilane

roadways reduced the expected nighttime target crash frequency by approximately 18 percent. A 

50-unit increase in the pavement marking retroreflectivity of white skip lines on multilane 

highways decreased the expected nighttime target crash frequency by approximately 10 percent. 

Similarly, a 50-unit increase in the retroreflectivity of yellow edge lines on multilane highways 

was related to a 35-percent increase in the expected nighttime target crash frequency. Donnell 

et al. conducted a literature review of several studies but found no relationship between 

retroreflectivity levels and crashes.(5) However, those studies did not account for degradation in 

retroreflectivity over time.

Bahar et al. developed models of retroreflectivity based on age, color, marker type, climate 

region, and amount of snow removal.(6) These models were then used to evaluate the safety 

impact of retroreflectivity on nighttime non-intersection/interchange crashes using data from 

California. The results indicated no relationship between nighttime non-intersection/noninterchange crashes and retroreflectivity level. In reviewing the Bahar et al. report, Carlson et al. 
5

pointed out that California’s policy was to restripe higher volume roads up to three times a year 

with paint and every 2 years with thermoplastic markings.(3) Consequently, few roadways with 

significant volumes should reach retroreflectivity levels below 100 mcd/m2

/lx, as predicted by 

the Bahar et al. models applied. Carlson et al. also questioned the grouping of segments that had

very low retroreflectivity readings with other segments that had adequate levels of 

retroreflectivity in the analysis.

(3)

7

CHAPTER 2. OBJECTIVE

This research examined the safety impacts of wet-reflective markings in Minnesota, North 

Carolina, and Wisconsin. The objective was to estimate the safety effectiveness of this strategy as 

measured by crash frequency. Intersection-related, animal-related, and ice/snow/sleet-related

crashes were excluded. The following target crash types were considered: 

• Total crashes (all types and severities combined).

• Injury crashes (K, A, B, and C injuries on the KABCO scale, where K is fatal injury, A is 

incapacitating injury, B is non-incapacitating injury, C is possible injury, and O is 

property damage only).

• Run-off-road crashes (all severities combined).

• Head-on crashes (all severities combined).

• Sideswipe-opposite-direction crashes (all severities combined).

• Sideswipe-same-direction crashes (all severities combined).

• Wet-weather crashes (all types and severities combined).

• Nighttime crashes (all types and severities combined).

• Nighttime wet-weather crashes (all types and severities combined).

The effects for dry-road crashes, which were not specifically evaluated as a target crash type, 

were inferred from the effects for total and wet-road crashes.

A further objective was to address questions of interest such as the following:

• Do effects vary by roadway type?

• Do effects vary by level of traffic volumes?

• Do effects vary by posted speed limit?

• Do effects vary by horizontal curvature?

• Do effects vary by shoulder width?

• Do effects vary by lane width?

• Do effects vary by the site-specific expected crash frequency prior to treatment?
8

• What is the overall effect, measured by the economic costs of crashes by crash type and 

severity?

• What is the B/C value?

The evaluation of overall effectiveness included consideration of the installation costs and crash 

savings in terms of the B/C ratio. 

Meeting these objectives placed some special requirements on the data collection and analysis 

tasks, including the following:

• Select a large enough sample size to detect, with statistical significance, what may be 

small changes in safety for some crash types.

• Identify appropriate untreated reference sites.

• Properly account for changes in safety due to changes in traffic volume and other nontreatment factors.

• Pool data from multiple jurisdictions to improve reliability of the results and facilitate 

broader applicability of the products of the research.
9

CHAPTER 3. METHODOLOGY

The EB methodology for observational before–after studies was used for the evaluation. This 

methodology is considered rigorous in that it accounts for regression-to-the-mean using a 

reference group of similar but untreated sites. In the process, safety performance functions (SPFs)

were used to address the following issues:

• Overcoming the difficulties of using crash rates in normalizing for volume differences 

between the before and after periods.

• Accounting for time trends.

• Reducing the level of uncertainty in the estimates of safety effect.

• Properly accounting for differences in crash experience and reporting practice in 

amalgamating data and results from diverse jurisdictions.

The methodology also provided a foundation for developing guidelines for estimating the likely 

safety consequences of a contemplated strategy. The SPFs for roadways without wet-reflective 

markings were used with observed crash histories to estimate the number of crashes without 

treatment, and the CMFs developed were applied to this number to estimate the number without 

treatment.

In the EB approach, the estimated change in safety for a given crash type at a site is given by the 

equation in figure 1.

Figure 1. Equation. Estimated change in safety. 

Where:

= Expected number of crashes that would have occurred in the after period without the strategy.

= Number of reported crashes in the after period. 

In estimating , the effects of regression-to-the-mean and changes in traffic volume were

explicitly accounted for using SPFs, which relate crashes of different types to traffic flow and 

other relevant factors for each jurisdiction based on untreated sites (reference sites). Annual SPF 

multipliers were calibrated to account for temporal effects on safety (e.g., variation in weather, 

demography, and crash reporting).

In the EB procedure, the SPF was used to first estimate the number of crashes that would be 

expected in each year of the before period at locations with traffic volumes and other 

characteristics similar to the one being analyzed (i.e., reference sites). The sum of these annual 

SPF estimates (P) was then combined with the count of crashes (x) in the before period at a 

strategy site to obtain an estimate of the expected number of crashes (m) before the strategy was 

applied. This estimate of m was calculated using the equation in figure 2.

∆SSSSSSSSSSSS = λλ − ππ

λ

π

λ
10

Figure 2. Equation. Empirical Bayes estimate of expected crashes.

Where the EB weight, w, was estimated from the mean and variance of the SPF estimate using 

the equation in figure 3.

Figure 3. Equation. Empirical Bayes weight.

Where:

k = Constant for a given model and is estimated from the SPF calibration. 

In the SPF calibration process, a negative binomial distributed error structure was assumed with k

being the overdispersion parameter of this distribution.

A factor was then applied to m to account for the length of the after period and differences in 

traffic volumes between the before and after periods. This factor was the sum of the annual SPF 

predictions for the after period divided by P, the sum of these predictions for the before period. 

The result, after applying this factor, was an estimate of . The procedure also produced an 

estimate of the variance of .

The estimate of was then summed over all sites in a strategy group of interest (to obtain sum) 

and compared with the count of crashes observed during the after period in that group ( sum). The 

variance of was also summed over all sites in the strategy group. 

The index of effectiveness ( ) was estimated using the equation in figure 4.

Figure 4. Equation. Index of effectiveness.

The standard deviation of was given by the equation in figure 5.

Figure 5. Equation. Standard deviation of index of effectiveness.

λ

λ

λ λ

π

λ

θ





 



 +

=

2

( ) 1 sum

sum

sum

sum

Var

λ

λ

λ

π

θ

θ

2

2

2 2

2

( ) 1

( ) ( )

( )

















+

















+

=

sum

sum

sum

sum

sum

sum

Var

Var Var

StDev

λ

λ

λ

λ

π

π θ

θ
11

The percent change in crashes is calculated as 100(1 − ); thus a value of = 0.7 with a standard 

deviation of 0.12 indicates a 30-percent reduction in crashes with a standard deviation of 

12 percent.

θ θ

13

CHAPTER 4. DATA COLLECTION

Minnesota, North Carolina, and Wisconsin provided data containing locations and dates of the 

installation of wet-reflective markings. Reference sites were also identified in each State that

were similar to the treated sites in terms of traffic volumes and roadway geometry but had

standard lane markings. These States also provided roadway geometry, traffic volumes, crash 

data, and information on other construction activities for both installation and reference sites. 

This section summarizes the data assembled for the analysis.

MINNESOTA

Installation Data

The Minnesota Department of Transportation (MnDOT) provided a list of installations of wetreflective markings along two-lane roadways and freeways with the year of installation. Data for 

the installation year were excluded from the analysis. The total length of installations used for 

this study was 771 mi. The installation information included the route number, milepost, 

construction period and, where available, whether the markings were used on the center line, 

edge line, or lane lines. MnDOT applied 3MTM and Epoplex® wet-reflective products. 

No other construction activities were reported at these locations.

Reference Sites

Reference sites were identified by selecting two-lane roadways and freeways with characteristics

similar to the treated sites. Although no electronic record of pavement marking type was

available, MnDOT officials stated that unless the location was in the treatment site data, the 

markings would not be of a wet-reflective type.

Roadway Data

Roadway data were obtained from the Highway Safety Information Service (HSIS) for 2003 to 

2012. Roadway data included the following variables:

• Roadway class.

• Area type (urban/rural).

• Shoulder width.

• Shoulder type.

• Median width.

• Number of lanes.

Traffic Data

Traffic data were obtained from HSIS for 2003 to 2012 in the form of average annual daily traffic 

(AADT). 
14

Crash Data

Crash data were obtained from HSIS for 2003 to 2012, including many variables related to the 

location, time, and characteristics of each crash.

Treatment Cost Data

MnDOT provided estimated cost information of $8,500/mi for ground-in markings and 

$1,900/mi for striping. These costs are per center line mile and would be doubled to $17,000 and 

$3,800/mi, respectively, for the four-lane freeway locations.

MnDOT uses a recommended service life of 2 years for pavement markings.

NORTH CAROLINA

Installation Data

The North Carolina Department of Transportation (NCDOT) provided a list of projects that 

placed wet-reflective markings on the edge line and/or lane lines on urban and rural freeways. 

The total length of roadway installations used in this study was 95 mi. Among the data provided 

by the reports were the location (including district, State route number, and mileposts) and the 

construction dates. Data for the installation year were excluded from the analysis. The records 

also indicated whether the wet-reflective markings were applied as a restriping project or as part 

of a resurfacing project.

The following three products were applied:

• Glomarc® 90 manufactured by Epoplex®.

• LS90TM manufactured by Epoplex®.

• Liquid Pavement Marking Series 1200 manufactured by 3MTM.

No other construction activities were reported at these locations.

Reference Sites

NCDOT also provided a list of locations that received standard pavement markings, which was 

used to identify reference sites. The list was further reduced by including only those sites whose 

roadway class was indicated as being urban or rural freeway.

Roadway Data

Roadway inventory data were obtained from the HSIS for 1998 to 2012. Roadway data included 

the following variables:

• Area type (urban/rural).

• Functional class.

• Divided versus undivided.

• Median width.
15

• Terrain.

• Number of lanes.

• Surface width.

• Shoulder type.

• Shoulder width.

• Surface type.

• Speed limit.

Traffic Data

Traffic data were obtained from HSIS for 1998 to 2012 in the form of AADT and percentage of 

trucks. 

Crash Data

Crash data were obtained from HSIS for 1998 to 2012, including many variables related to the 

location, time, and characteristics of each crash.

Treatment Cost Data

NCDOT provided approximate installation costs of $1.10 and $2.25 per linear ft for polyurea and 

tape, respectively. The equation in figure 6 is the basis for calculating the cost:

Figure 6. Equation. Calculation for treatment costs in North Carolina. 

This cost includes marking the four edge lines with a constant application and the skip lines,

which cover a quarter of the pavement in marking material. A 4-lane road will have 2 skip lines, 

a 6-lane road will have 4 skip lines, an 8-lane road will have 6 skip lines, and a 10-lane road will 

have 8 skip lines.

Service life estimates provided by NCDOT are less than 5 years for tape and 8 years for sprayed 

polyurea with high initial retroreflectivity values.

WISCONSIN

Installation Data

The Wisconsin Department of Transportation (WisDOT) provided a list of projects in which wetreflective markings were placed on urban and rural freeways and multilane divided roadways. 

The total length of roadway installations used in this study was 300 mi. Among the data provided 

by the reports were the location (linkable to the WisDOT Metamanager data system) and the 

construction dates. Data for the installation year were excluded from the analysis.

WisDOT applied the following two wet-reflective marking products:

• 380WR marking tape manufactured by 3MTM.

Cost per mile = unit cost per foot × 5,280 × (4 + number of skip lines × 0.25)
16

• A380AW marking tape manufactured by 3MTM.

These markings were installed on the lane lines only. Approximately 50 of the 300 mi used for 

analysis had shoulder widening and/or shoulder rumble strips installed at the same time. These 

locations were mostly on freeways. No further construction was reported on these road segments 

by WisDOT during the study period.

Reference Sites

Reference sites were identified by using those locations that received the wet-reflective markings 

after the treatment sites used in this study. The sum of reference site mi was 341 mi.

Roadway Data

Roadway inventory data were obtained from the Metamanager system maintained by WisDOT

for 2003 to 2012. The data are bidirectional, meaning that unlike the data for Minnesota and 

North Carolina, each record was for only one direction of travel. Roadway data included the 

following variables:

• Area type (urban/rural).

• Functional class.

• Divided versus undivided.

• Median width.

• Median type.

• Number of lanes.

• Traveled way width.

• Shoulder type.

• Shoulder width.

• Paved shoulder width.

• Presence of rumble strip.

Traffic Data

The project team obtained traffic data in the form of AADT from WisDOT for 2012 and a 

projected AADT for 10 years out. WisDOT recommended extrapolating the AADTs for previous 

years. The percentage of trucks in the traffic stream was also provided.

Crash Data

WisDOT provided crash data for 2003 to 2012 for all treatment and reference sites. The compiled 

crash data contained many variables related to the location, time, and characteristics of each 

crash.
17

Treatment Cost Data

The following costs were provided by WisDOT based on average bid prices on 2012, 2013, and 

2014 projects:

• Inlaid into asphalt 4-inch tape—$5,800/mi for single lane line mi.

• Inlaid into asphalt 8-inch tape, placed mainly at ramp gores/channelizing lines—

$5,800 per gore.

• Grooved white 4-inch tape—$5,800/mile for single lane line.

• Grooved white 8-inch tape –$5,000 per ramp gore.

• Grooved contrast 4-inch tape—$9,200/mi for single lane line mile.

• Grooved contrast 8-inch tape—$7,300 per gore ramp.

A service life of 8 years was assumed for the grooved treatment. The tape product has only been 

available for a few years, so the wet reflectivity service life and durability is still unknown.

DATA CHARACTERISTICS AND SUMMARY

Table 1 defines the crash types used by each State. An attempt was made to make the crash type 

definitions consistent. In all States, intersection-related, animal-related, and snow/slush/icerelated crashes were excluded because these crash types were not correctable by the treatment 

under study.

Table 2 provides summary information for the data collected for the treatment sites. The 

information in table 2 should not be used to make simple before–after comparisons of crashes per 

mi-year because such comparisons would not account for factors, other than the strategy, that 

may cause a change in safety between the before and after periods. Such comparisons are 

properly done with the EB analysis as presented later in this report.

Table 3 provides summary information for the reference site data. Comparisons of crash rates 

between States and between treatment and reference sites should consider that the rates were only 

per mile and traffic volumes were not considered. 
18

Table 1. Definitions of crash types by State.

Crash Type Minnesota North Carolina Wisconsin

Total Identified as nonintersection; not animal;

and not snow, slush, or ice

Identified as nonintersection and not 

animal and not snow,

slush, or ice

Identified as nonintersection related; not 

deer or other animal: and 

not snow, slush, or ice

Injury Resulted in an injury or 

possible injury

Resulted in an injury or 

possible injury

Resulted in a fatality or 

injury

Run-offroadDiagram of accident is 

run-off-road-left or runoff-road-right

Accident type is run-offroadRelation to Roadway is 

median, outside shoulder 

left, outside shoulder 

right, or off roadway 

location unknown

SideswipesamedirectionDiagram of accident is 

sideswipe-passing

Accident type is 

sideswipe-same-direction

Manner of Collision is 

sideswipe-samedirection

SideswipeoppositedirectionDiagram of accident is 

sideswipe-opposing

Accident type is 

sideswipe-oppositedirection

Manner of Collision is 

sideswipe-oppositedirection

Head-on Diagram of accident is 

head-on

Accident type is head-on Manner of Collision is 

head-on

Wet-road Road Surface Condition is 

wet or water (standing or 

moving)

Road Surface Condition is 

wet

Road Surface Condition 

is wet

Nighttime Light Condition is dark Light Condition is dark Light Condition is dark

Nighttime 

wet-road

Light Condition is dark 

and Road Surface 

Condition is wet or water 

(standing or moving)

Light Condition is dark 

and Road Surface 

Condition is wet

Light Condition is dark 

and Road Condition is 

wet
19

Table 2. Data summary for treatment sites.

Variable

Minnesota 

Freeway

Minnesota 

Two-Lane 

Undivided

North Carolina

Freeway

Wisconsin 

Freeway

Wisconsin 

Multilane

Number of mi 34.49 736.39 95.41 179.09 120.74

Mi-years before 172.44 5,539.77 823.03 975.06 551.05

Mi-years after 137.95 1,087.75 512.67 375.96 226.49

Crashes/mi/year before 0.71 0.20 3.84 5.42 2.81

Crashes/mi/year after 0.78 0.16 5.04 3.66 2.10

Injury crashes/mi/year before 0.31 0.10 1.31 1.81 1.02

Injury crashes/mi/year after 0.32 0.08 1.24 1.10 0.69

Run-off-road 

crashes/mi/year before

0.30 0.09 0.16 0.93 0.24

Run-off-road 

crashes/mi/year after

0.49 0.07 0.26 0.68 0.27

Head-on crashes/mi/year 

before

0.01 0.01 0.002 0.02 0.03

Head-on crashes/mi/year 

after

0.01 0.01 0.014 0.01 0.01

Sideswipe-same-direction 

crashes/mi/year before

0.05 0.01 0.39 0.79 0.42

Sideswipe-same-direction 

crashes/mi/year after

0.09 0.01 0.76 0.61 0.42

Sideswipe-opposite-direction 

crashes/mi/year before

0.01 0.004 0.01 0.02 0.03

Sideswipe-opposite-direction 

crashes/mi/year after

0.01 0.005 0.01 0.01 0.01

Wet-road crashes/mi/year 

before

0.07 0.03 0.92 1.17 0.49

Wet-road crashes/mi/year 

after

0.07 0.02 1.04 0.62 0.31

Nighttime crashes/mi/year 

before

0.31 0.07 1.01 1.41 0.55

Nighttime crashes/mi/year 

after

0.22 0.05 1.30 1.02 0.42

Nighttime Wet-road 

crashes/mi/year before

0.03 0.01 0.24 0.16 0.05

Nighttime Wet-road 

crashes/mi/year after

0.04 0.01 0.33 0.20 0.08

AADT after1 Avg: 15,352

Min: 14,236

Max: 17,775

Avg: 2,202

Min: 39

Max: 13,779

Avg: 47,943

Min: 10,333

Max: 124,214

Avg 19,178

Min: 3,178

Max: 78,335

Avg: 7,274

Min: 1,353

Max: 25,381

Average right shoulder width 

(ft)

Avg: 8.34

Min: 8.00

Max: 10.00

Avg: 5.25

Min: 0.00

Max: 10.00

Avg: 11.34

Min: 5.00

Max: 19.00

Avg: 11.29

Min: 3.00

Max: 18.00

Avg: 7.92

Min: 0.00

Max: 15.00

Average left shoulder width 

(ft)

Avg: 3.00

Min: 3.00

Max: 3.00

Avg: 5.25

Min: 0.00

Max: 10.00

Avg: 10.98

Min: 0.00

Max: 14.00

Avg: 7.03

Min: 0.00

Max: 23.00

Avg: 5.12

Min: 0.00

Max: 26.00

Number of lanes Avg: 4.00

Min: 4.00

Max: 4.00

Avg: 2.00

Min: 2.00

Max: 2.00

Avg: 4.77

Min: 4.00

Max: 10.00

Avg: 4.00

Min: 4.28

Max: 6.00

Avg: 4.18

Min: 2.00

Max: 6.00

Posted speed limit (mi/h) N/A N/A Avg: 64.64

Min: 55.00

Max 70.00

N/A N/A
20

Variable

Minnesota 

Freeway

Minnesota 

Two-Lane 

Undivided

North Carolina

Freeway

Wisconsin 

Freeway

Wisconsin 

Multilane

Surface width (ft) N/A N/A Avg: 30.28

Min: 24.00

Max 96.00

N/A N/A

Median width (ft) Avg: 54.00

Min: 54.00

Max: 54.00

Avg 0.00

Min 0.00

Max 0.00

Avg 29.48

Min 0.00

Max 110.00

Avg 42.80

Min 0.00

Max 350.00

Avg 34.94

Min 0.00

Max 380.00

Area type (mi) Urban: 1.731

Rural: 

32.759

Urban: 37.41

Rural: 698.98

Urban: 18.12

Rural: 77.29

Urban: 76.28

Rural: 

102.82

Urban: 36.39

Rural: 84.36

Terrain (mi) N/A N/A Flat: 17.86

Rolling: 62.56

Mountainous: 

14.99

N/A N/A

1

The AADT data provided for Wisconsin were for one direction.

Avg = Average.

Min = Minimum.

Max = Maximum.

N/A = Not applicable.
21

Table 3. Data summary for reference sites.

Variable

Minnesota 

Freeway

Minnesota 

Two-Lane 

Undivided

North Carolina

Freeway

Wisconsin 

Freeway

Wisconsin 

Multilane

Number of mi 852.04 9,037.40 231.47 121.27 48.99

Mi-years 8,520.42 90,374.00 3,472.05 919.72 394.86

Crashes/mi/year 6.27 0.10 9.01 3.72 3.12

Injury crashes/mi/year 1.71 0.05 2.85 1.21 1.37

Run-off-road 

crashes/mi/year

1.14 0.05 1.19 0.64 0.36

Head-on crashes/mi/year 0.06 0.004 0.02 0.02 0.03

Sideswipe-samedirection crashes/mi/year0.95 0.01 1.44 0.56 0.44

Sideswipe-oppositedirection crashes/mi/year0.01 0.003 0.01 0.01 0.04

Wet-road 

crashes/mi/year

1.15 0.01 2.01 0.71 0.59

Nighttime 

crashes/mi/year

1.47 0.03 2.36 1.06 0.70

Nighttime Wet-road 

crashes/mi/year

0.34 0.01 0.59 0.15 0.10

AADT1 Avg: 62,893

Min: 6,038

Max: 197,250

Avg: 1,637

Min: 100

Max: 16,135

Avg: 60,493

Min: 13,000

Max: 163,000

Avg: 16,898

Min: 4,478

Max: 78,335

Avg: 9,233

Min: 1,813

Max: 30,314

Average right shoulder 

width (ft)

Avg: 9.68

Min: 0.00

Max: 13.00

Avg: 4.46

Min: 0.00

Max: 17.00

Avg: 11.32

Min: 0.00

Max: 22.00

Avg: 11.27

Min: 0.00

Max: 17.00

Avg: 9.33

Min: 0.00

Max: 14.00

Average left shoulder 

width (ft.)

Avg: 5.43

Min: 0.00

Max: 15.00

Avg: 4.44

Min: 0.00

Max: 17.00

Avg: 10.36

Min: 0.00

Max: 14.00

Avg: 6.99

Min: 0.00

Max: 22.00

Avg: 4.58

Min: 0.00

Max: 12.00

Number of lanes Avg: 5.00

Min: 4.00

Max: 10.00

Avg: 2.00

Min: 2.00

Max: 2.00

Avg: 4.98

Min: 4.00

Max: 16.00

Avg: 4.22

Min: 4.00

Max: 8.00

Avg: 4.16

Min: 2.00

Max: 6.00

Posted speed limit (mi/h) N/A N/A Avg: 62.63

Min: 35.00

Max: 70.00

N/A N/A

Surface width (ft) N/A N/A Avg: 32.46

Min: 24.00

Max: 96.00

N/A N/A

Median width (ft) Avg: 32.68

Min: 2.00

Max: 84.00

Avg: 0.00

Min: 0.00

Max: 0.00

Avg: 24.61

Min: 0.00

Max: 110.00

Avg: 44.92

Min: 0.00

Max: 220.00

Avg: 14.54

Min: 0.00

Max: 120.00

Area type (mi) Urban: 266.32

Rural: 585.72

Urban: 253.11

Rural: 8,784.29

Urban: 84.91

Rural: 146.56

Urban: 60.70

Rural: 60.57

Urban: 22.20

Rural: 26.79

Terrain (mi) N/A N/A Flat: 0.00

Rolling: 215.80

Mountainous: 

15.67

N/A N/A

1

The AADT data for Wisconsin were for one direction.

Avg = Average.

Min = Minimum.

Max = Maximum.

N/A = Not applicable.

23

CHAPTER 5. DEVELOPMENT OF SAFETY PERFORMANCE FUNCTIONS

This section presents the SPFs developed for each State. The SPFs were used in the EB 

methodology to estimate the safety effectiveness of this strategy.(7) Generalized linear modeling 

was used to estimate model coefficients assuming a negative binomial error distribution, which is 

consistent with the state of research in developing these models. In specifying a negative 

binomial error structure, the overdispersion parameter, k, was estimated iteratively from the 

model and the data. For a given dataset, smaller values of k indicate relatively better models.

SPFs were calibrated separately for Minnesota, North Carolina, and Wisconsin using the 

corresponding reference sites from each State. The SPFs developed are presented by State in the 

following sections. Urban and rural segments were combined for modeling because of the limited 

sample sizes, particularly for some crash types. Factor variables were attempted for all models to 

account for any differences between urban and rural areas in terms of expected crashes.

Note that for sideswipe-opposite-direction crashes the number of crashes was very low, and these 

crashes were not analyzed.

NORTH CAROLINA

The form of the SPFs for North Carolina freeways, which are presented in table 4, is shown in 

figure 7.

Figure 7. Equation. Form of SPFs for North Carolina.

Where:

AADT = Average annual daily traffic volume.

lanes = 1 if a four-lane road; 0 if greater than four lanes.

medwid = Median width in ft.

URBRUR = 1 if urban; 0 if rural.

a, b, c, d, e = Parameters estimated in the SPF calibration process.

k = Overdispersion parameter of the model.

Crashes/mi/year = exp(a)AADTbexp(lanes*c+medwid*d+URBRUR*e)
24

Table 4. North Carolina freeway SPFs.

Crash Type

Parameter Estimates (Standard Error)

a b c d e k

Total -11.5356

(1.4658)

1.3273

(0.1286)

-0.2662

(0.1664)

-0.0130

(0.0039)

— 1.5431

Injury -12.6811

(1.3749)

1.3152

(0.1201)

-0.2428

(0.1571)

-0.0094

(0.0036)

— 1.2591

Run-off-road -7.5439

(1.9691)

0.6848

(0.1802)

-0.5109

(0.1953)

-0.0083

(0.0053)

0.7401

(0.1926)

1.6316

Sideswipesame-direction-20.6539

(0.9716)

1.9422

(0.0901)

— — — 0.9485

Wet-road -7.2571

(1.5024)

0.8176

(0.1313)

-0.6663

(0.1651)

-0.0130

(0.0045)

— 1.5109

Nighttime -10.9313

(1.3845)

1.1479

(0.1212)

-0.2865

(0.1572)

-0.0117

(0.0037)

— 1.2428

Nighttime 

wet-road

-9.0525

(1.5366)

0.8603

(0.1341)

-0.5965

(0.1683)

-0.0119

(0.0044)

— 1.1739

— Indicates that the variable associated with parameter was not included in the SPF.

WISCONSIN

In Wisconsin the data were directional, so the SPFs apply to one direction of travel only. The 

form of the SPFs for Wisconsin, which are presented in table 5 for freeways and table 6 for 

multilane roads, is shown in figure 8.

Figure 8. Equation. Form of SPFs for Wisconsin.

Where:

AADT = Average annual daily traffic volume.

lshtotwd = Total width left shoulder in feet.

rsl = 0 if rumble strip is present on left shoulder; 1 if not present (only applies for freeways).

a, b, c, d = Parameters estimated in the SPF calibration process.

k = Overdispersion parameter of the model.

Crashes/mi/year = exp(a)AADTbexp(lshtotwd*c+rsl*d)
25

Table 5. Wisconsin freeway SPFs.

Crash Type

Parameter Estimates (Standard Error)

a b c d k

Total -14.4921

(0.9000)

1.6163

(0.0915)

-0.1155

(0.0218)

0.1729

(0.1369)

0.5117

Injury -15.4639

(1.0433)

1.6091

(0.1050)

-0.1250

(0.0252)

0.2276

(0.1521)

0.4627

Run-off-road -6.5392

(1.0811)

0.6094

(0.1097)

-0.0548

(0.0265)

0.2825

(0.1457)

0.3635

Sideswipesame-direction-20.4391

(1.2177)

2.0075

(0.1159)

-0.0943

(0.0258)

— 0.3287

Wet-road -17.3779

(1.2207)

1.7533

(0.1182)

-0.1301

(0.0294)

— 0.6121

Nighttime -14.6122

(1.1014)

1.4966

(0.1100)

-0.1220

(0.0251)

0.4171

(0.1615)

0.4998

Nighttime wetroad-18.2917

(1.9027)

1.7034

(0.1786)

-0.1354

(0.0411)

— 0.5154

— Indicates that the variable associated with parameter was not included in the SPF.

Table 6. Wisconsin multilane divided SPFs.

Crash Type

Parameter Estimates (standard error)

a b c k

Total -11.1106

(1.6615)

1.3452

(0.1723)

-0.2588

(0.0326)

0.5833

Injury -11.1247

(2.2012)

1.2609

(0.2273)

-0.2642

(0.0424)

0.7870

Run-off-road -7.1200

(1.5806)

0.5964

(0.1740)

— 0.2558

Sideswipesame-direction-14.1156

(2.5936)

1.4600

(0.2686)

-0.2794

(0.0407)

0.2880

Wet-road -15.6008

(3.1910)

1.6419

(0.3310)

-0.2483

(0.0528)

1.0507

Nighttime -12.0965

(2.1780)

1.2711

(0.2257)

-0.1661

(0.0380)

0.4843

Nighttime wetroad-19.8380

(4.2743)

1.8316

(0.4600)

— 1.8614

— Indicates that the variable associated with parameter was not included in the SPF.

MINNESOTA

The form of the SPFs for Minnesota, which are presented in table 7 and table 8, is shown in 

figure 9: 

Figure 9. Equation. Form of SPFs for Minnesota.

Crashes/mi/year = exp(a)AADTbexp(URBRUR*c+AVGSHLD*d)
26

Where:

AADT = Average annual daily traffic volume.

URBRUR = 1 if Urban area; 0 if Rural.

AVGSHLD = Average shoulder width in feet.

a, b, c, d = Parameters estimated in the SPF calibration process.

k = Overdispersion parameter of the model.

Table 7. Minnesota freeway SPFs.

Crash Type

Parameter Estimates (Standard Error)

a b C d k

Total -14.3723

(0.4805)

1.5230

(0.0508)

0.3279

(0.0975)

-0.0396

(0.0138)

0.5708

Injury -14.5068

(0.5238)

1.4270

(0.0551)

0.2286

(0.1045)

-0.0408

(0.0141)

0.5316

Run-off-road -10.0870

(0.3655)

1.0144

(0.0367)

— -0.0427

(0.0142)

0.5299

Sideswipesame-direction-17.6510

(0.6394)

1.6061

(0.0642)

0.5061

(0.1239)

— 0.6457

Wet-road -16.8656

(0.6190)

1.6012

(0.0643)

0.4080

(0.1211)

-0.0558

(0.0158)

0.6772

Nighttime -15.4123

(0.5732)

1.4999

(0.0599)

0.1988

(0.1152)

-0.0435

(0.0152)

0.6361

Nighttime wetroad-18.7804

(0.8346)

1.6686

(0.0852)

0.4004

(0.1615)

-0.0657

(0.0185)

0.8175

— Indicates that the variable associated with parameter was not included in the SPF.

Table 8. Minnesota two-lane undivided SPFs.

Crash Type

Parameter Estimates (Standard Error)

a b c d k

Total -8.3936

(0.1057)

0.9379

(0.0158)

0.5104

(0.0577)

-0.0843

(0.0055)

0.4390

Injury -8.1365

(0.1298)

0.7822

(0.0197)

0.3747

(0.0730)

-0.0512

(0.0070)

0.4106

Run-off-road -7.1823

(0.1337)

0.6443

(0.0208)

-0.2317

(0.0911)

-0.0406

(0.0076)

0.5454

Sideswipe-same-direction -14.9362

(0.4288)

1.4366

(0.0584)

0.6093

(0.1558)

-0.1260

(0.0168)

1.1590

Wet-road -11.1087

(0.2432)

1.0215

(0.0353)

0.7042

(0.1066)

-0.0828

(0.0114)

0.5333

Nighttime -8.4612

(0.1569)

0.7791

(0.0237)

0.4400

(0.0849)

-0.0738

(0.0084)

0.4770

Nighttime wet-road Use model for total with factor of 0.050

Head-on Use model for total with factor of 0.040

Sideswipe-oppositedirectionUse model for total with factor of 0.034
27

Because models for nighttime wet-road, head-on, and sideswipe-opposite-direction crashes could 

not be calibrated because of low numbers of crashes, predictions for those crash types used the 

model for total crashes with a multiplier for the proportion of total crashes that were of the 

respective crash type.

29

CHAPTER 6. BEFORE–AFTER EVALUATION RESULTS

AGGREGATE ANALYSIS

Table 9 through table 14 provide the estimates of expected crashes in the after period without 

treatment, the observed crashes in the after period, and the estimated CMF and its standard error 

for all crash types considered. Sideswipe-opposite-direction crashes were not analyzed because of

the very low number of crashes. The effects for dry-road crashes, which were not specifically 

evaluated as a target crash type, were inferred from the effects for total and wet-road crashes;

they are shown in these tables for information purposes. Results are provided separately for each 

State as well as all States combined. All results obtained are reported in this section. 

Recommended CMFs are presented in chapter 8. 

The results for North Carolina freeways in table 9 indicate reductions for injury, wet-road, and 

nighttime wet-road crashes although only the reductions for injury and wet-road crashes were

statistically significant at the 95-percent confidence level. All other crash types indicate a slight 

increase in crashes, but only the increase for dry-road was statistically significant.

Table 9. Results for North Carolina freeways.

Metric Total Injury

RunOffRoadSideswipeSameDirection

WetRoadDryRoad Nighttime

Nighttime 

WetRoad

EB estimate of 

crashes 

expected in 

after period 

without 

strategy

2,502.24 727.42 124.21 388.47 615.70 1,886.53 637.75 183.27

Number of 

crashes 

observed in 

after period

2,583 634 135 392 532 2,051 664 167

Estimate of 

CMF

1.032 0.871 1.081 1.006 0.863 1.087 1.040 0.907

Standard error 

of estimate of 

CMF

0.028 0.044 0.122 0.073 0.051 0.034 0.055 0.093

Note: CMF estimates that are statistically significant at the 95-percent confidence level are shown in boldface.

The results for Wisconsin freeways in table 10 indicate reductions for total, injury, run-off-road, 

wet-road, dry-road, and nighttime crashes that are all statistically significant at the 95-percent 

confidence level. Sideswipe-same-direction and nighttime wet-road crashes had non-statistically 

significant increases.
30

Table 10. Results for Wisconsin freeways.

Metric Total Injury

RunOffRoadSideswipeSameDirection

WetRoadDryRoad Nighttime

Nighttime 

WetRoad

EB estimate of 

crashes 

expected in 

after period 

without 

strategy

1,497.71 444.37 283.82 217.45 255.89 1241.82 426.64 60.53

Number of 

crashes 

observed in 

after period

1,329 397 247 221 223 1106 373 71

Estimate of 

CMF

0.887 0.893 0.870 1.015 0.870 0.890 0.874 1.170

Standard error 

of estimate of 

CMF

0.030 0.051 0.061 0.075 0.065 0.033 0.052 0.149

Note: CMF estimates that are statistically significant at the 95-percent confidence level are indicated in boldface.

The results for Wisconsin multilane roads in table 11 indicate reductions for total, injury, run-offroad, wet-road, dry-road, and nighttime crashes that were all statistically significant at the 

95-percent confidence level. Sideswipe-same-direction and nighttime wet-road crashes had nonstatistically significant increases.

Table 11. Results for Wisconsin multilane roads.

Metric Total Injury

RunOffRoadSideswipeSameDirection

WetRoadDryRoad Nighttime

Nighttime 

WetRoad

EB estimate of 

crashes expected 

in the after 

period without 

strategy

556.77 256.08 110.93 93.17 92.62 465.15 133.13 16.71

Count of crashes 

observed in the 

after period

460 153 60 88 70 390 93 17

Estimate of 

CMF

0.825 0.595 0.538 0.941 0.751 0.838 0.696 1.001

Standard error 

of estimate of 

CMF

0.051 0.059 0.078 0.115 0.108 0.058 0.082 0.270

Note: CMF estimates that are statistically significant at the 95 percent confidence level are indicated in boldface.

The results for Minnesota two-lane roads in table 12 indicate reductions for total, wet-road, 

nighttime, and nighttime wet-road crashes, none of which were statistically significant at the 

95-percent confidence level. However, the results for wet-road crashes were statistically 

significant at the 90-percent confidence level. Sideswipe-same-direction and nighttime wet-road 

crashes had non-statistically significant increases. For Minnesota two-lane roads, the total 
31

numbers of crashes were low, so lack of statistical significance in the analysis results was not

unexpected. The indications of reductions in wet, nighttime, and nighttime wet-road crashes do 

still support the hypothesis that wet-reflective markings reduce these types of crashes.

Table 12. Results for Minnesota two-lane roads.

Metric Total Injury

RunOffRoadSideswipeSameDirection

WetRoadDryRoad NighttimeNighttime 

Wet-Road

EB estimate of 

crashes expected 

in after period 

without strategy

186.26 84.43 79.19 10.61 24.76 161.50 52.04 8.48

Count of crashes 

observed in after 

period

176 89 81 14 17 159 51 7

Estimate of CMF 0.944 1.053 1.022 1.310 0.685 0.984 0.979 0.823

Standard error of 

estimate of CMF

0.075 0.116 0.118 0.365 0.169 0.083 0.141 0.313

The results for Minnesota freeways in table 13 indicate reductions for total, injury, sideswipesame-direction, wet-road, and nighttime crashes but none were statistically significant at the 

95-percent confidence level. The results for wet-road crashes were statistically significant at the 

90-percent confidence level. Run-off-road and nighttime wet-road crashes had non-statistically 

significant increases. For Minnesota freeways, as with the Minnesota two-lane roads, the total 

numbers of crashes were low, so the statistical insignificance was not unexpected. However, the 

indications of reductions in wet and nighttime crashes do still support the hypothesis that wetreflective markings reduce these types of crashes.

Table 13. Results for Minnesota freeways.

Metric Total Injury

RunOffRoadSideswipeSameDirection

WetRoadDryRoad Nighttime

Nighttime 

WetRoad

EB estimate of 

crashes expected 

in after period 

without strategy

112.02 47.98 58.34 12.33 15.90 96.12 39.24 4.89

Number of 

crashes observed 

in after period

107 44 68 12 10 97 30 6

Estimate of 

CMF

0.949 0.907 1.153 0.949 0.614 1.002 0.756 1.181

Standard error of 

estimate of CMF

0.117 0.165 0.182 0.305 0.211 0.133 0.159 0.515

The combined results for all freeway sites (North Carolina, Wisconsin, and Minnesota) in

table 14 indicate reductions for total, injury, run-off-road, wet-road, nighttime, and wet-road 

nighttime crashes, but only those for injury and wet-road crashes were statistically significant at 
32

the 95-percent confidence level. The results for sideswipe-same-direction and dry-road crashes 

showed negligible and non-statistically significant increases for these crash types.

Table 14. Results for combined States freeways.

Metric Total Injury

RunOffRoadSideswipeSameDirection

WetRoadDryRoad NighttimeNighttime 

Wet-Road

EB 

estimate 

of 

crashes 

expected 

in after 

period 

without 

strategy

4,111.97 1,219.76 466.37 618.28 887.49 3224.48 1,103.63 248.69

Count of 

crashes 

observed 

in after 

period

4,019 1,075 450 625 765 3254 1,067 244

Estimate 

of CMF

0.977 0.881 0.964 1.010 0.861 1.009 0.966 0.979

Standard 

error of 

estimate 

of CMF

0.020 0.033 0.054 0.054 0.040 0.024 0.038 0.080

Note: CMF estimates that are statistically significant at the 95 percent confidence level are indicated in boldface.

DISAGGREGATE ANALYSIS

An attempt was made to further analyze the combined freeway dataset for wet-road crashes to 

identify site characteristics for which the safety benefits were greater. Only wet-road crashes 

were considered because this was the principal target crash and the only one with a consistent and 

statistically significant effect in each of the three States. Only freeways were considered because 

the datasets for multilane and two-way roadways had too few crashes for such an analysis.

A number of variables were investigated, including the following:

• Surface width.

• Shoulder width.

• Area type (urban versus rural).

• Number of lanes.

• Presence of shoulder rumble strip.

• AADT.

• Expected wet-road crash frequency per mi prior to treatment.
33

No differences or clear trends were seen for any of these variables and the estimated CMFs.

Therefore, for this dataset, the expected effect of this strategy on wet-road crashes on freeways 

was the same regardless of differences in these aspects of the roadway environment.

35

CHAPTER 7. ECONOMIC ANALYSIS

An economic analysis was conducted to determine the estimated B/C ratio for this strategy for 

multilane roads and for freeways. (For two-lane roads, an economic analysis could not be 

performed because the crash reductions were too small and were statistically insignificant.) The 

statistically significant reduction in total crashes for Wisconsin was used as the benefit in the 

analysis of multilane roads. For freeways, the results for all States combined were used to derive

the benefit in the analysis. Specifically, because the reduction in total crashes was not statistically 

significant, the reduction for the targeted wet-road crashes was used as the benefit. 

On the cost side, for the installations on multilane roads in Wisconsin, the analysis conservatively

assumed, in the absence of details of each installation, that the grooved contrast 4-inch tape, 

costing $9,200/mi for a single lane line treatment, was used for all installations. In total, 

259.76 lane mi were installed at an estimated cost of $2,389,792. For freeways, the same per mile

treatment cost was assumed for Wisconsin. For the North Carolina installations, NCDOT 

indicated that the polyurea treatment costing $1.10 per linear ft was applied. For Minnesota, the 

analysis conservatively assumed that the ground-in markings cost $17,000/mi. With these 

assumptions, the total estimated cost for freeway installations in the three States was $6,765,373. 

The analysis assumed the useful service life for safety benefits was 2 years. This was based on 

information from MnDOT, which found that the retroreflectivity lasted 2 years under wet 

conditions and 4 years under dry conditions. Service lives reported by NCDOT and WisDOT 

indicated longer periods, so a 2-year life was assumed as the conservative option.

Based on information from the Office of Management and Budget Circular A-4, a real discount 

rate of 7 percent was used to calculate the annual cost of the treatment based on the 2-year 

service life.

(8) With this information, the installation costs convert to annual costs of $3,741,928

for freeways in the three States and $1,321,794 for multilane roads in Wisconsin. 

For the benefit calculations, the most recent FHWA mean comprehensive crash costs 

disaggregated by crash severity, location type, and speed limit were used as a base.

(9) Council 

et al. developed these costs based on 2001 crash costs and found that the unit costs (in 

2001 dollars) for property damage only (PDO) and fatal plus injury crashes for speed limits 

posted greater or equal to 50 mi/h (assumed for freeways) from that report were $7,800 and 

$206,015, respectively. For all speed limits combined (assumed for multilane roads) the costs 

were $7,428 and $158,177, respectively. These were updated to 2014 dollars by applying the 

ratio of the USDOT 2014 value of a statistical life of $9.2 million to the 2001 value of 

$3.8 million.(1) Applying this ratio of 2.42 to the unit costs for PDO and fatal plus injury crashes, 

and then weighting by the frequencies of these two crash types in the after period (2,944 and 

1,075 for freeways from table 14 and 307 and 153 for multilane roads from table 10), aggregate 

2014 unit costs for total crashes were obtained as shown in figure 10 and figure 11.

Figure 10. Equation. Freeway calculation. 

2.42(7,800 × 2,944/(2,944 + 1,075) + 206,015 × 1,075/(2,944 + 1,075)) = $147,181
36

Figure 11. Equation. Multilane calculation. 

Fatal crashes were not considered on their own because of the very low numbers of such crashes 

in the data, which would skew the results. 

The crash reduction was calculated by subtracting the actual crashes in the after period from the 

expected crashes in the after period had the treatment not been implemented. The number of 

crashes saved per year was 36.87 wet-road crashes for freeways and 51.59 total crashes for 

multilane roads, which were obtained by dividing the crash reductions (122.49 and 96.77, 

respectively) by the average number of after period years per site (3.32 and 1.88, respectively). 

The annual benefits (i.e., crash savings) of $5,426,563 and $7,187,312 for freeways and multilane 

roads, respectively, were the product of the crash reductions per year (36.87 and 51.59) and the 

aggregate costs of a crash, with all severities combined ($147,181 and $139,316). The B/C ratio 

was calculated as the ratio of the annual benefit to the annual cost. The B/C ratios were estimated 

to be 1.45 for freeways and 5.44 for multilane roads. The USDOT recommends that sensitivity

analysis be conducted by assuming values of a statistical life 0.57 and 1.41 times the 

recommended 2014 value.(1) These factors can be applied directly to the estimated B/C ratios to 

obtain a range of 0.83 to 2.04 for freeways and 3.10 to 7.67 for multilane roads. These results, 

which are summarized in table 15, suggest that the treatment—even with conservative 

assumptions on cost, service life, and the value of a statistical life—can be cost effective, 

especially for multilane roads.

Table 15. Economic analysis results.

Type of Road

B/C Ratio

Point Estimate Lower Bound Upper Bound

Freeways 1.45 0.83 2.04

Multilane Roads 5.44 3.10 7.67

2.42(7,428 × 307/(307 +153) + 158,177 × 153/(307 +153)) = $139,316
37

CHAPTER 8. SUMMARY AND CONCLUSIONS

The objective of this study was to undertake a rigorous before–after evaluation of the safety 

effectiveness of wet-reflective pavement markings as measured by crash frequency. The study 

used data from three States—Minnesota, North Carolina, and Wisconsin—to examine the effects 

for specific crash types, including total, fatal plus injury, run-off-road, sideswipe-same direction, 

wet-road, nighttime, and nighttime wet-road crashes. Table 16 shows the various crash types for 

which a statistically significant CMF at the 95-percent confidence level could be estimated. 

Crashes occurring at or related to an intersection and snow/slush ice and animal-related crashes 

were not included and should not be included when applying the recommended CMFs.

Table 16. Recommended CMFs and standard errors.

Type of Road Total1 Injury

Run-OffRoad Wet-Road Nighttime

Freeways — 0.881

(0.033)

— 0.861

(0.040)

—

Multilane roads 0.825

(0.051)

0.595

(0.059)

0.538

(0.078)

0.751

(0.108)

0.696

(0.082) 1

Total crashes and other crash types do not include those related to intersections, animals,

or snow/slush/ice conditions.

— Indicates no recommended CMF is available.

B/C ratios estimated with conservative cost and service life assumptions were 1.45 for freeways 

and 5.44 for multilane roads. With the USDOT recommended sensitivity analysis, these values 

could range from 0.83 to 2.04 for freeways and 3.10 to 7.67 for multilane roads.(1) These results 

suggest that the treatment—even with conservative assumptions on cost, service life, and the 

value of a statistical life—can be cost effective.

With additional data, future research may provide statistically significant results for total, run-offroad, and nighttime crashes on freeways as well as more informative disaggregate analyses and 

the development of CMFs.

39

APPENDIX. ADDITIONAL INSTALLATION DETAILS FROM STATES

The following appendix presents additional details provided by the three participating States 

regarding the installation of the subject strategies in that State. State transportation departments

were asked to provide responses to the following questions:

1. Can you provide any installation guidelines for the markings? For example, width, spacing, 

minimum levels of initial retroreflectivity?

2. Are there any criteria for deciding which roads receive the wet-reflective markings? For 

example, a certain level of AADT, a critical crash rate etc.? 

3. Were any other safety countermeasures installed in conjunction with the wet-reflective 

markings at the treatment sites evaluated by this study?

4. Please describe any notable challenges related to the markings installation and how you 

overcame them.

5. Please describe any notable challenges related to the markings maintenance and how you 

overcame them.

6. What lessons learned or recommendations would you share with another State interested in 

the widespread application of wet-reflective markings?

RESPONSES FROM MINNESOTA

1. Widths and spacing follow normal striping practices. Some wet reflective markings are 

recessed. Minimum levels of retroreflectivity follow standard specifications: Latex: White275, Yellow-180, Epoxy: White-300, Yellow-200, Tape: White-600, Yellow-500.

2. Districts decide where they would like to install wet reflective markings based on their needs. 

In general, it will be roadways with an ADT > 1,500.

3. These were stand-alone striping projects.

4. One challenge we have noticed when looking at wet reflective installations after the fact is 

that the distribution of the larger elements is sometimes sporadic. Some areas will have good 

coverage and some will have light coverage. This may be remedied by making sure striping 

companies have double drop systems and their application ratio of bead/elements is following 

the manufacturer's specifications. Another possible challenge is groove depth. Substandard 

grooving contributes to accelerated wear on the wet reflective elements.

5. One maintenance challenge is the shorter life span of the wet reflectivity of these markings. 

From our experience, wet reflectivity lasts around 2 years. Beyond that, the dry reflectivity is 

still well beyond minimums. It would be great to have a way to refresh wet retroreflectivity.

6. We would recommend recessing all wet reflective markings. Inspect installations to make 

sure element/bead coverage is good.

RESPONSES FROM NORTH CAROLINA

1. The only “wet-reflective” marking we have approved is cold-applied plastic. Although it is 

approved, we do not have a detailed specification for wet-reflective markings. The minimum 
40

initial retroreflectivity for this cold applied plastic is 250 mcd/lux/m2 for white and yellow.

The only reason this is approved is due to the manufacturer only makes this type of tape. 

2. At this time, we don’t have any safety data showing a positive impact for wet-reflective 

marking. There are no specific criteria for installation. If a need is seen to install wet 

reflective markings due to an unusual circumstance it may be done. There is no AADT at 

which we do or don’t install these types of markings. 

3. No other safety countermeasures were installed in conjunction with the wet-reflective 

markings.

4. Our biggest challenge to marking installation is improper installation. This is not a regular 

occurrence, but it does happen. This is usually do to improper surface preparation and/or 

cleaning or poor workmanship (not applying the material correctly, applying too much or too 

little material, etc.). We overcome this by working with the installer to find out what the 

installation issue is and how it can be corrected. Additionally, we have an annual pavement 

marking training for DOT and contractors.

5. We now know enough about the types of markings we use, that if it’s installed correctly we 

can predetermine its life-cycle (through studies and historical performance). One of our 

biggest challenges to marking maintenance is snowplowing. We overcome this by installing 

lower profile markings (polyurea) in locations that see moderate to heavy snowplowing.

6. Show us a benefit.

RESPONSES FROM WISCONSIN

1. Standard pavement markings specifications are used for wet-reflective products. The 

minimum required retroreflectivity is 250 mcd. based on the ASTM E2177 test.

2. On newly paved or resurfaced roads wet-reflective tape is applied for the lane lines and gore 

areas on freeways, expressways or multilane divided roads. On existing pavements 3 years 

old or greater wet-reflective tape is used to supplement sprayed epoxy markings at the end of 

lane lines on concrete and faded asphalt freeways/expressways.

3. Some locations had either shoulder widening and/or shoulder rumble strips installed. (NOTE: 

This amounted to approximately 113 mi of the 600 mi used, mostly on freeways).

4. Initially, all of the wet reflective tape was inlaid into the asphalt surface which shortened the 

longevity of the tape. For several years, all tape is now grooved in a 120 mils slot no matter 

the surface of asphalt or concrete. The initial 10 day wait requirement for grooving in asphalt 

was causing completion delay so with the agreement of manufacturer and those at WisDOT 

in the pavement area, it was lowered to 5 days.

5. Inlaid tape was being removed by snowplow activity, thereby increasing the frequency of 

replacement/maintenance of the tape. Placing the tape in a groove decreased this problem. 

6. Place all wet reflective tape in a grooved slot.
41

ACKNOWLEDGEMENTS

This report was prepared for FHWA, Office Safety Research and Development under Contract 

DTFH61-13-D-00001. The FHWA Contract Officer’s Technical Manager for this project was 

Ms. Roya Amjadi.

The project team gratefully acknowledges the participation and assistance of the following 

organizations for this study:

Minnesota Department of Transportation.

North Carolina Department of Transportation.

Wisconsin Department of Transportation.

43

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2. Minnesota Department of Transportation. (2015) “Pavement Markings & Delineation.” 

http://www.dot.state.mn.us/trafficeng/pavement/index.html. Accessed August 12, 2015. 

3. Carlson, P., Park, E., and Kang, D. (2013) “Investigation of Longitudinal Pavement Marking 

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Estimating Equations. TRB 88th Annual Meeting Compendium of Papers CD-ROM. 

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6. Bahar, G., Masliah, M., Erwin, T., Tan, E., and Hauer, E. (2006) Pavement Marking 

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Time. National Cooperative Highway Research Program Web-Only Document 92. 

7. Hauer, E. (1997) Observational Before-After Studies in Road Safety—Estimating the Effect of 

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8. Office of Management and Budget. (2003). Circular A-4. 

https://www.whitehouse.gov/omb/circulars_a004_a-4/. Accessed August 12, 2015. 

9. Council, F., Zaloshnja, E., Miller, T., and Persaud, B. (2005) Crash Cost Estimates by 

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