Research, Development, and Technology

Turner-Fairbank Highway Research Center

6300 Georgetown Pike

McLean, VA 22101-2296

Safety Evaluation of Increasing

Retroreflectivity of STOP Signs

Publication No. FHWA-HRT-08-041 March 2008
FOREWORD 

The goal of this research was to evaluate and estimate the safety effectiveness of increasing 

retroreflectivity of STOP signs as one of the strategies in the Evaluation of Low-Cost Safety 

Improvements Pooled Fund Study (ELCSI-PFS), Phase I. 

The ELCSI-PFS provides Crash Reduction Factor (CRF) and economic analysis for the targeted 

safety strategies where possible. The estimate of effectiveness for increasing retroreflectivity of 

STOP signs was determined by conducting scientifically rigorous before-after evaluations at 

sites where this strategy was implemented in the United States. 

This safety improvement and all other targeted strategies in the ELCSI-PFS are identified as lowcost strategies in the NCHRP Report 500 guidebooks. Participating States in the ELCSI-PFS are 

Arizona, California, Connecticut, Florida, Georgia, Illinois, Indiana, Iowa, Kansas, Kentucky, 

Maryland, Massachusetts, Minnesota, Mississippi, Montana, New York, North Carolina, North 

Dakota, Oklahoma, Pennsylvania, South Carolina, South Dakota, Tennessee, Texas, Utah, and 

Virginia. 

Michael F. Trentacoste 

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 REPORT DOCUMENTATION PAGE 

1. Report No. 

FHWA-HRT-08-041 

2. Government 

Accession No. 

3. Recipient’s Catalog No. 

 

5. Report Date 

December 2007 

4. Title and Subtitle 

Safety Evaluation of Increasing Retroreflectivity of STOP Signs 

6. Performing Organization Code

7. Author(s) 

Persaud, Bhagwant; Craig Lyon; Kimberly Eccles; Nancy Lefler; and Roya Amjadi 

8. Performing Organization 

Report No. 

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

Vanasse Hangen Brustlin, Inc (VHB) Persaud Lyon, Inc 

8300 Boone Blvd., Ste. 700 87 Elmcrest Road 

Vienna, VA 22182-2626 Toronto, Ontario M9C 3R7 

 Canada

11. Contract or Grant No. 

DTFH61-05-D-00024 (VHB) 

13. Type of Report and Period 

Safety Evaluation 

Covered 1997–2004

12. Sponsoring Agency Name and Address 

U.S. Department of Transportation 

Federal Highway Administration 

Office of Safety–HSST 

1200 New Jersey Avenue, SE 

Washington, DC 20590

14. Sponsoring Agency Code 

FHWA

15. Supplementary Notes 

The FHWA (Office of Safety Research and Development) managed this study. The project team members were Kim Eccles, 

Nancy Lefler, Dr. Hugh McGee, Dr. Frank Gross, Dr. Forrest Council, Ram Jagannathan, Dr. Bhagwant Persaud, Craig Lyon, Dr. 

Raghavan Srinivasan, and Daniel Carter. The FHWA Office of Safety Research and Development Contract Task Order Manager 

was Roya Amjadi. 

16. Abstract 

The Federal Highway Administration (FHWA) organized a Pooled Fund Study of 26 States to evaluate low-cost safety strategies 

as part of its strategic highway safety effort. One of the strategies chosen to be evaluated for this study was STOP signs with

increased retroreflectivity. This strategy is intended to reduce the frequency of crashes related to driver unawareness of stop control 

at unsignalized intersections. 

Geometric, traffic, and crash data were obtained at unsignalized intersections for 231 sites in Connecticut and 108 sites in South 

Carolina. In each case, the strategy was implemented as a blanket application of STOP signs with increased retroreflectivity. 

Empirical Bayes (EB) methods were incorporated in a before-after analysis to determine the safety effectiveness of increasing the 

sign retroreflectivity. There was a statistically significant reduction in rear-end crashes in South Carolina. Based on the results of 

the disaggregate analysis, reductions in crashes were found at three-legged intersections and at intersections with low approach 

volumes. The analysis also indicated a slight reduction in nighttime- and injury-related crashes in Connecticut and South Carolina, 

but the results were not statistically significant. It was determined that a sample size much larger than that available would be 

needed to detect a significant effect in these types of crashes. Given the very low cost of installing STOP signs with increased 

retroreflectivity, even with conservative assumptions, only a very modest reduction in crashes is needed to justify their use. 

Therefore, this strategy has the potential to reduce crashes cost-effectively, particularly at lower volume intersections.

17. Key Words: Retroreflectivity, STOP signs, Low cost, Safety 

improvements, Safety evaluations, Empirical Bayesian, Unsignalized 

intersections 

18. Distribution Statement 

No restrictions. 

19. Security Classif. (of this report) 

Unclassified 

20. Security Classif. (of this page) 

Unclassified 

21. No. of Pages 

41 

22. Price 

Form DOT F 1700.7 (8-72) Reproduction of completed pages authorized
ii
TABLE OF CONTENTS 

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

INTRODUCTION......................................................................................................................... 1 

Background on Strategy.............................................................................................................. 1 

Background on Study.................................................................................................................. 3 

Literature Review........................................................................................................................ 4 

OBJECTIVE.................................................................................................................................. 4 

STUDY DESIGN........................................................................................................................... 5 

METHODOLOGY........................................................................................................................ 8 

DATA COLLECTION ............................................................................................................... 10 

Connecticut Data Collection ..................................................................................................... 10 

Background ........................................................................................................................... 10 

Installation Data ................................................................................................................... 10 

Roadway Data....................................................................................................................... 11 

Traffic Data........................................................................................................................... 11 

Crash Data ............................................................................................................................ 11 

South Carolina Data Collection................................................................................................. 12 

Background ........................................................................................................................... 12 

Installation Data ................................................................................................................... 12 

Roadway Data....................................................................................................................... 12 

Traffic Data........................................................................................................................... 12 

Crash Data ............................................................................................................................ 13 

Summary of Data ...................................................................................................................... 13 

DEVELOPMENT OF SPFS....................................................................................................... 15 

RESULTS..................................................................................................................................... 17 

Aggregate Analysis ................................................................................................................... 17 

Disaggregate Analysis............................................................................................................... 19 

ECONOMIC ANALYSIS........................................................................................................... 21 

SUMMARY.................................................................................................................................. 22 

CONCLUSION............................................................................................................................ 23 

iii
APPENDIX A: SOUTH CAROLINA SAFETY PERFORMANCE FUNCTIONS (SPFS) 24 

APPENDIX B: CONNECTICUT SPFS.................................................................................... 27 

ACKNOWLEDGEMENTS........................................................................................................ 31 

REFERENCES............................................................................................................................ 32 

iv
LIST OF FIGURES 

Figure 1. Relative Visual Comparison of Sheeting Types.............................................................. 3

LIST OF TABLES 

Table 1. Before Period Crash Rate Assumptions.............................................................................6 

Table 2. Minimum Required Before Period Intersection-Years for Treated Sites for Three Crash 

Rate Assumptions.............................................................................................................................7 

Table 3. Definitions of Crash Types ..............................................................................................13 

Table 4. Data Summary for South Carolina Sites (n = 108)..........................................................14 

Table 5. Data Summary for Connecticut Sites (n = 231)...............................................................15 

Table 6. Results for 108 South Carolina Strategy Sites.................................................................18 

Table 7. Results for 231 Connecticut Strategy Sites......................................................................18 

Table 8. Combined Results for 339 South Carolina and Connecticut Strategy Sites....................19 

Table 9. Results of the Disaggregate Analysis...............................................................................20 

Table 10. Total—All Severities .....................................................................................................24 

Table 11. Injury—All Types..........................................................................................................24 

Table 12. Right-Angle—All Severities..........................................................................................25 

Table 13. Rear-End—All Severities...............................................................................................25 

Table 14. Day—All Severities and Types......................................................................................26 

Table 15. Night—All Severities and Types...................................................................................26 

Table 16. Total—All Severities .....................................................................................................27 

Table 17. Injury—All Types..........................................................................................................27 

Table 18. Right-Angle—All Severities..........................................................................................28 

Table 19. Rear-End—All Severities...............................................................................................28 

Table 20. Day—All Severities and Types......................................................................................29 

Table 21. Night—All Severities and Types...................................................................................29 

v
ABBREVIATIONS AND SYMBOLS 

Abbreviations 

 A Injury, incapacitating 

 AADT Average annual daily traffic 

 ADT Average daily traffic 

AASHTO American Association of State Highway Transportation Officials 

 ANOVA Analysis of variance 

ASTM American Society for Testing and Materials 

B Injury, nonincapacitating 

 C Possible injury 

 ChiSq Chi-Squared 

 ConnDOT Connecticut Department of Transportation 

 DF Degrees of freedom 

 EB Empirical Bayes 

 FHWA Federal Highway Administration 

 ft Feet 

 HPMS Highway Performance Monitoring System 

 KABCO Scale used to represent injury severity in crash reporting 

 K Fatality 

 mi Miles 

NCHRP National Cooperative Highway Research Program 

NHTSA National Highway Traffic Safety Administration 

 O Property damage only 

Pr Probability 

 SCDOT South Carolina Department of Transportation 

 SPF Safety performance functions 

 Stddev Standard deviation 

 TRB Transportation Research Board 

 UCONN University of Connecticut 

 Var Variance 

vi
Symbols 

α Greek letter Alpha 

β Greek letter Beta 

Δ Greel letter Delta 

λ Greek letter Lamda 

π Greek letter Pi 

θ Greek letter Theta 

vii

EXECUTIVE SUMMARY 

The Federal Highway Administration (FHWA) organized a Pooled Fund Study of 26 States to 

evaluate low-cost safety strategies as part of its strategic highway safety effort. The purpose of 

the FHWA Low-Cost Safety Improvements Pooled Fund Study is to evaluate the safety 

effectiveness of several of the low-cost strategies through scientifically rigorous crash-based 

studies. One of the strategies chosen to be evaluated for this study was the implementation of 

STOP signs with higher retroreflectivity. This strategy is intended to reduce the frequency of 

crashes related to driver unawareness of stop control at unsignalized intersections. The safety 

effectiveness of this strategy had not previously been thoroughly documented and this study is an 

attempt to provide an evaluation through scientifically rigorous procedures. 

Geometric, traffic, and crash data were obtained at unsignalized intersections for 231 sites in 

Connecticut and 108 sites in South Carolina. In each case, the strategy was implemented as a 

blanket application of STOP signs with increased retroreflectivity. Empirical Bayes (EB) 

methods were incorporated in a before-after analysis to determine the safety effectiveness of 

increasing the sign retroreflectivity. For rear-end crashes, there was a statistically significant 

reduction in crashes in South Carolina. Based on the results of the disaggregate analysis, 

reductions in crashes were found at three-legged intersections and at intersections with low 

approach volumes. Installations at three-legged intersections (indiscriminate of urban/rural 

factor) and three-legged urban intersections in South Carolina were found to have a statistically 

significant reduction in crashes. In Connecticut, a statistically significant reduction in crashes was 

found for three-legged rural intersections. The disaggregate analysis also showed that the strategy 

is more effective at lower volumes for the minor approach. A statistically significant reduction in 

crashes was found at intersections with approaching volumes of less than 1,200 annual average 

daily traffic (AADT) in South Carolina and less than 1,000 AADT in Connecticut. The analysis 

indicated a slight reduction in nighttime- and injury-related crashes in Connecticut and South 

Carolina, but the results were not statistically significant. It was determined that a sample size 

much larger than that available would be needed to detect a significant effect in these types of 

crashes. Given the very low cost of this strategy, even with conservative assumptions, only a very 

modest reduction in crashes is needed to justify its use. Therefore, this strategy has the potential 

to reduce crashes cost effectively, particularly at lower volume intersections. 

INTRODUCTION 

Background on Strategy 

Intersections account for a small portion of the total highway system, yet in 2005, approximately 

2.5 million intersection-related crashes occurred, representing 41 percent of all reported crashes. 

Intersection-related crashes account for more than 50 percent of total crashes in urban areas and 

over 30 percent of total crashes in rural areas. In addition, 8,655 fatal crashes (22 percent of the 

total 39,189 fatal crashes) occurred at or within an intersection environment in 2005.(1) The high 

frequency of crashes is not surprising, however, due to the fact that intersections present more 

points of conflict than non-intersections.

1
Unsignalized intersections often present potential hazards not associated with signalized 

intersections because of the priority of movement on the major roadway. This is often 

problematic on two-lane highways. Unsignalized intersections are usually found along low- to 

moderate-volume roads in rural and suburban areas that are generally associated with higherspeed travel than those in more developed suburban and urban areas.(2)

Driver compliance with intersection traffic control devices is vital to intersection safety. At stopcontrolled intersections, drivers on the stop-controlled approach must identify and observe the 

STOP sign. Therefore, the STOP sign must be visible and conspicuous. This is particularly 

important during nighttime or other reduced visibility conditions such as rainy weather. One 

method to increase both the visibility and conspicuity of STOP signs is to use higher 

retroreflectivity sheeting. 

Retroreflectivity is the property of a material that reflects a large portion of the light directly back 

to the source, through a wide range on angles of incidence of illumination. When applied to a 

sign, retroreflective sheeting will redirect light from the driver’s headlights back to the driver’s 

eyes. The amount of light from an object reaching the driver’s eyes will have a great impact on 

the ability of a driver to see that object. Retroreflective materials use micro-sized glass beads, 

either enclosed or encapsulated, or microprisms (cube corner reflectors) in the sign sheeting 

material. Variations in the technology result in differing levels of retroreflectivity. A higher 

retroreflectivity measure will return a greater amount of light to the driver’s eye at night, hence 

making the retroreflective object more visible.(3) While the difference in sign brightness 

(retroreflectivity) provided by different sheeting types cannot be illustrated adequately by 

photography, figure 1 does provide a relative visual comparison of STOP signs with six different 

grades of retroreflective sheeting. The American Society for Testing and Materials (ASTM) 

develops technical standards for industry worldwide. This includes retroreflective sheeting which 

is included in ASTM’s D495—Standard Specification for Retroreflective Sheeting and Traffic 

Control.

(4) ASTM Type I and II are commonly known as Engineering Grade and SuperEngineering Grade, respectively. Both are made with glass bead compositions. ASTM Type III, 

commonly known as High Intensity, is made with an encapsulated glass bead technology, while 

Types VII, VIII, and IX are manufactured with microprismatic technology.(4) 

2
Figure 1. Image. Relative Visual Comparison of Sheeting Types. 

The strategy to change to STOP signs with higher retroreflectivity was implemented in 

Connecticut from 1998 to 2001 and in South Carolina from 1997 to 2004 in an effort to reduce 

crashes at unsignalized intersections. 

Background on Study 

In 1997, the American Association of State Highway and Transportation Officials (AASHTO) 

Standing Committee for Highway Traffic Safety, with the assistance of the FHWA, the National 

Highway Traffic Safety Administration (NHTSA), and the Transportation Research Board (TRB) 

Committee on Transportation Safety Management, met with safety experts in the field of driver, 

vehicle, and highway issues from various organizations to develop a strategic plan for highway 

safety. These participants developed 22 key areas that affect highway safety. One of these areas is 

unsignalized intersection crashes. 

The National Cooperative Highway Research Program (NCHRP) published a series of 

implementation guides to advance the implementation of countermeasures targeted to reduce 

crashes and injuries. Each guide addresses one of the 22 emphasis areas and includes an 

introduction to the problem, a list of objectives for improving safety in that emphasis area, and 

strategies for each objective. Each strategy is designated as proven, tried, or experimental. Many 

of the strategies discussed in these guides have not been rigorously evaluated; about 80 percent of 

the strategies are considered tried or experimental. 

The FHWA organized a Pooled Fund Study of 26 States to evaluate low-cost safety strategies as 

part of this strategic highway safety effort. The purpose of the Pooled Fund Study is to evaluate 

the safety effectiveness of several tried and experimental low-cost safety strategies through 

scientifically rigorous crash-based or simulation-based studies. Based on inputs from the Pooled 

Fund Study Technical Advisory Committee and the availability of data, installing higher 

3
retroreflective STOP signs was selected as a strategy that should be evaluated as part of this 

effort. 

Literature Review 

The literature review did not uncover any studies that specifically evaluated the safety effects, in 

terms of crash frequency and severity measures, of increasing retroreflectivity levels of STOP 

signs. There has been research, however, that shows increased driver visibility distance provided 

by increased retroreflectivity levels. This research includes a study by Carlson and Hawkins, 

which investigated the legibility effects of increasing the retroreflectivity of freeway guide 

signs.(5) In this study, ASTM Type III and Type IX retroreflective sheeting were analyzed. A 

total of 60 subjects, both young and old, participated in this nighttime study. The measure of 

effectiveness used in this study was legibility distance. The statistical test used was a mixedfactor repeated-measures analysis of variance (ANOVA). The ANOVA test indicated that 

sheeting type was statistically significant (F1,116 = 34.69, p-value < 0.0001). The improvement 

associated with increasing the retroreflectivity was nearly a 10-percent (16.2-m (53.0-ft)) increase 

in visibility distance. The link between visibility and crashes has not been established; therefore, 

no safety inference can be made from this finding. 

OBJECTIVE 

This research examined the change in crash frequency due to increasing the retroreflectivity of 

STOP signs at unsignalized intersections. The desired objective was to identify sites with crashes 

related to poor visibility due to the retroreflectivity of the STOP sign in the before period and 

estimate the expected change in crashes due to increasing the reflectivity using the EB method. 

While this is a worthy objective, it was not possible to determine those sites that had poor 

retroreflectivity in the before period because this was a blanket strategy. Although the type of 

sign (Type I, Engineer Grade) used in the before period was known, the exact condition including 

age of the signs or any degree of deterioration that occurred on each of the signs was not known. 

In addition, there were very few nighttime crashes, which made it difficult to identify a sufficient 

sample of sites that had crashes related to low retroreflectivity. Therefore, the objective was 

modified to estimate, in general, the safety effectiveness of this strategy as measured by crash 

frequency. Target crash types considered included the following: 

• All intersection-related crash types. 

• Injury crashes (includes K, A, and B on KABCO scale). 

• Right-angle (side impact) crashes. 

• Rear-end crashes. 

• Daytime crashes. 

• Nighttime crashes. 

4
The range of safety effects was expected to vary by crash type; therefore, a second objective was 

to estimate, if necessary, the overall effect of the strategy by considering the economic costs by 

crash type and crash severity using crash costs recently developed for FHWA.(6)

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

• Do effects vary by traffic volumes? 

• Do effects vary by approach speeds? 

• Do effects vary by number of lanes? 

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

tasks. These were: 

• The need to select a large enough sample size to detect, with statistical significance, what 

may be small changes in safety for some crash types. 

• The need to properly account for traffic volumes changes. 

• The need to pool data from more than one jurisdiction to improve reliability of the results 

and facilitate broader applicability of the products of the research. 

STUDY DESIGN 

The study design involved a sample size analysis and the prescription of needed data elements. 

The sample size analysis assessed the size of sample required to statistically detect an expected 

change in safety. Assumptions on the expected safety effects, on the average crash frequency at 

potential strategy sites in the before period, and on the average number of after period years of 

available data are basic to estimating sample sizes. Following a literature review and the 

application of methodology in Hauer, a minimum sample size was estimated.(7)

For this analysis, it was assumed at the time that the study was designed that a conventional 

before-after study with comparison group design would be used, since available sample size 

estimation methods are based on this assumption. To facilitate the analysis, it was also assumed 

that the number of comparison sites is equal to the number of strategy sites. The sample size 

estimates provided would be conservative in that state-of-the-art EB before-after methodology 

actually proposed for the evaluations would require fewer sites. 

Sample sizes were estimated for various assumptions of likely safety effect and crash frequencies 

before the strategy was installed. Table 1 provides the crash frequency assumptions used. Rate A 

is based on a Minnesota study.(8) Rate B is based on an Ohio Study.(9) Rate C is based on 

Minnesota data from FHWA-RD-03-0037.(10) Right-angle and rear-end proportions were adopted 

from SafetyAnalyst development data.(11) The literature review provided no sound basis for an 

assumption on the expected safety effect. Thus, the analysis was based on logical values for this 

parameter. 

5
Table 1. Before Period Crash Rate Assumptions. 

Crash Type Rate A (crashes/ 

intersection/year) 

Rate B (crashes/ 

intersection/year) 

Rate C (crashes/ 

intersection/year) 

All 3.45 7.62 0.44 

Right-Angle (39% 

of total assumed) 1.35 2.97 0.17 

Rear-End (23% of 

total assumed) 0.79 1.75 0.10 

Table 2 provides estimates of the required number of before period intersection-years in the 

sample for both the 90-percent and 95-percent confidence levels. The calculations assume equal 

number of intersection-years for strategy and comparison sites and equal length of before and 

after periods. Intersection-years are the number of intersections where the strategy was applied 

multiplied by the number of years the strategy was in place at each intersection. For example, if a 

strategy was applied at nine intersections and has been in place for three years at all 

9 intersections, this is 27 intersection-years. 

A minimum sample size of 1,076 intersection-years and a desirable sample size of 2,036 

intersection-years per period were calculated as shown in bold in table 2. It was expected that 

these sample sizes could be reduced if the assumption for crashes per intersection-year before 

strategy implementation turns out to be conservatively low for strategy data, or if more after 

period years than assumed are available. The desirable sample assumes that the reduction in 

crashes could be as low as a 10-percent reduction in all crashes and that this is the smallest 

benefit that one would be interested in detecting with 90-percent confidence. The logic behind 

this approach is that safety managers may not wish to implement a measure that reduces crashes 

by less than 10 percent, and the required sample size to detect a reduction smaller than 10 percent 

would likely be prohibitively large. The minimum sample indicates the level for which a study 

seems worthwhile (i.e., it is feasible to detect with 90-percent confidence the largest effect that 

may reasonably be expected based on what is known currently about the strategy). In this case, a 

20-percent reduction in right-angle crashes was assumed as this upper limit on safety 

effectiveness. 

These sample sizes could be reduced if the assumption for crashes per intersection-year before 

strategy implementation turned out to be conservatively low for strategy data or if there are more 

after period years of data available than assumed. 

6
Table 2. Minimum Required Before Period Intersection-Years for Treated Sites for Three 

Crash Rate Assumptions. 

Expected 95% Confidence 90% Confidence 

Percent 

Reduction in 

Crashes A B C A B C 

5 1,629 738 12,773 1,141 516 8,943 

10 371 168 2,907 260 118 2,036 

20 76 34 594 53 24 416 

30 27 12 211 19 9 147 

All 

40 12 5 92 8 4 64 

5 4,163 1,892 33,060 2,915 1,325 23,146 

10 948 431 7,525 663 302 5,268 

20 194 88 1,537 135 62 1,076 

30 69 31 545 48 22 381 

Right 

Angle 

40 30 14 237 21 10 166 

5 7,114 3,212 56,203 4,981 2,249 39,349 

10 1,619 731 12,793 1,134 512 8,956 

20 331 149 2,612 232 105 1,829 

30 117 53 926 82 37 648 

Rear 

End 

40 51 23 403 36 16 282 

Note: Bold denotes the calculated minimum and desirable sample size for 

intersection-years per period. 

7
METHODOLOGY 

The EB methodology for observational before-after studies was used for the evaluation.(7) This 

methodology is rigorous in that it accomplishes the following: 

 

• It properly accounts for regression-to-the-mean. 

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

between the before and after periods. 

• It reduces the level of uncertainty in the estimates of safety effect. 

• It provides a foundation for developing guidelines for estimating the likely safety 

consequences of contemplated strategy. 

• It properly accounts for differences in crash experience and reporting practice in 

amalgamating data and results from diverse jurisdictions. 

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

(1)

Δ Safety = λ - π ,

Where: 

λ is the expected number of crashes that would have occurred in the after 

 period without the strategy. 

π is the number of reported crashes in the after period. 

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

explicitly accounted for using safety performance functions (SPFs) relating crashes of different 

types to traffic flow and other relevant factors for each jurisdiction based on untreated sites. 

Annual SPF multipliers were calibrated to account for the temporal effects on safety of variation 

in weather, demography, crash reporting, and so on. 

In the EB procedure, the SPF is 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. The sum of these annual SPF estimates (P) is 

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 strategy. This estimate of m is: 

)()( = 1 + 2 Pwxwm (2)

Where: 

w1 and w2 are estimated from the mean and variance of the SPF estimate as: 

8
k P

P

w 1 1

+ =

 (3)

( 1 )

1

2

k Pk

w

+ =

 (4) 

Where: 

 k is a constant for a given model and is estimated from the SPF calibration 

 process with the use of a maximum likelihood procedure. (In that 

 process, a negative binomial distributed error structure is assumed with k 

 being the dispersion parameter of this distribution.) 

A factor is 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 is 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, is an estimate of λ. The procedure also produces an estimate 

of the variance of λ. 

The estimate of λ is then summed over all sites in a strategy group of interest (to obtain λsum) and 

compared with the count of crashes during the after period in that group (πsum). The variance of λ

is also summed over all sites in the strategy group. 

The Index of Effectiveness (θ ) is estimated as: 

⎟

⎠

⎞ ⎜

⎝

⎛ +

=

2

)( 1 sum

sum

sum

sum

Var

λ

λ

λ

π

θ

. (5) 

The standard deviation of θ is given by: 

2

2

2 2

2

)( 1

)()(

)(

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

+

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

+

=

sum

sum

sum

sum

sum

sum

Var

Var Var

StDev

λ

λ

λ

λ

π

π θ

θ

. (6) 

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

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

deviation of 12 percent. 

9
DATA COLLECTION 

A survey was conducted to collect data for several low-cost strategies. Two States, Connecticut 

and South Carolina, responded that they had installed a large number of STOP signs with 

increased retroreflectivity as a blanketed effort across the State to improve safety. In addition to 

the locations and dates of the STOP signs, additional data including roadway geometry, traffic, 

and crash data were collected in order to conduct the evaluation. This section provides a 

summary of the data assembled for the analysis. 

Connecticut Data Collection 

Background 

The Connecticut Department of Transportation (ConnDOT) replaced over 7,000 STOP signs 

(R1-1) on State highways and town roads approaching State highways from December 1998 to 

May 2001. The signs were replaced as part of a comprehensive replacement program. The overall 

motivation for the effort was traffic safety. However, individual locations were not selected based 

on crash experience. Instead, the replacement was a blanketed effort at all stop-controlled 

intersections. 

The existing STOP signs were made up of Type I, Engineer Grade reflective sheeting. The exact 

condition before replacement of each of the signs, including the age of the signs and the degree of 

deterioration, was unknown. The sheeting was upgraded to a material that provides relatively 

high retroreflectivity at large observational angles, which was designated by the ASTM as 

Type IX sheeting. (At the time the signs were installed there was not a Type IX reflective 

sheeting ASTM designation.) 

ConnDOT provided installation data, roadway, and traffic data for use in this study. The data 

collected were entered into a database, designed specifically for use in this evaluation, and 

matched to crash data supplied by the University of Connecticut (UCONN). Details on the data 

are provided in the following sections. 

Installation Data 

The installation data provided by ConnDOT contained the town name, route number, intersecting 

road name, size of the sign, and the date the sign was replaced. Of the 7,000 sign locations 

provided by ConnDOT, 231 intersections were included in the evaluation. The primary 

motivation for selecting these 231 intersections was the availability of traffic volume data. This is 

discussed in the section on traffic data. 

Of the intersections used in the evaluations, 762-mm (30-inch) STOP signs were installed at 218 

intersections, 1,219.2-mm (48-inch) STOP signs were installed at 11 intersections, and a 

combination of 762-mm (30-inch) and 1,219.2-mm (48-inch) STOP signs were installed at the 

remaining 2 intersections. 

10
Roadway Data 

ConnDOT provided access to a 2004 electronic photo log of the roadway system. Roadway data 

were collected for each intersection from the photo log. This included the intersection log mile, 

number of intersection approaches, number of roadway lanes per approach, presence of a 

shoulder on each approach, presence of a median on each approach, presence of other warning 

measures (e.g., STOP AHEAD warning signs), and intersection illumination. 

There was a concern that some of the STOP signs used in the evaluation had received subsequent 

strategies such as a signal or a flashing beacon. Based on the photo logs, 18 intersections were 

identified that had been signalized or a flashing beacon had been installed since the STOP signs 

were replaced. A list of energized signals, provided by ConnDOT, helped to identify other 

intersections that had received a signal, and the signalization date. ConnDOT also provided the 

dates of the flashing beacons installations. 

Traffic Data 

The primary reason many intersections were excluded from the evaluation was the lack of traffic 

volume data. In order to be included in the evaluation, traffic volume on the major road was 

needed both before and after the sign was replaced. In addition, there had to be a traffic count on 

the minor roadway in at least the before or after period, although both were preferred. 

Volume data are available from three sources in Connecticut: average daily traffic (ADT) maps, 

electronic count data in a spreadsheet format, and special counts. The ADT maps are available in 

both hard copy and electronic (.pdf) formats from 1999 to 2004. Traffic counts are conducted 

every three years to develop these maps. The count locations vary from year to year; not all 

locations were counted on each map. The electronic count data contained the same information as 

the ADT maps but for a longer period. Electronic count data are available from 1995 to 2006. For 

the purposes of extrapolating counts from nearby intersections, spatial count maps (i.e., the ADT 

maps) are preferable to tabular count data. 

The third source of volume data are special counts. ConnDOT provided paper copies of special 

counts. These are volume counts that are requested for a variety of reasons including 

signalization studies, citizen complaints, and traffic operations analysis. 

The stop-controlled roadway was considered the minor roadway for this study. In most cases, this 

was also the lower volume roadway. There were a few three-legged intersections where the stopcontrolled approach had a higher ADT than the nonstop-controlled approach. Therefore, there 

were a few intersections in the database where the minor roadway had a higher ADT than the 

major roadway. 

Crash Data 

The Connecticut Transportation Institute at the UCONN provided crash data from 1995 to 2004 

for this study. These data were originally provided to UCONN by the ConnDOT. UCONN 

formatted the raw data into a more user-friendly version. These data included all crashes on 

State-maintained roadways and crashes on non-State-maintained roadways that occurred within 

11
0.02 km (0.01 mi) of an intersection with a State-maintained roadway. Therefore, all intersections 

in this study included at least one State-maintained roadway. 

During the evaluation, it was discovered that there were log-mile changes throughout the study 

period. That is, the same intersection could have two different log miles in two different years. 

This was due to changes in the Connecticut roadway system. ConnDOT supplied a file of where 

log mile changes have occurred. These were used to resolve the log mile changes. 

South Carolina Data Collection 

Background 

District 1 of the South Carolina Department of Transportation (SCDOT) conducted a 

comprehensive replacement of over 6,000 STOP signs from 1997 to 2005. District 1 is located in 

central South Carolina and is comprised of Aiken, Kershaw, Lee, Lexington, Richland, and 

Sumter Counties. Data from Kershaw and Lexington counties were used for this study. 

The existing signs were made of Type I Engineer Grade reflective sheeting. The exact age and 

condition of the signs prior to replacement was unknown. They were replaced with signs that had 

Type III high-intensity reflective sheeting. The signs were replaced as part of a comprehensive 

replacement program. 

SCDOT provided installation data, roadway, traffic, and crash data for use in this study. The data 

collected were entered into a database, designed specifically for use in this evaluation, and 

matched to crash data. Details on the data are provided in the following sections. 

Installation Data 

SCDOT provided a database of sign installations. For each sign, the database included the 

county, route, milepoint, direction, installation date, and sheeting type. Out of the more than 

6,000 signs, 108 intersections were used in the evaluation. As with the Connecticut data, this was 

largely due to the availability of the traffic data. Of the 108 intersections, 93 had 762-mm 

(30-inch) STOP signs installed, and 15 had 1,219.2-mm (48-inch) STOP signs installed. 

Roadway Data 

SCDOT provided a copy of the roadway and traffic data that were collected for the Highway 

Performance Monitoring System (HPMS). The HPMS is a national highway information system 

that includes data on the extent, condition, performance, use, and operating characteristics of the 

Nation’s highways. This database provided data on the land use (i.e., urban or rural), number of 

lanes, lane width, presence of a shoulder, shoulder width, presence of a median, and median type 

for each intersection approach. The speed limit was also available for 35 of the intersections. 

Traffic Data 

The majority of the traffic volumes used for this study were extracted from the HPMS files. 

These values came from a segment of roadway ranging from 0.16 to 8.05 km (0.1 to 5.0 mi) or 

more in length. The range was less in urban areas and greater in rural areas. Therefore, in rural 

12
areas, the volume count used to describe the volume entering the intersection may be collected 

from a point up to 8.05 km (5 mi) from the intersection. There were no records of where in the 

segment the count was actually collected. 

 

For those locations where AADTs were not available through the HPMS, the AADT numbers 

were calculated from turning movement counts. Factors supplied by SCDOT were used to 

calculate the AADTs from the raw turning movement count data. 

Crash Data 

SCDOT supplied crash data in two databases. One database contained crashes occurring from 

1994 to 2000. The second database contained crashes occurring from 2001 to 2005. The second 

database system was necessary because the crash data report and some associated variables were 

modified in 2001. In addition, prior to 1997, there was no threshold on reporting property damage 

only crashes. Starting in 1997, only crashes involving an injury or property damage greater than 

$1,000 were reported in the system. 

Based on guidance from the SCDOT, the crash milepost was not used to locate crashes. Instead, 

the variable “base-offset distance” was used to identify crashes occurring at intersections. 

Summary of Data 

The analysis included a total of 3,323.8 intersection-years of data (2,038.6 intersection-years 

from CT and 1,285.2 intersection-years from SC). This sample was greater than the 1,076 

intersection-years estimated in the study design required to detect a 20-percent reduction in rightangle crashes and the 2,036 intersection-years required to detect a 10-percent reduction in all 

crashes. 

Table 3 provides crash definitions used in the two States. This information is crucial in applying 

the safety effect estimates in other jurisdictions. 

Table 3. Definitions of Crash Types. 

State Intersection-Related Injury Right-Angle Rear-End Daytime Nighttime 

SC Within 264 ft of 

intersection 

K, A or B 

on KABCO 

scale 

Defined as 

angle 

Defined as 

rear-end 

Daylight, 

dawn, 

dusk 

Dark 

CT 

Within 264 ft of 

intersection, within 

0.01 mi on minor 

K, A or B 

on KABCO 

scale 

Defined as 

angle or 

turningintersecting 

paths 

Defined as 

rear-end 

Daylight, 

dawn, 

dusk 

Dark 

1 ft = 0.305 m 

1 mi = 1.61 km 

13
Table 4 and table 5 provide summary information for the data collected. This information should 

not be used to make simple before-after comparisons of crashes per site-year since such an 

analysis would not account for factors other than the strategy that may cause safety to change 

between the two periods. Such comparisons are properly done with the EB analysis as presented 

in subsequent sections. 

Table 4. Data Summary for South Carolina Sites (n = 108). 

Variable Mean Minimum Maximum 

Months before 100.7 45.0 144.0 

Months after 42.1 2.0 99.0 

Crashes/site-year before 2.1 0.0 16.1 

Crashes/site-year after 2.0 0.0 18.9 

Injury crashes/site-year before 0.7 0.0 3.8 

Injury crashes/site-year after 0.6 0.0 6.0 

Right-angle crashes/site-year before 0.8 0.0 7.1 

Right-angle crashes/site-year after 0.7 0.0 6.4 

Rear-end crashes/site-year before 0.7 0.0 7.1 

Rear-end crashes/site-year after 0.7 0.0 12.9 

Daytime crashes/site-year before 1.7 0.0 13.9 

Daytime crashes/site-year after 1.6 0.0 15.4 

Nighttime crashes/site-year before 0.4 0.0 2.5 

Nighttime crashes/site-year after 0.4 0.0 3.7 

Major road AADT before 9,847 413 53,587 

Minor road AADT before 2,017 218 7,970 

Major road AADT after 10,414 344 57,353 

Minor road AADT after 2,139 206 9,178 

14
Table 5. Data Summary for Connecticut Sites (n = 231). 

Variable Mean Minimum Maximum 

Months before 59.7 48.0 84.0 

Months after 46.2 3.0 60.0 

Crashes/site-year before 1.9 0.0 18.9 

Crashes/site-year after 2.4 0.0 32.0 

Injury crashes/site-year before 0.7 0.0 5.9 

Injury crashes/site-year after 0.8 0.0 6.7 

Right-angle crashes/site-year before 0.5 0.0 3.6 

Right-angle crashes/site-year after 0.6 0.0 4.0 

Rear-end crashes/site-year before 0.6 0.0 10.6 

Rear-end crashes/site-year after 0.9 0.0 11.8 

Daytime crashes/site-year before 1.4 0.0 14.1 

Daytime crashes/site-year after 1.8 0.0 22.0 

Nighttime crashes/site-year before 0.5 0.0 5.1 

Nighttime crashes/site-year after 0.6 0.0 10.0 

Major road AADT before 7,690 929 29,816 

Minor road AADT before 2,033 68 18,074 

Major road AADT after 8,021 969 31,267 

Minor road AADT after 2,122 71 18,879 

DEVELOPMENT OF SPFS 

This section presents the SPFs that were developed for use in the EB methodology. Generalized 

linear modeling was used to estimate model coefficients using the software package SAS® and 

assuming a negative binomial error distribution, which is consistent with the state of research in 

developing these models. 

SPFs were calibrated separately for South Carolina and Connecticut. The reference groups used 

to develop SPFs were the same as the strategy groups since the installations were blanketed 

across the jurisdictions. The approach taken was as follows: 

1. Combine the before and after period data to develop SPFs. 

2. Recalibrate each SPF separately for the before and after periods to develop yearly 

multipliers. 

15
Since the installations were over a multiyear period, it was possible to represent yearly trends in 

crash counts in an unbiased way that would not be possible if all installations occurred in the 

same year. 

The primary form of the SPFs is: 

 (7) Crashes/year = α (maj)β1 (maj)β2

Where: 

maj is major road entering AADT. 

min is minor road entering AADT. 

α, β1 and β2 are parameters estimated from data in the SPF calibration process. 

In some cases, the separate exponents could not be estimated with significance and the following 

Safety Function (SF) form was used: 

Crashes/year = α (AADT)β0 

 (8) 

Where: 

AADT is the total entering AADT. 

β0 is a parameter estimated from data in the SPF calibration process.

Using additional variables did not significantly improve the models. In specifying a negative 

binomial error structure, the “dispersion” parameter, k, which relates the mean and variance of 

the SPF estimate and is used in equations 3 and 4 of the EB procedure, is iteratively estimated 

from the model and the data. The value of k is such that the smaller its value, the better a model is 

for a given set of data. 

The SPFs developed are presented in appendix A. Note the following in interpreting the output: 

• The value of α is obtained as the e ln(α),where ln(α) is the model output. 

• The value of the parameter k is used in the EB approach. 

• The value for Pr > ChiSq gives the level at which the estimate is significant. For example, 

Pr > ChiSq = 0.05 indicates that the parameter estimate is statistically significant at the 

5-percent level (or, alternatively, that the 95-percent confidence interval does not include 

a value of 0). 

SPFs were estimated for the following crash classifications: 

• Total (all severities and types combined). 

• Injury (all crash types combined). 

• Right angle (all severities combined). 

• Rear end (all severities combined). 

16
• Day (all severities and types combined). 

• Night (all severities and types combined). 

RESULTS 

Based on the data, two sets of results were calculated and are presented in the following sections. 

One set contains aggregate results for each jurisdiction and for the two combined; the other set is 

based on a disaggregate analysis that attempted to discern factors that may be most favorable to 

increasing STOP sign retroreflectivity. 

Aggregate Analysis 

The aggregate results are shown in tables 6 through 8. Results significant at the 95-percent 

confidence level are bolded. Note that a negative sign indicates an increase in crashes. 

The results indicate that there may be a slight effect in South Carolina, but this effect is too small 

to detect with statistical significance, as evidenced by the relatively large standard errors 

(i.e., substantially greater than one-half of the estimated effect). The exception is for rear-end 

crashes, for which the reduction in crashes is significant at the 95-percent confidence level as 

shown in table 6. 

The aggregate effects are negligible and statistically insignificant for Connecticut, and for the two 

jurisdictions combined. There are no detectable effects for nighttime crashes, the primary targets 

of this measure, which is likely a result of the reality that there are relatively few of these crashes 

at the strategy sites. 

These inconclusive results and the fact that they are based on non-selective implementations 

emphasize the need for a disaggregate analysis to see if significant effects can be detected for 

specific conditions. This analysis is presented in the next section. 

17
Table 6. Results for 108 South Carolina Strategy Sites. 

Right-Angle Rear-End Night Day Injury Total

EB estimate of crashes expected in 

the after period without strategy 266.5 257.4 134.5 559.6 220.1 692.9

Count of crashes observed in the 

after period 247 213 141 515 200 656 

Estimate of percent reduction 7.6% 17.5% -4.4% 9.1% 9.4% 5.4% 

Standard error 7.6 7.3 10.8 5.3 8.1 4.9 

Notes: Bold denotes results significant at the 95% confidence level. The negative sign indicates 

an increase in crashes. 

Table 7. Results for 231 Connecticut Strategy Sites. 

Right-Angle Rear-End Night Day Injury Total 

EB estimate of crashes expected in 

the after period without strategy 483.3 663.6 510.8 1494.8 700.1 2,019.2

Count of crashes observed in the 

after period 512 729 478 1543 659 2025 

Estimate of percent reduction -5.8% -9.7% 6.6% -3.2% 6.0% -0.2% 

Standard error 6.2 5.7 5.5 3.6 4.8 3.1 

Note: The negative sign indicates an increase in crashes. 

18
Table 8. Combined Results for 339 South Carolina and Connecticut Strategy Sites. 

Right-Angle Rear-End Night Day Injury Total 

EB estimate of crashes expected in 

the after period without strategy 749.8 921.0 645.3 2054.4 920.2 2712.1

Count of crashes observed in the 

after period 759 942 619 2058 859 2681 

Estimate of percent reduction -1.2% -2.2% 4.4% -0.1% 6.7% 1.2% 

Standard error 5.3 4.8 6.0 2.7 4.5 2.7 

Note: The negative sign indicates an increase in crashes. 

Disaggregate Analysis 

Table 9 presents the results of the disaggregate analysis. Nighttime crashes are the primary 

targets of this measure and should be the basis for this analysis; however, there are too few of 

these crashes to facilitate a disaggregate analysis. The results of the disaggregate analysis are 

based on all crashes combined. Significant results at the 95-percent confidence level are shown in 

bold. 

The three factors that provided indications of an association with crash effects are environment 

(urban versus rural), number of approach legs, and minor road entering AADT. 

19
Table 9. Results of the Disaggregate Analysis. 

Intersection Type Sites 

EB estimate of 

crashes expected 

in the after period 

without strategy 

Count of crashes 

observed in the 

after period 

Estimate of 

percent reduction

(standard error) 

SC urban 47 333.9 288 13.7% (6.7) 

SC rural 61 360.0 368 -2.0% (7.0) 

SC three-legged 48 354.7 299 15.9% (6.3) 

SC four-legged 60 338.2 357 -5.3% (7.4) 

SC three-legged, urban 20 172.9 128 26.3% (8.3) 

SC four-legged, urban 27 160.0 160 0.05% (10.6) 

SC three-legged, rural 28 181.8 171 6.3% (9.4) 

SC four-legged rural 33 178.2 197 10.2% (10.2) 

CT urban 190 1,789.5 1,830 -2.2% (3.3) 

CT rural 41 229.7 195 15.4% (8.1) 

CT three-legged 172 1,458.0 1,399 4.1% (3.5) 

CT four-legged 59 559.2 625 -11.6% (6.3) 

CT three-legged, rural 29 152.6 118 23.1% (9.2) 

CT four-legged, rural 12 75.2 76 -0.2% (15.8) 

SC < 1200 minor AADT 42 219.0 165 24.9% (7.2) 

SC > 1200 minor AADT 66 473.9 491 -3.4% (6.3) 

CT < 1000 minor AADT 90 509.0 437 14.3% (5.6) 

CT >1000 minor AADT 141 1,510.7 1,588 -5.1% (3.7) 

Notes: Bold denotes results significant at the 95% confidence level. The negative sign indicates 

an increase in crashes. 

For the urban versus rural factor, there are opposing indications from the two States, with the 

more favorable effects for rural installations in Connecticut and urban installations in South 

Carolina. 

For number of approaches (i.e., legs), a more consistent pattern emerges. For both States, in 

particular for the favored environment, installations at three-legged intersections appear to be 

more effective than at four-legged intersections. 

For minor entering AADT, there is a consistent pattern that this strategy is more effective at 

lower volumes. The boundaries of 1,200 AADT in South Carolina and 1,000 in Connecticut were 

chosen to provide the most discrimination between upper and lower AADT levels in order to 

20
indicate the effect of this factor. Therefore, these numbers should not be used in decisions on 

whether or not to install a sign. The analysis does suggest, however, that lower minor road 

locations should be given higher priority if there is a need to prioritize locations (as should threelegged intersections). 

Speculation on reasons for the differential effects found is undertaken in a discussion section later 

in the report. However, it should be pointed out that further investigation was undertaken to 

ensure that the effects found were not due to biases in the analysis. This further investigation 

involved an examination of the results of a naïve before-after study that simply compared crash 

frequencies pre- and post-strategy and did not use safety performance functions. The naïve 

before-after study yielded similar conclusions to the EB study regarding the influence of the three 

factors, but different magnitudes for the crash effects for the various groupings in table 9. The 

project team also investigated whether the findings regarding the differential effects for one 

factor may have been confounded by co-linearity of this factor with another for which similar 

effects were found. For example, this investigation revealed that the conclusion regarding minor 

road AADT was equally relevant for three-legged and four-legged intersections and for urban and 

rural intersections. As shown in table 9, the finding regarding three-legged versus four-legged 

intersections is equally valid for urban and rural environments. 

Data were available for an analysis of other possible factors that might influence crash effects. 

However, no such effects could be ascertained. The other factors examined were sign size 

(762 mm (30 inches) versus 1,219.2 mm (48 inches)), the presence of lighting (for Connecticut), 

the presence of other measures such as STOP AHEAD signs, the major road entering volume, 

and the expected crash frequency prior to strategy. For sign size, there were very few that were of 

the 1,219.2-mm (48-inch) variety and so, statistically, it was difficult to detect different crash 

effects for the two sign sizes, even if such differences exist. 

ECONOMIC ANALYSIS 

The purpose of this analysis was to determine the economic feasibility of applying this strategy. 

The life-cycle costs of the strategy were estimated and expressed as an annual cost. The crash 

benefits required to offset these costs were estimated using the most recent FHWA unit crash cost 

data for unsignalized intersections. The results of the aggregate and disaggregate analysis of 

crash effects were used to make a judgment on the circumstances that would be favorable to 

ensuring economic feasibility (i.e., circumstances that may yield a benefit cost ratio of at least 

2:1). 

Cost data provided by the two States suggest a conservatively high initial cost of about $200 per 

intersection, considering the mix of three-legged and four-legged intersections and sign sizes. 

State sources also suggest an expected sign life of 8 years, again conservatively estimated. Costs 

would be even lower if the marginal costs of replacing the signs were used. As of 2007, the 

approximate costs of sheeting are as follows: 

• Type I sheeting is $0.75 per square foot. 

• Type II sheeting is $1.25 per square foot. 

21
• Types VII, VIII, and IX are $3.50 per square foot. 

These would reflect the costs if a jurisdiction used higher retroreflective materials as part of its 

routine maintenance program, as opposed to replacing all of the existing signs across a 

jurisdiction at one time regardless of the condition of the existing signs, as was done in 

Connecticut and South Carolina. 

Based on the Office of Management and Budget suggested discount rate of 7 percent, and on the 

expected service life (8 years), the initial costs per intersection were converted to annual costs 

using the standard economics formula for a capital recovery factor. The more conservative $200 

initial cost translates into an annual cost of around $33 over the 8-year cycle, requiring an annual 

crash saving of more than $66 per intersection for a benefit cost ratio of at least 2:1. 

The most recent FHWA mean comprehensive costs per crash for unsignalized intersections are 

$13,238 for rear-end and $61,114 for right-angle crashes.(6) Comprehensive crash costs represent 

the present value, computed at a discount rate, of all costs over the victim’s expected life span 

that result from a crash. The major categories of costs used in the calculation of comprehensive 

crash costs include medical-related costs, emergency services, property damage, lost 

productivity, and monetized quality-adjusted life years.(6) By applying the more conservative 

figure, $13,238, a $66 saving would require a reduction of approximately 0.005 crashes per 

intersection per year. This is a reduction of approximately 0.5 percent for rural Connecticut 

intersections, which have an annual crash frequency of 1.11, the lowest of the four 

State/environment groups. This reflects the more conservative costs of replacing all existing signs 

across a jurisdiction at one time with signs with retroreflective material regardless of condition of 

the existing signs. 

Even with the conservative assumptions made, just a very modest reduction in crashes is required 

to justify this strategy economically. The evidence suggests that this reduction is easily 

achievable, in particular, under the circumstances identified from the disaggregate analysis. 

SUMMARY 

The objective of this study was to evaluate the safety effectiveness as measured by crash 

frequency of higher retroreflective sheeting on STOP signs at unsignalized intersections. The 

study was designed to detect a 10-percent reduction in all crashes with 90-percent confidence. 

The study also examined the effects of higher retroreflectivity on specific crash types. While it is 

desirable to evaluate the effectiveness of this strategy on related crashes (i.e., nighttime, lowvisibility), there was not a sufficient number of related crashes to determine an effect with 

confidence. 

The aggregate analysis indicates that higher retroreflective STOP signs may affect the likelihood 

of crashes at unsignalized intersections, but the effect is not detectable with the study design and 

available sample size. The exception is for rear-end crashes in South Carolina, where there was a 

significant reduction. 

The disaggregate analysis provided further insight into the circumstances where crash reductions 

were identified. Installations at three-legged intersections (indiscriminate or urban/rural factor) 

22
and three-legged urban intersections in South Carolina were found to have a statistically 

significant reduction in crashes. In Connecticut, a statistically significant reduction in crashes was 

found for three-legged rural intersections. The disaggregate analysis also showed that the strategy 

is more effective at lower volumes for motorists approaching the intersection along the minor 

road. Statistically significant reduction in crashes were found at intersections with approaching 

volumes of less than 1,200 in South Carolina and less than 1,000 in Connecticut. This volume 

related finding is expected. At higher volume intersections, there are more visual cues for the 

approaching minor road motorist that the intersection is stop-controlled. Most notably, other 

traffic stopped in front of the driver on the approach is a visual cue. 

For the urban versus rural factor, there are opposing indications from the two States, with the 

more favorable effects for rural installations in Connecticut and urban installations in South 

Carolina. There was no explanation available for these inconsistent results between the two 

States. 

There are no detectable effects for nighttime crashes. As discussed previously, this might be 

because there are relatively few of these crashes at the strategy sites. It is also likely that this is 

because these are blanket installations and the significant benefits at relatively few nighttime 

crash problem locations become diluted by the negligible effects at other locations. To establish 

the benefits for nighttime crashes with statistical significance would require a database with a 

substantial number of sites at which this strategy was implemented because of a high frequency 

of nighttime crashes perceived to be “correctable” by this strategy. The sample size required for 

such a special database would be of a similar order of magnitude to that required for the database 

for the blanketed installations. 

It should be noted, however, that the study results do not support the degradation of signs 

below any desired retroreflectivity requirements. The results are based on a blanket improvement 

with no knowledge of the previous sign conditions. This being the case, it is difficult to determine 

the safety effectiveness of more highly retroreflective sheeting on STOP signs for specific 

conditions. There was not a large enough sample size to detect any significant effects. The 

sample size required to detect a significant effect would be outside the scope of this project. As 

indicated in the FHWA Supplemental Notice of Proposed Amendments, improving sign 

retroreflectivity will be a benefit to all drivers, including older drivers.(12) All drivers need legible 

signs in order to make important decisions at key locations, such as intersections and exit ramps 

on high speed facilities. This is particularly true for regulatory and warning signs. 

CONCLUSION 

A minimal reduction in crashes can be expected with the installation of higher retroreflective 

STOP signs. However, given the very low cost of this strategy, even with conservative 

assumptions, only a very modest reduction in crashes is needed to justify their use. Therefore, 

this strategy has the potential to reduce crashes cost effectively, particularly at lower volume 

intersections. 

23
 APPENDIX A: SOUTH CAROLINA SAFETY PERFORMANCE FUNCTIONS (SPFS) 

Table 10. Total—All Severities.

Rural Urban 

Parameter 

Estimate Standard 

Error 

Pr > 

ChiSq Estimate Standard Error 

Pr > 

ChiSq 

ln(α) -7.9320 1.7537 <.0001 -10.5902 2.6984 <.0001 

β0 

β1 0.5990 0.1309 <.0001 0.6639 0.2299 0.0039 

β2 0.4331 0.1652 0.0087 0.6460 0.2170 0.0029 

k 0.6494 0.1253 1.1429 0.2385 

Note: The negative sign indicates an increase in crashes. 

Table 11. Injury—All Types. 

Note: The negative sign indicates an increase in crashes.

Rural Urban 

Parameter 

Estimate Standard 

Error 

Pr > 

ChiSq Estimate Standard Error 

Pr > 

ChiSq 

ln(α) -7.2994 2.0709 0.0004 -11.5587 2.7736 <.0001 

β0 

β1 0.4866 0.1491 0.0011 0.6890 0.2386 0.0039 

β2 0.3401 0.1912 0.0753 0.5893 0.2091 0.0048 

k 0.7322 0.1652 0.8016 0.2096 

24
Table 12. Right-Angle—All Severities.

Rural Urban 

Parameter 

Estimate Standard 

Error 

Pr > 

ChiSq Estimate Standard Error 

Pr > 

ChiSq 

ln(α) -8.9674 2.1553 <.0001 -10.0403 3.0644 0.0011 

β0 

β1 0.6588 0.1516 <.0001 0.5194 0.2731 0.0572 

β2 0.3689 0.2044 0.0711 0.6448 0.2642 0.0147 

k 0.8566 0.1899 1.5344 0.3539 

Note: The negative sign indicates an increase in crashes. 

Table 13. Rear-End—All Severities. 

Rural Urban 

Parameter 

Estimate Standard 

Error 

Pr > 

ChiSq Estimate Standard Error 

Pr > 

ChiSq 

ln(α) -15.3916 3.1187 <.0001 -16.4839 4.3121 0.0001 

β0 

β1 1.0693 0.2289 <.0001 1.0228 0.3342 0.0022 

β2 0.6968 0.2495 0.0052 0.8386 0.2947 0.0044 

k 1.2372 0.2744 1.7207 0.4234 

Note: The negative sign indicates an increase in crashes. 

25
Table 14. Day—All Severities and Types. 

Rural Urban 

Parameter 

Estimate Standard 

Error 

Pr > 

ChiSq Estimate Standard Error 

Pr > 

ChiSq 

ln(α) -9.6967 1.9103 <.0001 -9.0236 2.9690 0.0024 

β0 

β1 0.7080 0.1413 <.0001 0.5947 0.2581 0.0212 

β2 0.5015 0.1761 0.0044 0.5143 0.2401 0.0322 

k 0.7423 0.1498 1.4078 0.2902 

Note: The negative sign indicates an increase in crashes. 

Table 15. Night—All Severities and Types. 

Rural Urban 

Parameter 

Estimate Standard 

Error 

Pr > 

ChiSq Estimate Standard Error 

Pr > 

ChiSq 

ln(α) -3.8185 1.6073 0.0175 -7.2487 3.1635 0.0219 

β0 0.3381 0.1728 0.0504 0.6526 0.3382 0.0536 

β1 

β2 

k 0.6292 0.1496 1.0298 0.2840 

Note: The negative sign indicates an increase in crashes. 

26
APPENDIX B: CONNECTICUT SPFS 

Table 16. Total—All Severities. 

Rural Urban 

Parameter 

Estimate Standard 

Error 

Pr > 

ChiSq Estimate Standard Error 

Pr > 

ChiSq 

ln(α) -8.9117 2.7596 0.0012 -7.2564 1.1264 <.0001 

β0 1.0156 0.3127 0.0012 

β1 0.6607 0.1069 <.0001 

β2 0.2883 0.0649 <.0001 

k 1.1312 0.2959 1.1736 0.1312 

Note: The negative sign indicates an increase in crashes. 

Table 17. Injury—All Types. 

Rural Urban 

Parameter 

Estimate Standard 

Error 

Pr > 

ChiSq Estimate Standard Error 

Pr > 

ChiSq 

ln(α) -7.8518 3.1177 0.0118 -8.6687 1.1143 <.0001 

β0 0.7967 0.3529 0.0240 

β1 0.6715 0.1096 <.0001 

β2 0.3149 0.0628 <.0001 

k 1.2010 0.3581 0.9519 0.1266 

Note: The negative sign indicates an increase in crashes. 

27
Table 18. Right-Angle—All Severities. 

Urban 

Parameter 

Estimate Standard 

Error 

Pr > 

ChiSq 

ln(α) -7.1373 1.3875 <.0001 

β0 

β1 0.4303 0.1297 0.0009 

β2 0.3670 0.0817 <.0001 

k 1.2848 0.1734 

Note: The negative sign indicates an increase 

in crashes. 

A model for rural night, all severities and types, crashes could not be estimated. A proportion of 

26.7 percent of total crashes was used; that is, a factor of 0.267 to the rural total crash SPF was 

applied. 

Table 19. Rear-End—All Severities. 

Rural Urban 

Parameter 

Estimate Standard 

Error 

Pr > 

ChiSq Estimate Standard Error 

Pr > 

ChiSq 

ln(α) -18.4262 3.7098 <.0001 -12.4637 1.3882 <.0001 

β0 

β1 1.1696 0.3556 0.0010 1.0315 0.1300 <.0001 

β2 0.9914 0.2073 <.0001 0.3829 0.0746 <.0001 

k 0.9103 0.3719 1.4777 0.1906 

Note: The negative sign indicates an increase in crashes. 

28
29

Table 20. Day—All Severities and Types. 

Rural Urban 

Parameter 

Estimate Standard 

Error 

Pr > 

ChiSq Estimate Standard Error 

Pr > 

ChiSq 

ln(α) -10.0048 2.3582 <.0001 -7.8070 1.1413 <.0001 

β0 

β1 0.3965 0.2239 0.0766 0.6752 0.1085 <.0001 

β2 0.8869 0.1495 <.0001 0.3056 0.0653 <.0001 

k 0.6789 0.2349 1.1850 0.1359 

Note: The negative sign indicates an increase in crashes. 

Table 21. Night—All Severities and Types. 

Urban 

Parameter 

Estimate Standard 

Error 

Pr > 

ChiSq 

ln(α) -7.9631 1.1996 <.0001 

β0 

β1 0.6405 0.1155 <.0001 

β2 0.2202 0.0671 0.0010 

k 1.0040 0.1377 

Note: The negative sign indicates an increase 

in crashes. 

A model for rural night, all severities and types, crashes could not be estimated. A proportion of 

24.0 percent of total crashes was used; that is, a factor of 0.267 to the rural total crash SPF was 

applied. 

31

ACKNOWLEDGEMENTS 

This report was prepared by Vanasse Hangen Brustlin, Inc. (VHB) for the FHWA, Office of 

Safety under Contract DTFH61-05-D-00024. The current FHWA COTM for this project is Roya 

Amjadi. Kerry Perrillo Childress served as FHWA COTM from September 2005 until December 

2006. Kimberly Eccles, P.E., of VHB was the study principal investigator. Dr. Bhagwant Persaud 

and Craig Lyon, subcontractors to VHB, conducted the analysis of the strategy and are the 

primary authors of the report. Nancy X. Lefler of VHB led the data collection for the study and is 

a supporting author. Other significant contributions to the study were made by Dr. Hugh McGee, 

Dr. Forrest Council, Michelle Scism, Nitesh Gupta, and Vicki Glenn, all of VHB. 

The project team acknowledges the participation of the following organizations and individuals 

for their assistance in this study: 

• The Connecticut Department of Transportation, particularly Mr. John F. Carey, 

Mr. Al Iallonardo, Mr. Brad Overturf, Mr. Sebastian Puglisi, Ms. Kerry Ross, 

Mr. Gene Interlandi, and Ms. Julie Annino. 

• Connecticut Transportation Institute at the University of Connecticut (UCONN), 

particularly Mr. Thomas Jonsson. 

• The South Carolina Department of Transportation, particularly Ms. Terecia Wilson, 

Ms. Amelia Glisson, and Mr. Bryan Jones. 

33

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