TECHBRIEF Safety Evaluation of Edge 

Line Rumble Stripes (ELRSs)

on Rural, Two-Lane 

Horizontal Curves

FHWA Publication No.: FHWA-HRT-17-068

FHWA Contact: Roya Amjadi, HRDS-20, (202) 493-3383, 

roya.amjadi@fhwa.dot.gov

This document is a technical summary of the Federal 

Highway Administration report Safety Evaluation of 

Edge Line Rumble Stripes (ELRSs) on Rural, TwoLane Horizontal Curves (FHWA-HRT-17-069).

Objective

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 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 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. Forty 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 

Research, Development, and 

Technology

Turner-Fairbank Highway 

Research Center

6300 Georgetown Pike

McLean, VA 22101-2296

www.fhwa.dot.gov/research
2

Low-Cost Safety Improvements Pooled 

Fund Study, which functions under the 

DCMF program.

This study evaluated the application 

of edge line rumble stripes (ELRSs) on 

rural, two-lane horizontal curves. This 

strategy was intended to reduce the 

frequency of run-off-road (ROR) crashes 

and nighttime crashes by alerting 

drivers that they are about to leave the 

traveled lane and by enhancing visibility 

of the edge line during nighttime and 

wet-weather conditions.

Several research studies have 

examined the use of shoulder rumble 

strips (SRSs); however, research into 

the performance of ELRSs is rare and 

has not been rigorously evaluated. 

Additionally, milled rumble strips 

are installed on roadway segments, 

consisting of both horizontal tangents 

and horizontal curves. Installations on 

only horizontal curves are uncommon 

and, therefore, safety effectiveness 

evaluations have not focused on their 

effectiveness on horizontal curves 

specifically. This research focused on 

the safety effectiveness of ELRSs on 

rural, two-lane horizontal curves, taken 

from rumble stripe installations that are 

not specific to horizontal curves.

Introduction

ELRSs are a variation of the common 

SRSs used to alert drowsy or distracted 

drivers when they leave the travel lane 

to the right. SRSs and ELRSs both target 

ROR crashes. Key distinctions between 

SRSs and ELRSs are provided in the 

following points:

• SRSs are provided on the shoulder 

between the pavement marking and 

the outside edge of the pavement 

and can be defined by their offset 

from the edge line pavement 

marking.

• ELRSs are installed where the 

edge line pavement marking 

would normally be placed, and 

the pavement marking is installed 

directly over the rumble strip. In 

this way, the rumble stripes are 

installed closer to the travel lane 

than common SRSs. Additionally, 

vertical faces are created within 

the milled rumble strip to which 

pavement markings are applied, 

thereby enhancing the visibility of 

the edge line during nighttime and 

wet-weather conditions.

The Manual on Uniform Traffic Control 

Devices (MUTCD) defines rumble strips 

as either slightly raised or depressed 

road surfaces with a rough texture 

designed to provide a haptic alert for 

inattentive drivers leaving the travel 

lane.(1) As shown in figure 1, rumble 

strips may be installed either on the 

shoulders or center line on rural, twolane highways, and the edge line may 

be adjacent to the rumble strip or overlapping with it, creating a rumble stripe. 

The literature review revealed that 

SRSs have been studied extensively 

on all facility types, including rural, 

two-lane highways. However, few 
3

research studies considered the safety 

effectiveness of ELRSs, particularly 

the effectiveness on horizontal curves. 

Torbic et al., in the National Cooperative 

Highway Research Program (NCHRP) 

Report 641, evaluated the impact of SRS 

placement on safety, focusing on singlevehicle ROR (SVROR) fatal and injury 

crashes.(2) The report defined placement 

as edge line and non-edge line, which 

it then compared to no rumble strips. 

The report defined ELRSs as rumble 

strips with an offset distance of 0 to 

8 inches and non-ELRSs as having an 

offset of 9 inches or more. For two-lane 

rural roadways, there was no significant 

or practical difference between ELRSs 

and non-ELRSs. ELRSs were associated 

with a 33-percent reduction in SVROR 

fatal and injury crashes. Also, there is 

no evidence that suggests SRSs result 

in a reduction of SVROR crashes 

involving heavy vehicles.

Other researchers examined operational 

and visibility impacts of ELRSs. The 

University Transportation Center 

for Alabama compared ELRSs to flat 

thermoplastic edge markings and found 

that the initial dry retroreflectivity was 

similar; however, ELRSs lost visibility 

at a lower rate due to cumulative traffic 

passes.(3) ELRS wet retroreflectivity 

was higher than flat thermoplastic 

markings, but degradation could not be 

compared. The Mississippi Department 

of Transportation (MDOT) installed 

ELRSs on a portion of Interstate 59 

with generally encouraging results.(4)

Preliminary data indicated that the 

strategy provided an excellent audible 

alert, increased visual awareness of 

the travel lane, increased reflectivity, 

and provided similar results as inverted 

profile striping, and Mississippi 

residents welcomed the installation. 

However, there were also concerns 

associated with the rumble stripes, 

including noise pollution and 

the potential for an increase in 

overcorrecting and head-on crashes. 

Figure 1. Illustrations. Examples of longitudinal rumble strip markings.(1)

A. Edge line not on rumble strip. B. Edge line on rumble strip. C. Center line on rumble strip.

Note: Arrows indicate the direction of travel; hollow squares indicate rumble strip; edge line may be located

alongside the rumble strip (A) or on the rumble strip (B); the center line markings may also be located on a center

line rumble strip (C).
4

Carlson et al. examined wet-night 

visibility of pavement markings using 

experimental drivers on a closed rain 

tunnel.(5,6) The study tested nine different 

treatments in random orders and 

measured perception distance for each 

sample location. The driver alerted the 

researcher when he or she observed 

a marking and when the type could 

be determined. Part of this research 

included the testing of rumble stripes. 

The findings suggest there is little 

difference between flat thermoplastic 

lines and rumble stripe lines at low 

rainfall rates. However, the detection 

distance was 13 to 38 percent greater 

for rumble stripe lines for medium and 

heavy rainfall rates.

Methodology

This research examined the safety 

impacts of ELRSs on rural horizontal 

curves in Kentucky and Ohio. The objective was to estimate the safety effectiveness of this strategy as measured 

by crash frequency. Excluding intersection-related and animal crashes, the 

project team included the following 

crash types:

• Total (all types and severities 

combined).

• Fatal and injury (K, A, B, and C 

injuries on KABCO scale) (K is fatal 

injury, A is incapacitating injury, B 

is non-incapacitating injury, C is 

possible injury, and O is property 

damage only).

• ROR (all severities combined).

• Nighttime (including dusk and 

dawn; all severities combined).

• Nighttime ROR (including dusk and 

dawn; all severities combined).

A further objective was to conduct a 

disaggregate analysis to investigate 

whether the safety effects vary by factors 

such as the level of traffic volumes, the 

frequency of crashes before treatment, 

posted speed limit, and shoulder width.

The evaluation of overall effectiveness 

included the consideration of the 

installation costs and crash savings in 

terms of the B/C ratio.

The project team used the empirical 

Bayesian (EB) methodology for 

observational before–after studies for 

the evaluation.(7) 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, the project team 

used safety performance functions 

(SPFs), and the use of these addresses 

the following:

• It overcomes the difficulties of 

using crash rates in normalizing 

for volume differences between the 

before and after periods.

• It accounts for time trends.

• It reduces the level of uncertainty in 

the estimates of safety effect.

• It properly accounts for differences 

in crash experience and reporting 

practice in amalgamating data and 

results from diverse jurisdictions.

The methodology also provides a foundation for developing guidelines for estimating the likely safety consequences 

of a contemplated strategy.
5

The project team estimated the SPFs 

used in the EB methodology through 

generalized linear modeling 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 project team estimated an 

overdispersion parameter, which they 

used in the EB calculations, iteratively 

from the model and the data. For a given 

dataset, smaller values of this parameter 

indicate relatively better models.

The full report includes a detailed 

explanation of the methodology, 

including a description of how the 

estimate of safety effects for target 

crashes was calculated.

Results

Due to differences in application in 

Kentucky and Ohio, the results are 

presented separately. The Kentucky 

results represent the safety effectiveness of only ELRS application, while 

the Ohio results include the effects of a 

simultaneous statewide sign upgrade 

program. In this program, many curves 

received new or upgraded advance 

warning signs, in-curve warning signs, 

and updates to advisory speeds. The 

results are presented in two parts. The 

first part contains aggregate results, and 

the second part is based on a disaggregate analysis that attempted to discern factors that may be most favorable 

to the installation of ELRSs on rural, 

two-lane horizontal curves. The second 

part focuses on Ohio data, since the 

sample sizes are too small in Kentucky 

for further disaggregate analysis.

Aggregate Analysis

Table 1 provides the estimates of 

expected crashes in the after period 

without treatment, the observed crashes 

in the after period, and the estimated 

crash modification factor (CMF) and its 

standard error (SE) for all crash types 

considered in Kentucky. Table 2 presents 

the results for Ohio.

The results for Kentucky indicate 

statistically significant reductions for all 

crash types except for nighttime ROR 

crashes at the 95-percent confidence 

level. Nighttime crashes have the 

Statistic Total Fatal and 

Injury ROR Nighttime Nighttime ROR

EB estimate of crashes 

expected in the after period 

without strategy

113.9 40.8 67.6 33.1 22.5

Count of crashes observed 

in the after period 86 26 50 21 17

Estimate of CMF 0.75* 0.64* 0.74* 0.63* 0.75

SE of estimate of CMF 0.09 0.14 0.11 0.14 0.19

Table1. Results for Kentucky.

*Indicates statistically significant results at the 95-percent confidence level.
6

smallest CMF (which translates to the 

greatest reduction) with a CMF of 0.63. 

Total, fatal and injury, and ROR crashes 

have CMFs of 0.75, 0.64, and 0.74, 

respectively. The CMF for nighttime 

ROR crashes is consistent with other 

crash types; however, the sample size is 

very small. The CMFs are smaller than, 

but consistent with, those found in the 

most comprehensive and reliable study 

of SRSs to date, NCHRP Report 641.(2)

Based on a before–after EB analysis, 

the project team found that milled SRSs 

have a crash reduction of 16 percent 

(SE = 8) for all SVROR crashes and 36 

percent (SE = 10) for SVROR fatal and 

injury crashes.(2) However, the analysis 

results for SRSs in NCHRP Report 641 

considered segments with both horizontal tangents and curves; therefore, a 

direct comparison of results cannot be 

made. It is also important to remember 

that all crash types considered in this 

research exclude intersection-related 

and animal crashes. 

The results for Ohio indicate statistically 

significant reductions for all crash 

types. Nighttime ROR crashes have the 

smallest CMF with a CMF of 0.71. Total, 

fatal and injury, ROR, and nighttime 

crashes have CMFs of 0.79, 0.79, 

0.78, and 0.75, respectively. As with 

the Kentucky results, the CMFs 

are consistent with those found in 

NCHRP Report 641.(2) The resulting 

Ohio installation CMFs reflect the 

installation of ELRSs on horizontal 

curves as well as the impact of the 

statewide signing program. Due to 

the spillover effects of the signing 

program, further analyses involving 

curves that specifically received new or 

additional signs were not fruitful. The 

results indicated that there is little to 

no additional benefit for the combination 

of ELRSs and horizontal curve signing 

in comparison to horizontal curve 

signing only. 

Disaggregate Analysis of Ohio Data

The disaggregate analysis sought to 

identify those conditions under which 

the treatment is most effective. Since 

ROR, nighttime, and nighttime ROR 

crashes are the focus of this treatment, 

Statistic Total Fatal and 

Injury ROR Nighttime Nighttime ROR

EB estimate of crashes 

expected in the after period 

without strategy

514.2 208.6 392.7 191.6 160.1

Count of crashes observed 

in the after period 405 165 305 144 114

Estimate of CMF 0.79* 0.79* 0.78* 0.75* 0.71*

SE of estimate of CMF 0.04 0.07 0.05 0.07 0.07

*Indicates statistically significant results at the 95-percent confidence level.

Table 2. Results for Ohio.
7

these crash types are the focus of the 

disaggregate analysis. Additionally, 

disaggregate results are presented for 

total crashes and fatal and injury crashes. 

The data sample for Kentucky is too 

small to perform disaggregate analyses; 

therefore, disaggregate analyses focus 

on Ohio data. 

The project team identified several 

variables as being of interest and 

available, including degree of curve, 

posted speed limit, paved shoulder 

width, lane width, annual average 

daily traffic (AADT), and before-period 

expected crash frequency. Disaggregate 

results are provided by AADT in table 

3 and before-period expected crash 

frequency in table 4. The number of 

crashes in the after period is presented 

for each CMF to indicate the sample 

size available. Several of the estimated 

CMFs rely on small samples, especially 

for nighttime crashes and nighttime 

ROR crashes. 

As shown in table 3, smaller CMFs (i.e., 

larger safety benefits) were found for 

all crash types for sites with an AADT 

of 4,000 or more vehicles per day; 

however, the 95-percent confidence 

intervals overlap for each crash type. 

At AADTs lower than 4,000 vehicles per 

day, for example, an ROR CMF of 0.82 

was estimated versus a CMF estimate 

of 0.64 for AADTs of 4,000 or greater 

vehicles per day. A similar difference 

was found for all other crash types. The 

4,000 vehicles per day AADT cutoff is 

consistent with previous research by 

Patel et al. and Lyon et al.(8,9)

The 95-percent confidence intervals did 

not overlap for total crashes and ROR 

crashes. Due to the differences in the 

frequencies of different crash types, the 

before-period expected crash frequency 

cutoff varied for each crash type. For 

example, an ROR CMF of 1.13 was 

estimated for horizontal curves with 

an ROR before-period expected crash 

frequency less than 0.20 crashes per 

year. This can be compared to a CMF of 

0.66 for horizontal curves with 0.20 or 

more before-period expected crashes 

per year. Note that the CMF of 1.13 for 

an ROR before-period expected crash 

rate less than 0.20 was not statistically 

significant. Similar results were found 

for all other crash types. 

Crash Type <4,000 Observed Crashes <4,000 

CMFs (SE)

4,000+ Observed 

Crashes

4,000+ 

CMFs (SE)

Total 289 0.82* (0.06) 116 0.72* (0.08)

Fatal and injury 118 0.82* (0.08) 47 0.72* (0.12)

ROR 239 0.82* (0.06) 66 0.64* (0.09)

Nighttime 105 0.79* (0.08) 39 0.66* (0.12)

Nighttime ROR 88 0.78* (0.09) 26 0.54* (0.11)

*Indicates statistically significant results at the 95-percent confidence level.

Table 3. Ohio results by AADT.
8

Caution should be used in interpreting 

and applying these disaggregate 

CMF results because of correlation 

among variables and because they 

were not robust enough to develop 

crash modification functions. A crash 

modification function is a formula used 

to compute the CMF for a specific 

site as a function of its site-specific 

characteristics. For example, crash 

modification functions would allow 

the estimation of CMFs for different 

levels of AADT and before-period 

crash frequency. Additionally, the 

disaggregate analysis results used 

the EB analysis data, which includes 

the effects of the statewide horizontal 

curve signing program. However, the 

disaggregate analysis CMFs may be 

used in prioritizing treatment sites. For 

example, sites with a high proportion of 

ROR crashes and high AADTs will have 

high priority.

Economic Analysis

The research team conducted an 

economic analysis to estimate the B/C 

ratio for this strategy on rural, two-lane 

horizontal curves. For the purposes of 

the economic analysis, the assumed 

treatment is the application of ELRSs. 

The team used the recommended CMFs 

of 0.75 for Kentucky and 0.79 for Ohio 

to estimate the benefit of this treatment 

strategy. The Ohio results likely include 

the impact of additional sign upgrades, 

for which the Ohio Department of 

Transportation (ODOT) did not have data 

that could be included in this analysis. 

Additionally, the project team did not 

include the cost of pavement markings 

in the study because they are already 

present on the roadway, and the ELRSs 

did not affect the lifespan of edge line 

pavement markings. Treatment costs 

range from $1,700 per mile for Ohio and 

$2,500 per mile for Kentucky. Service 

life is 12 to 15 years for Kentucky and 

7 to 10 years for Ohio (ODOT noted 

that the service life lasts as long as the 

pavement). The project team assumed 

a conservative value of 12 years for 

Kentucky and 7 years for Ohio.

Crash Type Value Less Than Value 

Observed Crashes

Less Than Value

CMFs (SE)

Greater Than or 

Equal to Value 

Observed Crashes

Greater Than or 

Equal to Value

CMFs (SE)

Total 0.25 136 1.09 (0.11) 269 0.69* (0.05)

Fatal and injury 0.10 63 1.00 (0.14) 102 0.70* (0.08)

ROR 0.20 111 1.13 (0.12) 194 0.66* (0.05)

Nighttime 0.15 85 0.93 (0.11) 59 0.59* (0.08)

Nighttime ROR 0.075 38 0.85 (0.15) 76 0.66* (0.08)

*Indicates statistically significant results at the 95-percent confidence level.

Table 4. Ohio results by before-period expected crash frequency.
9

The FHWA Office of Safety R&D suggests 

that the Office of Management and 

Budget Circular A-4 be used to determine the conservative real discount 

rate of 7 percent.(10) The project team 

applied this discount rate to calculate 

the annual cost of the treatment for 

12-year and 7-year service lives in 

Kentucky and Ohio, respectively. With 

this information, the project team 

computed the capital recovery factor 

to be 7.94 for a 12-year service life and 

5.39 for a 7-year service life. 

The FHWA’s most recent mean 

comprehensive crash costs, which are 

disaggregated by crash severity, location type, and speed limit, are based on 

2001 dollar values.(11) The 2001 unit costs 

for property-damage-only (PDO) and 

injury crashes from the FHWA report 

($7,428 and $158,177) were multiplied 

by the ratio of the 2014 (when the 

analysis was performed) value of a 

statistical life of $9.2 million to the 2001 

value of $3.8 million.(12,13) By applying 

this ratio of 2.42 to the unit costs for PDO 

and injury crashes and then weighting 

by the frequencies of these two crash 

types in the after period, the project 

team obtained the aggregate 2014 

unit costs for total crashes of $128,268 

and $166,603 for Kentucky and Ohio, 

respectively.

The project team calculated the total 

crash reduction 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 mile-year is 0.812 in Kentucky 

and 0.913 in Ohio. The project team 

obtained these numbers by dividing 

the total crash reduction by the number 

of after-period mile-years per site. 

The annual benefit (i.e., crash savings) 

of $104,165 in Kentucky and $150,368 

in Ohio is the product of the crash 

reduction per mile-year and the 

aggregate cost of a crash (all severities 

combined). The B/C ratio is calculated as 

the ratio of the annual benefit per mile 

to the annual cost per mile. The project 

team estimates the B/C ratio to be 331:1 

in Kentucky and 477:1 in Ohio. USDOT 

recommended a sensitivity analysis be 

conducted by assuming values of a 

statistical life of 0.57 and 1.41 times 

the recommended 2014 value.(12) These 

factors were applied directly to the 

estimate B/C ratios to obtain a range of 

189:1 to 467:1 for Kentucky and 272:1 

to 672:1 for Ohio. On first inspection, 

the B/C ratios are larger than expected 

for an installation of this type. However, 

the installations took place on corridors, while the analysis only looked at 

the safety effects on horizontal curves. 

Horizontal curves have higher crash 

rates than overall corridors, and the 

cost per mile of installation would not 

be representative for installations 

only on horizontal curves. As a curvespecific treatment, the B/C ratio would 

likely be reduced due to the higher 

deployment cost for spot-specific 

installations; however, these results 

suggest that the treatment can be 

highly cost effective. 
10

Summary and Conclusions

The objective of this study was to 

undertake a rigorous before–after 

evaluation of the safety effectiveness, 

as measured by crash frequency, 

of ELRSs applied on rural, two-lane 

horizontal curves. The CMFs shown in 

table 5 and table 6 are recommended 

for the various crash types. The results 

for Kentucky are based on smaller 

sample sizes, and the results for Ohio 

include the effects of a statewide 

horizontal curve warning sign upgrade 

program.

To date, the most comprehensive and 

reliable study of SRSs is published in 

NCHRP Report 641—Guidance for the 

Design and Application of Shoulder and 

Centerline Rumble Strips.(2) Compared 

to the results of that study for ELRSs, 

the results suggest that placing rumble 

strips on or near the edge line for 

horizontal curves, specifically, may 

result in greater reductions in all crash 

types. It also appears that ELRSs are 

associated with significant reductions 

in nighttime crashes but do not further 

reduce nighttime ROR crashes on 

horizontal curves.

A disaggregate analysis of the results 

indicated that, for horizontal curves with 

AADTs greater than 4,000 for all crash 

types, there are larger safety benefits; 

however, the differences by AADT are 

not statistically significant at the 

95-percent confidence level. The 

disaggregate analysis further indicated 

larger safety benefits for horizontal 

curves with a higher before-period 

expected crash frequency. The results 

suggested no benefit for curves with 

low before-period expected crash 

frequencies for all crash types. The 

difference in CMFs between low beforeperiod expected crash frequency and 

Statistic Total Fatal and 

Injury ROR Nighttime Nighttime ROR

Estimate of CMF 0.75* 0.64* 0.74* 0.63* 0.75

SE of estimate of CMF 0.09 0.14 0.11 0.14 0.19

Statistic Total Fatal and 

Injury ROR Nighttime Nighttime ROR

Estimate of CMF 0.79* 0.79* 0.78* 0.75* 0.71*

SE of estimate of CMF 0.04 0.07 0.05 0.07 0.07

*Indicates statistically significant results at the 95-percent confidence level.

*Indicates statistically significant results at the 95-percent confidence level.

Table 5. Recommended CMFs for ELRSs based on Kentucky data.

Table 6. Recommended CMFs for ELRSs and curve signage based on Ohio data.
11

high before-period expected crash 

frequency is statistically significant for 

total and ROR crashes. 

The project team estimated the B/C 

ratios to range from 189:1 to 467:1 

for Kentucky and from 272:1 to 672:1 

for Ohio. The high B/C ratios should 

be considered in the context that the 

installations took place on corridors, 

while the analysis only looked at the 

safety effects on horizontal curves. 

Horizontal curves have higher crash 

rates than overall corridors, and the 

cost per mile of installation would 

not be representative for installations 

only on horizontal curves. Additionally, 

the safety effectiveness evaluation in 

Ohio included the impacts of upgraded 

horizontal curve warning signage. As 

a curve-specific treatment, the B/C 

ratio would likely be reduced due to 

the higher deployment cost for spotspecific installations; however, these 

results suggest that the treatment—

even its most expensive variation—can 

be highly cost effective. 

References

1. U.S. Department of Transportation 

Federal Highway Administration. 

(2009). Manual on Uniform Traffic 

Control Devices for Streets and 

Highways, USDOT Federal Highway 

Administration, Washington, DC. 

2. Torbic, D., Hutton, J., Bokenkroger, C., 

Bauer, K., Harwood, D., Gilmore, D., 

Dunn, J., Ronchetto, J., Donnell, E., 

Sommer H., III, Garvey, P., Persaud, B., 

and Lyon, C. (2009). Guidance for the 

Design and Application of Shoulder 

and Centerline Rumble Strips, Report 

641, National Cooperative Highway 

Research Program, Transportation 

Research Board of the National 

Academies, Washington, DC. 

3. Lindly, J. and Narci, A. (2006). 

Evaluation of Rumble Stripe 

Markings, UTCA Report 04405, 

University Transportation Center for 

Alabama, University of Alabama, 

Tuscaloosa, AL.

4. Willis, J. and Dean, W. (2004). 

“Mississippi’s Rumble Stripe Experience.” Presented at the 83rd Annual 

Meeting of the Transportation 

Research Board, Washington, DC. 

5. Carlson, P.J., Miles, J.D., Pratt, M.P., 

and Pike, A.M. (2005). Evaluation of 

Wet-Weather Pavement Markings: 

First Year Report, Report No. FHWA/

TX-06/0-5008-1, Texas Department of 

Transportation, Austin, TX. 

6. Carlson, P.J., Miles, J.D., Pike, A.M., 

and Park, E.S. (2007). Evaluation of 

Wet-Weather and Contrast Pavement 

Marking Applications: Final Report, 

Report No. FHWA/TX-07/0-5008-2, 

Texas Department of Transportation, 

Austin, TX.

7. Hauer, E. (1997). Observational 

Before-After Studies in Road 

Safety—Estimating the Effect of 

Highway and Traffic Engineering 

Measures on Road Safety. Elsevier 

Science, Incorporated, Amsterdam, 

Netherlands. 

8. Patel, R.B., Council, F.M., and Griffith, 

M.S. (2007). “Estimating the Safety 

Benefits of Shoulder Rumble Strips 

on Two-Lane Rural Highways in 

Minnesota: An Empirical Bayes 

Observational Before-After Study.” 

Presented at the 86th Annual Meeting of the Transportation Research 

Board, Washington, DC.
12

NOVEMBER 2017 FHWA-HRT-17-068

HRDS-20/11-17(200)E

Researchers—Scott Himes, Frank Gross, Bhagwant Persaud, and Kimberly Eccles.

Distribution—This TechBrief is being distributed according to a standard 

distribution. Direct distribution is being made to the Divisions and Resource Center.

Availability—This TechBrief may be obtained from the FHWA Product Distribution 

Center by e-mail to report.center@dot.gov, fax to (814) 239-2156, phone to (814) 

239-1160, or online at http://www.fhwa.dot.gov/research.

Key Words—Rumble strips, rumble stripes, low-cost, safety improvements, safety 

evaluations, empirical Bayesian.

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 the Government, industry, and 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.

9. Lyon, C., Persaud, B., and Eccles, 

K. (2015). Safety Evaluation of 

Centerline Plus Shoulder Rumble 

Strips, Report No. FHWA-HRT-15-048, 

Federal Highway Administration, 

Washington, DC.

10. Office of Management and Budget. 

(2003). Circular A-4: Regulatory 

Analysis. USDOT, Washington, DC.

11. Council, F., Zaloshnja, E., Miller, 

T., and Persaud, B. (2005). Crash 

Cost Estimates by Maximum 

Police-Reported Injury Severity 

Within Selected Crash Geometries, 

Report No. FHWA-HRT-05-051, 

Federal Highway Administration, 

Washington, DC. 

12. U.S. Department of Transportation. 

(2014). Guidance on Treatment of 

the Economic Value of a Statistical 

Life (VSL) in U.S. Department of 

Transportation Analyses—2014 

Adjustment, Memorandum, USDOT, 

Washington, DC. Available online: 

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