Errata

Date: July 17, 2015

Issuing Office: Federal Highway Administration—Office of Research, Development, 

and Technology: Safety R&D

Address: Turner-Fairbank Highway Research Center, 6300 Georgetown Pike, 

McLean, VA 22101

Name of Document: Evaluation of Pavement Safety Performance

FHWA Publication No.: FHWA-HRT-14-065

The following changes were made to the document after publication on the Federal Highway 

Administration Web site: 

Location Incorrect Values Corrected Values

Page i, TECHNICAL REPORT 

DOCUMENTATION PAGE, Item 15 for 

Supplementary Notes

Contracting Officer’s Technical 

Representative: Roya Amjadi, TurnerFairbank Highway Research Center, 

Office of Safety Research and 

Development, 6300 Georgetown Pike 

McLean VA 22101; E-Mail: 

roya.amjadi@dot.gov; Phone: 202-

493-3383

FHWA Contract Task Managers: Roya 

Amjadi, Office of Safety Research and 

Development, and Jim Sherwood, 

Office of Infrastructure Research and 

Development at Turner-Fairbank 

Highway Research Center, 6300 

Georgetown Pike McLean VA 22101.
Research, Development, and Technology

Turner-Fairbank Highway Research Center

6300 Georgetown Pike

McLean, VA 22101-2296

Evaluation of Pavement

Safety Performance

PUBLICATION NO. FHWA-HRT-14-065 FEBRUARY 2015
FOREWORD

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

Highway Administration (FHWA) Evaluation of Low-Cost Safety Improvements Pooled Fund 

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

on the effectiveness of the safety improvements identified by the National Cooperative Highway 

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

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

studies provide a crash modification factor (CMF) and benefit-cost (BC) economic analysis for 

each of the targeted safety strategies identified as priorities by the pooled fund member states.

The intent of the study was to isolate the effects of various low cost pavement treatments on 

roadway safety. This was a retrospective study for pavement safety performance, looking back at 

crash data both before and after treatments were installed. Both flexible and rigid pavement 

treatments were analyzed, with the majority typically used for pavement preservation or minor 

rehabilitation purposes. Although state highway agencies recognize that most of these treatments 

generally improve pavement friction, they are not typically installed explicitly for safety 

improvement. The one exception is high friction surfacing, which is typically applied as a spot 

safety treatment. Under this effort, CMFs and BC ratios were developed for various low-cost 

pavement treatments.

Monique R. Evans 

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. This report does not constitute a standard, 

specification, or regulation. 

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-14-065

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

4. Title and Subtitle

Evaluation of Pavement Safety Performance

5. Report Date

February 2015

6. Performing Organization Code

7. Author(s)

David K. Merritt, Craig A. Lyon, Bhagwant N. Persaud 

8. Performing Organization Report 

No.

9. Performing Organization Name and Address

The Transtec Group, Inc.

6111 Balcones Dr.

Austin, TX 78731

10. Work Unit No. (TRAIS)

11. Contract or Grant No.

DTFH61-10-D-0027-T-11004

12. Sponsoring Agency Name and Address

U.S. Department of Transportation

Federal Highway Administration

1200 New Jersey Avenue SE, HIPT-20

Washington, DC 20590

13. Type of Report and Period 

Covered

Final Report

September 2011–November 2013

14. Sponsoring Agency Code

HIPT-20

15. Supplementary Notes

FHWA Contract Task Managers: Roya Amjadi, Office of Safety Research and Development, and Jim Sherwood, 

Office of Infrastructure Research and Development at Turner-Fairbank Highway Research Center, 6300 

Georgetown Pike, McLean, VA 22101.* 

16. Abstract

The intent of this study was to isolate the effects of various low-cost pavement treatments on roadway safety. This 

was a retrospective study of pavement safety performance, looking back at crash data before and after treatments 

were installed. Both flexible and rigid pavement treatments were analyzed, with the majority typically used for 

pavement preservation or minor rehabilitation purposes. Although State highway agencies recognize that most of 

these treatments generally improve pavement friction, they are not typically installed explicitly for safety 

improvement, with one exception, high-friction surfacing, which is typically applied as a spot safety treatment. 

The research was conducted as part of Phase VI of the Federal Highway Administration Evaluation of Low-Cost 

Safety Improvements Pooled Fund Study (ELCSI–PFS). This pooled fund study (PFS) was established to conduct 

research on the effectiveness of the safety improvements identified by the National Cooperative Highway 

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

and Transportation Officials Strategic Highway Safety Plan. The intent of the work conducted under the various 

phases of the ELCSI-PFS is to provide a crash modification factor (CMF) and benefit-cost (BC) economic 

analysis for each of the targeted safety strategies identified as priorities by the PFS States. Under the effort 

described herein, CMFs and BC ratios were developed for various low-cost pavement treatments.

17. Key Words

pavement safety, low-cost treatment, pavement friction, 

pavement texture, pavement treatment

18. Distribution Statement

No restrictions. This document is available to the 

public through the National Technical Information 

Service, Springfield, VA 22161.

http://www.ntis.gov

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this page)

Unclassified

21. No. of Pages

154

22. Price

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

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

CHAPTER 1. INTRODUCTION .................................................................................... 5

IMPORTANCE OF PAVEMENT SAFETY ............................................................. 5

PAVEMENT SURFACE CHARACTERISTICS ...................................................... 5

Friction 5

Pavement Texture ....................................................................................................... 7

Durability .................................................................................................................... 9

Drainage .................................................................................................................... 10

BACKGROUND OF ELCSI PFS ............................................................................. 10

PHASE VI STUDY ..................................................................................................... 11

Scope of Phase VI Study ........................................................................................... 11

Products and Desired Outcomes ............................................................................... 12

CHAPTER 2. LITERATURE SEARCH ...................................................................... 13

SKID-RESISTANT PAVEMENTS........................................................................... 13

PROVIDE GROOVED PAVEMENT ...................................................................... 15

FURTHER LITERATURE REVIEW ...................................................................... 18

CHAPTER 3. PAVEMENT TREATMENT TYPES .................................................. 25

PHASE VI TREATMENTS CONSIDERED ........................................................... 25

Thin Hot-Mix Asphalt Overlay ................................................................................. 25

Open Graded Friction Course ................................................................................... 26

Ultra-Thin Bonded Wearing Course ......................................................................... 27

Chip Seal (Seal Coat) ................................................................................................ 28

Slurry Seal ................................................................................................................. 30

Microsurfacing .......................................................................................................... 30

Diamond Grinding .................................................................................................... 31

Grooving ................................................................................................................... 32

Micro-Milling ........................................................................................................... 33

Shotblasting/Abrading .............................................................................................. 34

Cape Seal .................................................................................................................. 35

Scrub Seal ................................................................................................................. 36

High Friction Surfacing ............................................................................................ 36

COMPARISON OF TREATMENT MACROTEXTURE PROPERTIES ........... 37

CHAPTER 4. DATA COLLECTION........................................................................... 39

OVERVIEW OF DATA COLLECTION PROCESS ............................................. 39

Contact ELCSI-PFS Volunteer States to Identify Treatment Types and 

Availability of Crash and Roadway Data ................................................................. 39

Request Treatment Locations and Crash and Roadway Data ................................... 39

Narrow List of Treatment Types and States Based on Sample Size ......................... 39

Verify Treatment Locations, Installation Date, and Underlying Surface ................. 39

Identify Reference Sites and Collect Crash Data ...................................................... 40

Collect additional Data Collection: Climatic Data, Cost Information, and 

Materials Information ............................................................................................... 40

DATA COLLECTION ITEMS ................................................................................. 40

iii
SUMMARY OF DATA BY STATE .......................................................................... 41

FINAL TREATMENTS AND STATES ................................................................... 43

CONVENTIONAL TREATMENT DATA COLLECTION .................................. 44

Pennsylvania ............................................................................................................. 44

North Carolina .......................................................................................................... 53

California .................................................................................................................. 62

Minnesota .................................................................................................................. 72

CHAPTER 5. ANALYSIS .............................................................................................. 79

ANALYSIS OBJECTIVES ........................................................................................ 79

ANALYSIS METHODOLOGY ................................................................................ 80

DEVELOPMENT OF SAFETY PERFORMANCE FUNCTIONS ....................... 81

SPECIFICS OF THE EMPIRICAL BAYES BEFORE-AFTER 

EVALUATION ........................................................................................................... 81

Overall Safety Effects ............................................................................................... 81

Effects on Different Severity and Impact Types ....................................................... 82

Effects of Design, Traffic, Operational, and Safety Characteristics ......................... 83

Differential Effects Over Time ................................................................................. 83

SAFETY PERFORMANCE FUNCTIONS .............................................................. 83

Pennsylvania ............................................................................................................. 83

North Carolina .......................................................................................................... 84

California .................................................................................................................. 86

Minnesota .................................................................................................................. 89

USE OF CLIMATE DATA ........................................................................................ 91

Objective ................................................................................................................... 91

Source of Data ........................................................................................................... 91

Methodology and Results ......................................................................................... 92

Conclusions on the Use of Climate Data .................................................................. 92

CHAPTER 6. BEFORE–AFTER ANALYSIS ............................................................. 95

AGGREGATE RESULTS ......................................................................................... 95

Chip Seal Results ...................................................................................................... 95

Diamond Grinding Results ....................................................................................... 96

Thin HMA Results .................................................................................................... 96

OGFC Results ........................................................................................................... 97

Grooving Results ...................................................................................................... 98

Microsurfacing Results ............................................................................................. 98

Slurry Seal Results .................................................................................................... 99

UTBWC Results ..................................................................................................... 100

Summary of Aggregate Results .............................................................................. 101

Disaggregate Results ............................................................................................... 101

Effect of Other Factors ............................................................................................ 103

iv
CHAPTER 7. HIGH FRICTION SURFACING TREATMENT............................. 105

INTRODUCTION .................................................................................................... 105

DATA COLLECTION ............................................................................................. 105

SUMMARY OF HFS TREATMENT DATA COLLECTION ............................. 106

Colorado .................................................................................................................. 106

Kansas ................................................................................................................... 106

Kentucky ................................................................................................................. 106

Michigan ................................................................................................................. 107

Montana .................................................................................................................. 107

South Carolina ........................................................................................................ 107

Tennessee ................................................................................................................ 107

Wisconsin ................................................................................................................ 108

Other States ............................................................................................................. 108

SUMMARY OF HFS TREATMENT SITES ......................................................... 109

ANALYSIS OBJECTIVES ...................................................................................... 110

ANALYSIS METHODOLOGY .............................................................................. 110

Naïve Study Approach ............................................................................................ 110

C-G Study Approach ............................................................................................... 111

RESULTS .................................................................................................................. 112

CONCLUSIONS FOR HFS TREATMENT .......................................................... 113

CHAPTER 8. BC ANALYSIS ..................................................................................... 115

METHODOLOGY ................................................................................................... 115

CONVENTIONAL TREATMENTS ...................................................................... 116

HFS TREATMENT .................................................................................................. 117

CHAPTER 9. DISCUSSION AND CONCLUSIONS ............................................... 119

SUMMARY ............................................................................................................... 119

RELATING RESULTS TO PAVEMENT FACTORS ......................................... 120

Chip Seals ............................................................................................................... 121

Diamond Grinding .................................................................................................. 121

Thin HMA Overlay ................................................................................................. 121

OGFC ................................................................................................................... 122

Microsurfacing ........................................................................................................ 122

Slurry Seal ............................................................................................................... 123

UTBWC .................................................................................................................. 123

HFS ................................................................................................................... 123

Age of Treatment—Chip Seal................................................................................. 123

Age of Treatment—OGFC ..................................................................................... 124

ACKNOWLEDGMENTS ............................................................................................ 125

APPENDIX A—INVESTIGATION OF THE DEVELOPMENT OF 

CMFUNCTIONS .......................................................................................................... 127

OBJECTIVES ........................................................................................................... 127

METHODOLOGY ................................................................................................... 127

v 
RESULTS .................................................................................................................. 129

Thin HMA—Freeway—Total Crashes ................................................................... 129

OGFC—Two-Lane—Total Crashes ....................................................................... 129

Diamond Grinding—Freeway—Total Crashes ...................................................... 130

APPENDIX B—LIST OF HFS TREATMENT SITES PROVIDED BY STATE .. 131

REFERENCES .............................................................................................................. 139

vi 
LIST OF FIGURES

Figure 1. Illustration. Key mechanisms of pavement–tire friction ................................................. 6

Figure 2. Graph. Pavement texture categories and their effect on surface characteristics (adapted 

from Henry) .............................................................................................................................. 7

Figure 3. Photo. Microtexture provided by aggregates on an asphalt pavement ............................ 8

Figure 4. Photo. Macrotexture for an asphalt pavement surface (left) and concrete surface from 

tining (right). ............................................................................................................................. 9

Figure 5. Photo. Placement of thin asphalt overlay (top) and surface texture of new 

(bottom left) and worn (bottom right) dense graded asphalt surfaces. ................................... 26

Figure 6. Photo. Surface texture of OGFC ................................................................................... 27

Figure 7. Placement of an UTBWC using a self-priming paver (top) and surface texture of an 

UTBWC (bottom) ................................................................................................................... 28

Figure 8. Photo. Placement of a chip seal (top) and surface texture of a single layer chip seal 

(bottom)................................................................................................................................... 29

Figure 9. Photo. Placement of a slurry seal (top) and surface texture of a cured slurry seal 

(bottom)................................................................................................................................... 30

Figure 10. Microsurfacing placement (top) and surface texture of cured microsurfacing 

treatment (bottom) .................................................................................................................. 31

Figure 11. Photo. Diamond ground concrete pavement surface. .................................................. 32

Figure 12. Photo. Grooved concrete pavement surface (top) and Next Generation Concrete 

Surface (bottom) ..................................................................................................................... 33

Figure 13. Photo. Micro-milled asphalt pavement surface ........................................................... 34

Figure 14. Photo. Shotblast asphalt pavement (top) and concrete pavement (bottom) ................ 35

Figure 15. Photo. Emulsion material is scrubbed into the pavement surface for a scrub seal 

surface treatment ..................................................................................................................... 36

Figure 16. Photo. Installation of HFS (top) and finished surfaces (bottom)................................. 37

Figure 17. Equation. Change in safety for a given crash type at a treated site. ............................ 81

Figure 18. Equation. Estimate of the expected number of crashes before treatment.................... 82

Figure 19. Equation. Estimate of weight ...................................................................................... 82

Figure 20. Equation. Estimate of the index of safety effectiveness .............................................. 82

Figure 21. Equation. Standard deviation of the estimated index of safety effectiveness ............. 82

Figure 22. Equation. Model form for Pennsylvania SPFs ............................................................ 83

Figure 23. Equation. Model form for North Carolina SPFs ......................................................... 84

Figure 24. Equation. Model form for California SPFs ................................................................. 86

Figure 25. Equation. Model form for Minnesota SPFs ................................................................. 89

Figure 26. Photo. Example of wear in wheelpaths over time for chip seal treatments, 

reducing surface texture and friction .................................................................................... 102

Figure 27. Equation. Estimated number of crashes that would have occurred in the after 

period with no treatment in the naïve study .......................................................................... 110

Figure 28. Equation. Estimated variance of B in the naïve study ............................................... 111

Figure 29. Equation. Estimated CMF in the naïve study. ........................................................... 111

Figure 30. Equation. Estimated CMF variance in the naïve study ............................................. 111

Figure 31. Equation. Estimated number of crashes that would have occurred in the after 

period with no treatment in the C-G study............................................................................ 112

Figure 32. Equation. Estimated variance of B in the C-G study................................................. 112

Figure 33. Equation. Estimated CMF in the C-G study .............................................................. 112

vii
Figure 34. Equation. Estimated CMF variance in the C-G study. .............................................. 112

Figure 35. Equation. Annualized cost pavement treatment. ....................................................... 115

Figure 36. Equation. Model estimated for total crashes on thin HMA treatments on 

freeways. ............................................................................................................................... 129

Figure 37. Equation. Model estimated for total crashes on OGFC treatments on two-lane 

roads. ..................................................................................................................................... 129

Figure 38. Equation. Model estimated for total crashes on diamond grinding treatments on 

freeways ................................................................................................................................ 130

viii
LIST OF TABLES

Table 1. Summary of results from NYSDOT skid-accident reduction program analysis ............ 19

Table 2. Flexible and concrete pavement treatment strategies considered in Phase VI ............... 25

Table 3. Typical macrotexture depth for various pavement treatments ........................................ 38

Table 4. List of potential data collected for treatment sites. ......................................................... 41

Table 5. Treatment strategies and quantities provided, by State, for asphalt pavements ............. 42

Table 6. Treatment strategies and quantities provided, by state, for concrete pavements ............ 43

Table 7. Summary statistics for Pennsylvania treatment site geometry ....................................... 47

Table 8. Summary statistics for Pennsylvania treatment site AADT and crashes ........................ 49

Table 9. Summary statistics for Pennsylvania reference sites. ..................................................... 52

Table 10. Summary statistics for North Carolina treatment site geometry ................................... 56

Table 11. Summary statistics for North Carolina treatment site AADT and crashes. .................. 58

Table 12. Summary statistics for North Carolina reference sites ................................................. 61

Table 13. Summary statistics for California treatment site geometry .......................................... 64

Table 14. Summary statistics for California treatment site AADT and crashes. .......................... 67

Table 15. Summary statistics for California reference sites ......................................................... 71

Table 16. Summary statistics for Minnesota treatment site geometry .......................................... 74

Table 17. Summary statistics for Minnesota treatment site AADT and crashes .......................... 76

Table 18. Summary statistics for Minnesota reference sites......................................................... 78

Table 19. SPF parameter estimates and standard errors for Pennsylvania treatment sites. .......... 84

Table 20. SPF parameter estimates and standard errors for North Carolina treatment sites ........ 85

Table 21. SPF parameter estimates and standard errors for California treatment sites ................ 87

Table 22. SPF parameter estimates and standard errors for Minnesota treatment sites ............... 90

Table 23. Summary of pilot test results of including climatic data .............................................. 92

Table 24. Estimates of CMFs for chip seal treatment ................................................................... 95

Table 25. Estimates of CMFs for diamond grinding treatment .................................................... 96

Table 26. Estimates of CMFs for thin HMA treatment. ............................................................... 97

Table 27. Estimates of CMFs for open OGFC treatment ............................................................. 98

Table 28. Estimates of CMFs for grooving treatment .................................................................. 98

Table 29. Estimates of CMFs for microsurfacing treatment......................................................... 99

Table 30. Estimates of CMFs for slurry seal treatment .............................................................. 100

Table 31. Estimates of CMFs for UTBWC treatment ................................................................ 100

Table 32. Estimates of CMFs for chip seal treatment for wet-road crashes on two-lane roads 

by period after treatment. ...................................................................................................... 102

Table 33. Estimates of CMFs for single and multi-layer chip seal treatment for wet-road 

crashes (NC and PA only) by period after treatment. ........................................................... 103

Table 34. Estimates of CMFs for diamond grinding treatment for wet-road crashes on 

freeways by period after treatment ....................................................................................... 103

Table 35. Estimates of CMFs for OGFC treatment for wet-road crashes on freeways and 

two-lane roads by period after treatment .............................................................................. 103

Table 36. Summary statistics of HFS treatment site data collected ............................................ 109

Table 37. Summary statistics of HFS comparison site data collected. ....................................... 109

Table 38. Results for the naïve before–after study based on all sites. ........................................ 113

Table 39. Results for the before–after C-G study for treatment sites for which comparison 

sites were available ............................................................................................................... 113

ix
Table 40. Results of BC analysis for conventional treatment groups with statistically 

significant crash reductions................................................................................................... 116

Table 41. Results for model for total crashes on thin HMA treatments on freeways ................. 129

Table 42. Results for model for total crashes on OGFC treatments on two-lane roads ............. 130

Table 43. Results for model for total crashes on diamond grinding treatments on freeways. .... 130

Table 44. Treatment sites by State, part 1 ................................................................................... 131

Table 45. Treatment site by State, part 2. ................................................................................... 135

x 
LIST OF ABBREVIATIONS 

AADT Average Annual Daily Traffic

AASHTO American Association of State Highway and Transportation

BC Benefit-Cost 

Caltrans California Department of Transportation 

CDOT Colorado Department of Transportation 

C-G Comparison Group (study) 

CMF Crash Modification Factor

CMFunction Crash Modification Function 

CPI Consumer Price Index 

EB Empirical Bayes

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

FHWA Federal Highway Administration

GLM Generalized Linear Modeling

GP Groove Pavement 

HFS High-Friction Surfacing

HIS Highway Information System

HMA Hot Mix Asphalt 

HSIS Highway Safety Information System

KDOT Kansas Department of Transportation 

KTC Kentucky Transportation Cabinet 

MDOT Michigan Department of Transportation

MDT Montana Department of Transportation 

MnDOT Minnesota Department of Transportation 

NCDC National Climatic Data Center

NCDOT North Carolina Department of Transportation 

NCHRP National Cooperative Highway Research Program

NYSDOT New York State Department of Transportation 

OGAC Open Graded Asphalt Concrete 

OGFC Open-Graded Friction Course 

PennDOT Pennsylvania Department of Transportation 

R-OGAC Rubberized Open Graded Asphalt Concrete 

ROR Run-Off Road

RTM Regression to the Mean

SCDOT South Carolina Department of Transportation 

SCRIM Sideway-Force Coefficient Routine Investigation Machine 

SEAHC Surface Enhancements at Horizontal Curves

SHRP2 Strategic Highway Research Program 2

SPF Safety Performance Function

TDOT Tennessee Department of Transportation 

TRIMS Tennessee Information Management System

UTBWC Ultra-Thin Bonded Wearing Course 

WisDOT Wisconsin Department of Transportation 

xi

EXECUTIVE SUMMARY

The intent of this study was to isolate the effects of various low-cost pavement treatments on 

roadway safety. This was a retrospective study of pavement safety performance, looking back at 

crash data before and after treatments were installed. Both flexible and rigid pavement treatments 

were analyzed, with the majority typically used for pavement preservation or minor 

rehabilitation purposes. Although State highway agencies recognize that most of these treatments 

generally improve pavement friction, they are not typically installed explicitly for safety 

improvement, with one exception, high-friction surfacing (HFS), which is typically applied as a 

spot safety treatment. 

The research was conducted as part of Phase VI of the Federal Highway Administration 

(FHWA) Evaluation of Low-Cost Safety Improvements Pooled Fund Study (ELCSI–PFS). This 

PFS was established to conduct research on the effectiveness of the safety improvements 

identified by the National Cooperative Highway Research Program (NCHRP) Report 500 guides 

as part of implementation of the American Association of State Highway and Transportation 

Officials (AASHTO) Strategic Highway Safety Plan. The intent of the work conducted under the 

various phases of the ELCSI-PFS is to provide a crash modification factor (CMF) and benefitcost (BC) economic analysis for each of the targeted safety strategies identified as priorities by 

the PFS States. 

With respect to pavement surfaces, NCHRP Report 500, Volume 6, which addresses reducing 

run-off-road (ROR) crashes, presents Strategy 15.1 A7, “Skid-Resistant Pavements,” as a key to 

reducing ROR crashes. Volume 7, which addresses reducing collisions on horizontal curves, 

likewise discusses Strategy 15.2 A7, “Provide Skid-Resistant Pavement Surfaces,” as a key 

strategy for reducing crashes at horizontal curves. The report recognizes that there had been only 

limited research conducted on site-specific treatments as of 2003. However, given the results of 

other research on general effectiveness of decreased skidding, the report places this strategy in 

the “proven” category. The report also recognizes that the effectiveness of friction-enhancing

treatments will diminish over time; therefore, States using this strategy must conduct a dynamic 

program to target the appropriate sites for new treatment and to maintain the safety benefit from 

existing treatments.

In a similar manner, the FHWA Low-Cost Treatments for Horizontal Curve Safety guide 

recommends “skid-resistive pavement surface treatments” as a low-cost treatment for reducing 

crashes at horizontal curves. This guide specifically mentions remedial treatments such as hotmix asphalt (HMA) overlays, surface treatments, grinding, and grooving of pavement surfaces 

for both concrete and asphalt pavements where friction demand is higher. 

A further literature review revealed important insights to consider, including the following:

• Confounding factors that influence collision risk and may interact with the safety 

effects of skid resistance include location type (segment, intersection approach, curve, 

etc.), area type, speed limit, traffic volume, rainfall propensity, roadway geometry, 

temperature, and pavement structure. 

1 
• Expected collision reductions from friction improvements depend on both the level of 

friction prior to and after treatment. Reductions in wet-road crashes up to 

approximately 75 percent may be expected.

• Friction of various low-cost pavement treatments tends to decrease with time, and 

therefore the safety benefit may also be expected to decrease with time.

• Evaluations of improved skid resistance have typically not applied statistically 

rigorous before–after study designs.

The ultimate outcome of this effort to build on knowledge from previous work, while 

overcoming the shortcomings of those studies, is to gain a better understanding of the effects that 

various common, low-cost pavement treatments have on roadway safety. Two of the more 

tangible ways for quantifying this is through CMF and BC ratios for each treatment type. These 

products will potentially help State transportation departments in the decisionmaking process for 

selection of a pavement treatment for a particular project. 

In achieving these outcomes, the state-of-the-art empirical Bayes (EB) before–after methodology 

was applied to evaluate the effects on various crash types—total, injury, wet road, dry road, wetroad ROR, and all ROR—of the following treatments, based on data from California, North 

Carolina, Pennsylvania, and Minnesota. 

• Chip Seal (single and double layer). 

• Diamond Grinding (concrete pavement only). 

• Grooved Concrete Pavement. 

• Microsurfacing (asphalt and concrete pavement). 

• Open-Graded Friction Course (OGFC) (asphalt and concrete pavement). 

• Slurry Seal (asphalt pavement). 

• Thin HMA (asphalt and concrete pavement). 

• Ultra-Thin Bonded Wearing Course (UTBWC) (asphalt and concrete pavement). 

In addition, a simple before–after evaluation was completed for HFS treatments based on limited 

data from several States, including Colorado, Kansas, Kentucky, Michigan, Montana, South 

Carolina, Tennessee, and Wisconsin. HFS was analyzed separately because it is typically used 

specifically for safety improvement (through friction enhancement) and not pavement 

preservation, as with other treatments. Although the HFS treatment data were insufficient to 

apply the EB method, it still revealed tremendous crash reduction potential for this treatment. 

The combined results for all treatment types subjected to the rigorous EB evaluation (except 

grooving, for which there were very few sites) suggest that the treatments resulted in benefits for 

wet-road crashes, with the exception of thin HMA for two-lane roads (for both California and 

North Carolina, the two States with large enough samples for a definitive result), and for OGFC 

for two-lane and multilane roads, for which the effect was negligible. 

For dry-road crashes, crashes increased for microsurfacing on two-lane roads (except for North 

Carolina), thin HMA and OGFC on two-lane roads, and OGFC and chip seal on multilane roads. 

2 
There were indications of a benefit for UTBWC, chip seal, and slurry seal on two-lane roads, and 

diamond grinding on freeways. 

The CMFs for treatments by road and crash type may be considered for use in the Highway 

Safety Manual and the CMF Clearing House. 

A thorough disaggregate analysis of the before–after evaluation data was undertaken in which 

regression analysis was used to investigate the effects on the CMFs of a number of variables, 

including traffic, precipitation, expected crash frequency before treatment, and environment 

(urban/rural). In the end, the crash modification functions (CMFunctions) developed were not 

robust enough to recommend them. Nevertheless, there were useful insights that suggest that it 

would be worthwhile to pursue the development of robust CMFunctions in future research. The 

results did suggest that there is a relationship between CMFs and average annual daily traffic 

(AADT) and sometimes precipitation, urban versus rural setting, and expected crash frequency. 

However, the direction of the effect is not always consistent, varying by crash type, site type, and 

treatment. Future research is needed to reconcile (i.e., explain) these apparent inconsistencies. 

An economic analysis was conducted for treatments and States for which the sample size was 

large enough and for which there was a statistically significant (5-percent level) benefit for total 

crashes based on the EB evaluation. The results indicate that BC ratios larger than 2.0, 

considering impacts on safety only, are attainable for the following: 

• Chip seal on two-lane roads (California only). 

• Diamond grinding on freeways. 

• Thin HMA on multilane roads (North Carolina only)

• OGFC on freeways. 

• Slurry seal on two-lane roads. 

• OTBWC on two-lane roads. 

For other treatments/road classes/States, sample sizes were too small in some cases and, in other 

cases, overall safety benefits were not achieved or were statistically insignificant. 

For HFS treatments, the results of the cursory before–after analysis suggest that HFS can be a 

highly safety- and cost-effective treatment for which implementation should continue. It is

strongly recommended that additional data be collected to conduct a robust EB before study to 

derive a CMF that could be recommended to practitioners and for which a BC ratio could be 

confidently estimated. 

3 

CHAPTER 1. INTRODUCTION

IMPORTANCE OF PAVEMENT SAFETY

Roadway safety is a complicated issue to say the least. Any time human behavior is a factor, 

predicting how drivers will respond to road conditions is difficult at best. Variables such as 

weather, roadway geometry, visibility issues, pavement surface conditions, and the like, further 

complicate the ability to quantify the safety of a particular roadway. 

One factor that is fairly well understood is the link between pavement friction and safety, or 

more specifically, the probability of wet-weather skidding crashes. The probability of wetskidding crashes is reduced when friction between a vehicle tire and pavement is high. The 

FHWA and National Transportation Safety Board estimate that up to 70 percent of wetpavement crashes can be prevented or minimized (in terms of damage) by increasing pavement 

friction.

(1) While we cannot control human response to road conditions, we can control the 

properties of pavement surfaces to help reduce the probability of skid-related crashes.

Pavement surfaces affect several factors related to roadway safety. First, the frictional properties 

of pavement surfaces affect the resistance to tires sliding across the pavement surface. Pavement 

friction helps to keep vehicles on the road when brakes are applied, particularly when the wheels 

lock up, and when navigating curves or steering aggressively. This is particularly important in 

wet weather when a thin film of water on the surface of the pavement reduces contact between 

the tire and pavement surface. Another important factor is the ability of the pavement surface to 

channel water out from beneath the tire. The texture and porosity of a pavement surface help to 

provide a path to channel water away from beneath the tire to reduce the potential for 

hydroplaning. Texture and porosity also affect the splash and spray potential of a roadway in wet 

conditions, which can significantly impact visibility in wet weather. 

PAVEMENT SURFACE CHARACTERISTICS

Pavement surface characteristics are primarily functional parameters that affect the safety and 

comfort of the road user. The most commonly studied surface characteristics are friction, 

smoothness, tire–pavement noise, and texture. Briefly summarized below are two key 

characteristics as they relate to roadway safety: friction and texture.

Friction

In short, pavement friction is the force that resists the relative motion between a vehicle tire and 

pavement surface. This force is generated when a tire rolls or slides over the pavement surface 

and is measured as the nondimensional coefficient of friction.(2) Although a number of factors 

affect the actual frictional resistance in a given situation, in general, the higher the coefficient of 

friction of a pavement surface itself, the lower the probability that a tire will slide across the 

surface in a fully locked braking condition. 

The two key mechanisms involved in tire-pavement friction are adhesion and hysteresis, as 

illustrated in figure 1. Adhesion is the friction that results from the small-scale 

bonding/interlocking of the tire rubber and pavement surface. Hysteresis is the frictional force 

that results from energy loss during deformation (or enveloping around the pavement texture) as 

5 
the tire moves across the surface.

(2) In general, for wet conditions, the adhesion component 

decreases with speed while the hysteresis component increases. Both mechanisms are directly 

affected by pavement texture, as discussed below. 

Source: National Cooperative Highway Research Program

Figure 1. Illustration. Key mechanisms of pavement–tire friction.

(2) 

As documented in the AASHTO Guide for Pavement Friction, although a basic relationship 

exists between pavement friction and wet-crash rates, no specific threshold values have been 

established for pavement friction that make a pavement more or less safe.(3) Pavement friction 

demand, which is specific to the characteristics of a particular roadway, must be considered 

when establishing any sort of threshold. Pavement friction demand is dictated by site conditions 

(grade, superelevation, radius of curvature, terrain, climatic conditions, etc.), traffic 

characteristics (volume and mix of vehicle types), and driver behavior (prevailing speed, 

response to conditions, etc.). These conditions are continually changing over time and are 

different for every roadway, making it difficult to establish a “one size fits all” friction threshold. 

Although one could err on the side of providing a level of friction that is exceptional and 

expected to be above friction demand for the vast majority of situations, there are other 

considerations and potential costs associated with this approach. The cost to construct these types 

of treatments could potentially be much higher than for a conventional surface treatment if 

nonconventional and/or nonlocally available materials are required. There are also the costs to 

the users, such as increased rolling resistance and therefore decreased fuel economy, and 

increased tire wear. 

6 
Pavement Texture

Pavement surface texture directly affects pavement friction as well as other factors related to 

roadway safety. Texture affects not only the coefficient of friction of a pavement surface, but 

also the ability of the pavement to shed or channel water away from beneath the tire. Pavement 

texture is typically broken up into four different types, as illustrated in figure 2. Of primary 

concern for pavement safety are microtexture and macrotexture.

Source: The Transtec Group, Inc.

Figure 2. Graph. Pavement texture categories and their effect on surface characteristics

(adapted from Henry).

(4) 

Microtexture—Microtexture is the fine-scale roughness that is not necessarily visible to the 

naked eye, but is apparent to the touch (see figure 3). It provides a degree of “sharpness” 

necessary for the tire to break through any residual water film that remains after the bulk water 

has run off and interacts directly with the tire rubber on a molecular scale to provide adhesion.(3)

Microtexture is affected primarily by the surface properties of the aggregate particles that make 

up the pavement surface, and primarily affects the frictional properties of a pavement surface at 

lower speeds. 

For asphalt pavements and asphalt surface treatments, the coarse aggregate generally provides 

microtexture. For concrete pavements, the mortar (fine aggregate and cement paste) provides 

microtexture until the coarse aggregate is exposed (e.g., through diamond grinding or polishing), 

7 
at which point the coarse aggregate also contributes to microtexture. For this reason, there are 

generally very restrictive requirements on the coarse aggregates for asphalt pavements and fine 

aggregates for concrete pavements. 

Source: The Transtec Group, Inc.

Figure 3. Photo. Microtexture provided by aggregates on an asphalt pavement. 

Macrotexture—Macrotexture is a larger-scale, visible roughness component of pavement texture 

formed by the size and shape of the aggregate particles themselves, the porosity of the pavement 

surface, or from texture imparted to the pavement surface from grooving, tining, etc. (figure 4).

The primary function of macrotexture is to provide a path for bulk water drainage from beneath 

the tire so that the adhesive component of friction provided by microtexture is reestablished.(3)

However, macrotexture also affects the hysteresis component of friction because macrotexture 

causes the deformation of the tire rubber. As such, macrotexture has a significant effect on 

friction at higher speeds. 

8 
Source: The Transtec Group, Inc.

Figure 4. Photo. Macrotexture for an asphalt pavement surface (left) and concrete surface 

from tining (right). 

For asphalt pavements and surface treatments, macrotexture is primarily controlled by aggregate 

properties (size, shape, gradation) as well as the porosity of the finished surface. For concrete 

pavements, macrotexture is primarily controlled by the finish or texture imparted to the surface 

(carpet/turf drag, tining, diamond grinding, grooving, etc.). 

Pavement texture is typically quantified in terms of macrotexture depth, reported as either mean 

texture depth, measured using volumetric techniques such as ASTM E 965, or mean profile 

depth, measured using laser-based devices and quantified according to ASTM E 1845.(5,6)

Chapter 2 discusses some previous studies that have examined the relationship between 

macrotexture depth and crash rates, and chapter 3 presents some typical macrotexture depths for 

the various treatments considered under this effort. 

Durability

As discussed above, properties of pavement surface treatments have a direct impact on texture, 

friction, and ultimately safety. As such, selection of the materials used in a pavement or surface 

treatment is a critical aspect of pavement design and treatment selection. Constituent materials 

and mixture designs must provide the necessary microtexture and macrotexture components to 

ensure good friction. 

Materials must also be durable, however, if pavement friction is to be sustained over time. 

Pavements, by their very nature, are completely exposed to weather and are subjected to 

potentially millions of wheel passes over their lifespan. The repeated application of wheel loads 

tends to wear down or wear away paving materials. Weather, likewise, can slowly wear away 

pavement surfaces through oxidation, erosion, or freeze–thaw related deterioration. There must 

be a balance between providing the necessary friction characteristics and providing a durable, 

long-lasting surface, while not ignoring the importance of cost. 

9 
Drainage 

Although not explicitly considered a pavement surface characteristic, drainage is a critical factor 

in pavement safety. Even the most aggressive (high-friction) pavement surface can be rendered 

ineffective if water does not drain from the pavement surface. While the porosity of a surface or 

drainage “channels” (e.g., tining or grooving) imparted to the pavement surface can aid with 

channeling water from beneath a tire, pavement cross-slope is a key component of drainage and 

may need to be addressed first for any low-cost surface treatment to be effective. (Note: Because 

of a lack of cross-slope information on the pavement surfaces analyzed in this study, the effect of 

inadequate cross-slope could not be accounted for.) 

BACKGROUND OF ELCSI PFS 

In 1997, the AASHTO Standing Committee on Highway Traffic Safety, with the assistance of 

the FHWA, the National Highway Traffic Safety Administration, and the Transportation 

Research Board 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.

The NCHRP published a series of guides to advance the implementation of countermeasures 

targeted to reduce crashes and injuries. Each guide addresses 1 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. 

FHWA organized the ELCSI–PFS, consisting of 38 volunteer States, to evaluate low-cost safety 

strategies identified by the NCHRP Report 500 guides under this strategic highway safety effort.

The intent of the work conducted under the various phases of the ELCSI-PFS is to provide a 

CMF and BC economic analysis for each of the targeted safety strategies identified as priorities 

by the PFS States. 

NCHRP Report 500 Volumes 6 and 7 address pavement surfaces. Volume 6, which addresses 

reducing ROR crashes, presents Strategy 15.1 A7 “Skid-Resistant Pavements” as a key to 

reducing ROR crashes.(7) Volume 7, which addresses reducing collisions on horizontal curves, 

likewise discusses Strategy 15.2 A7 “Provide Skid-Resistant Pavement Surfaces” as a key 

strategy for reducing crashes at horizontal curves.(8) The report recognizes that there had been 

only limited research on site-specific treatments as of 2003. However, given the results of other 

research on general effectiveness of decreased skidding, the report places this strategy in the 

“proven” category. The report also recognizes that the effectiveness of high-friction treatments 

will diminish over time; therefore, States using this strategy must conduct a dynamic program to 

target the appropriate sites for new treatment and to maintain the safety benefit from existing 

treatments.

In a similar manner, the FHWA Low-Cost Treatments for Horizontal Curve Safety guide 

recommends “skid-resistive pavement surface treatments” as a low-cost treatment for reducing 

crashes at horizontal curves.(9) This guide specifically discusses remedial treatments such as 

10 
asphalt overlays, surface treatments, grinding, and grooving of pavement surfaces for both 

concrete and asphalt pavements where friction demand is higher. 

Shrinking State highway agency budgets for construction and rehabilitation is one reason for an 

emphasis on low-cost treatments. Another reason is that although rural roads account for the 

majority of highway crashes when quantified in terms of vehicle mi traveled, rural roads are 

often lower priority when it comes to funding rehabilitation or improvements. This was verified 

by a Government Accountability Office study that found that the large number of rural roads 

carry relatively low volumes of traffic, often making it difficult to justify the costs of 

improvements to these roads. Many rural roads also fall under the jurisdiction of local 

government entities, which do not have the resources to undertake significant projects to increase 

rural road safety.(10) 

PHASE VI STUDY

The goal of the Phase VI study was to isolate the effects of various low-cost pavement treatments 

on roadway safety, since the pavement itself plays a major role in highway safety. This unique 

study sought to identify any potential differences in safety performance for various types of 

pavement treatments because this has not been carefully examined in previous research. 

Scope of Phase VI Study

The Phase VI study was a retrospective study of pavement safety performance, looking back at 

crash data before and after treatments were installed. No test sections were constructed explicitly 

for this study. Crash data were analyzed to evaluate the effectiveness of low-cost pavement 

improvement strategies using the EB before–after study methodology for the most part. Both 

flexible and rigid pavement treatment were analyzed, and CMFs for each type of pavement 

improvement were developed. BC ratios were developed for those applications for which there 

were statistically significant overall crash reduction benefits.

Although the definition of a low-cost pavement treatment is not clear in terms of a ceiling on the 

cost per lane-mi or cost per square yd, in general, these are treatments applied to existing 

pavement surfaces without substantially changing the pavement structure. These are treatments 

that will generally change the pavement surface characteristics, but do not necessarily add 

structural capacity to the pavement. Therefore, full-depth pavement reconstruction and projects 

that serve to realign or substantially alter the pavement cross-section (e.g., superelevation) were 

excluded from consideration. 

The majority of the treatments considered in this effort are typically used for pavement 

preservation or minor rehabilitation purposes. Although State highway agencies recognize that 

most of these treatments generally improve pavement friction, they are not typically installed 

explicitly for safety improvement, with certain exceptions that are discussed below. These are 

also primarily treatments that are used for long stretches of pavement preservation/rehabilitation, 

with the exception of HFS, which is typically used solely for spot safety treatments.

11 
Products and Desired Outcomes

The ultimate outcome of the Phase VI effort is a better understanding of the effects that various 

common, low-cost pavement treatments have on roadway safety. Two of the more tangible ways 

this is quantified is through CMFs and BC ratios for each treatment type. These products will 

potentially help State transportation departments in the decisionmaking process for selection of a 

pavement treatment for a particular project.

12 
CHAPTER 2. LITERATURE SEARCH

SKID-RESISTANT PAVEMENTS

The following treatment summary is taken from Volume 6 of the NCHRP 500 series guidebooks 

for addressing ROR collisions (pages V-27 through V-30).(7) 

The 1999 statistics from FARS show that for two-lane, undivided, non-interchange, 

non-junction roadways, 11 percent of single-vehicle ROR fatal crashes occur on 

wet roadways, with 3 percent more occurring on roadways with snow, slush, or ice. 

Accidents on wet pavements are often related to the skid resistance of the 

pavement. It can also happen that the pavement friction available under dry 

roadway conditions will be significantly less than specified for the roadway and 

assumed in establishing design criteria (e.g., superelevation on curves). This can 

also lead to crashes. However, the major problem appears to be with wet pavement 

crashes. 

A vehicle will skid during braking and maneuvering when frictional demand 

exceeds the friction force that can be developed at the tire-road interface. While this 

can happen on dry pavements at high speeds, friction force is greatly reduced by a 

wet pavement surface. In fact, a water film thickness of 0.002 inches reduces the 

tire pavement friction by 20 to 30 percent of the dry surface friction. Therefore, 

countermeasures should seek to increase the friction force at the tire-road interface 

and reduce water on the pavement surface. The coefficient of friction is most 

influenced by speed. However, many additional factors affect skid resistance, 

including the age of the pavement, pavement structural condition, traffic volume, 

road surface type and texture, aggregates used, pavement mix characteristics, tire 

conditions, and presence of surface water. 

There has been a large amount of research funded by the FHWA, AASHTO, and 

pavement associations concerning designing better pavements—pavements which 

are more durable and more cost-effective (e.g., the FHWA/AASHTO Strategy 

Highway Research Program). The FHWA has issued a series of pavement-related 

technical advisories on such issues as needed changes in surface finishing of 

Portland cement concrete pavements for increased safety (FHWA, 1996).(11) An 

important parameter in all this work is pavement skid resistance, perhaps the major 

safety-related factor along with pavement drainage design. However, most of this 

research and implementation effort is oriented toward policy or systemwide 

changes in new pavements or repaving efforts. While the best safety-related 

pavement design possible should be used in all paving efforts, the details of 

pavement design are beyond the scope of this guide. 

Instead, this section will concentrate on improvements that can be made to sites that 

have, or are expected to experience, skidding-related ROR crashes. These usually 

involve improvements to increase skid resistance (higher friction factor). Such 

improvements should have high initial skid resistance, durability to retain skid 

resistance with time and traffic, and minimum decrease in skid resistance with 

13 
increasing speed. Countermeasures to improve skid resistance include asphalt 

mixture (type and gradation of aggregate as well as asphalt content), pavement 

overlays on both concrete or asphalt pavements, and pavement grooving. Water can 

also build up on pavement surfaces due to tire rutting, an inadequate crown, and 

poor shoulder maintenance. These problems can also cause skidding crashes and 

should be treated when present. While there is only limited research on such sitespecific programs, the results of this research coupled with the results of research 

on the general effectiveness of decreasing skidding would place this in the “proven” 

category. 

Treatment will target locations where skidding is determined to be a problem, in 

wet or dry conditions. The ultimate target, however, is a vehicle involved in a crash 

due to skidding, usually on wet pavement. With respect to ROR or head-on crashes, 

the target vehicle is one that runs (skids) off the road due to insufficient skid 

resistance or becomes involved in a head-on crash either by skidding into the 

opposing lane or by crossing into the opposing lane after an overcorrection from an 

initial ROR maneuver caused by insufficient skid resistance.

There are many different specific countermeasures that may be implemented to 

improve skid resistance. This may include changes to the pavement aggregates, 

adding overlays, or adding texture to the pavement surface. The effectiveness of the 

countermeasure not only depends on that measure selected, but also will vary with 

respect to location, traffic volume, rainfall propensity, road geometry, temperature, 

pavement structure, etc. 

The New York State DOT has implemented a program that identifies sites statewide 

that have a low skid resistance and treats them with overlays or microsurfacing as 

part of the maintenance program. A site is eligible for treatment if its 2-year wet 

accident proportion is 50 percent higher than the average wet accident proportion 

for roads in the same county. Between 1995 and 1997, 36 sites were treated on 

Long Island, resulting in a reduction of more than 800 annually recurring wet road 

accidents. These results and others within the state support earlier findings that 

treatment of wet road accident locations result in reductions of 50 percent for wet 

road accidents and 20 percent for total accidents. While the reductions in ROR or 

head-on crashes cannot be extracted from the data at this time, it appears that 

reductions in these types would be at least the same as for total crashes. 

While these results could be subject to some regression-to-the-mean bias, the New 

York staff has found that untreated sites continue to stay on the listing until treated 

in many cases—an indication that these reductions are clearly not totally due to 

regression. The New York State DOT is planning a more refined data analysis to 

account for possible biases in these effectiveness estimates. Based on the current 

knowledge, this identification/treatment strategy would be classified as “proven.” 

Monitoring the skid resistance of pavement requires incremental checks of 

pavement conditions. Evaluation must identify ruts and the occurrence of polishing. 

Recent research (Galal et al., 1999) has suggested that the surface should be 

14 
restored between 5 and 10 years in order to retain surface friction, but the life span 

is affected by site characteristics such as traffic volume.(12) In addition, spot- or 

section-related skid accident reduction programs will be clearly most successful if 

targeted well. The New York State DOT program noted above provides a 

methodology for such targeting. In addition, in a 1980 Technical Advisory, the 

FHWA provided a detailed description of a “Skid Accident Reduction Program,” 

including not only details of various treatments, but also the use of crashes and 

rainfall data in targeting the treatments. Skid resistance changes over time. This 

requires a dynamic program and strong commitment. As noted in the preceding 

section, it also requires good “targeting.” When selecting sites for skid resistance 

programs, it is important to somehow control for the amount of wet-pavement 

exposure. This will help decrease the identification of sites that have a high wetaccident proportion or rate simply because of high wet-weather exposure with no 

real pavement-friction problems. Unfortunately, it is difficult or impossible for an 

agency to develop good wet-pavement crash rates per vehicle mile for all roadway 

sections due to the lack of good wet-weather exposure data for all sites. Such data 

would require both good rainfall data for all potential sites and good measures of 

traffic volume during wet and dry weather. In its Skid Accident Reduction Program, 

the New York State DOT uses a surrogate for such detailed data. The DOT 

compares the proportion of wet-weather crashes at each site with the proportion for 

similar roads in the same county. The assumption here is that rainfall (and thus wetpavement exposure) would be similar across a county, a reasonable assumption. 

Data are needed on traffic crashes by roadway condition. In addition, measures of 

traffic exposure that identify and reflect both dry and wet periods are needed. 

Finally, measurements of road friction and pavement water retention should be 

documented both before and after implementation of a strategy. 

New York State DOT estimates that its resurfacing/microsurfacing projects are 

approximately 0.5 miles long, with an average treatment cost of approximately 

$20,000 per lane mile (1995 dollars). 

PROVIDE GROOVED PAVEMENT

The following treatment summary is taken from Volume 7 of the NCHRP 500 series guidebooks 

for horizontal curves collisions (pages V-25 through V-27).(8) 

Pavement grooving is a technique by which longitudinal or transverse cuts are 

introduced on a surface to increase skid resistance and to reduce the number of wetweather crashes. The grooves increase skid resistance by improving the drainage 

characteristics of the pavement and by providing a rougher pavement surface. 

Several studies show that grooved pavements reduce wet-weather crashes. 

However, some potential adverse effects should be considered before this strategy 

is implemented, including the potential of increased noise pollution, accelerated 

wearing of pavements, and negative effects on steering. 

15 
While pavement grooving is a way to add texture to the pavement surface, its 

primary objective is to improve the drainage and to mitigate hydroplaning. The 

grooves decrease the water film thickness on a pavement surface and allow for 

greater tire-pavement surface interaction during adverse weather conditions. 

Because pavement grooving is such a unique approach to increasing the skid 

resistance of a pavement, it is treated separately. The section immediately following 

this one presents results of studies that evaluated the safety effectiveness of 

pavement grooving. That is followed by a section that presents attributes unique to 

pavement grooving that should be considered before this type of treatment is 

implemented. 

Numerous studies on the safety effectiveness of pavement grooving have been 

conducted, but none of these studied have controlled for regression to the mean so 

the results should be considered with caution. Wong (1990) performed a beforeafter evaluation of the effectiveness of pavement grooving based upon data from 

one site in California.(13) The site was a two-lane highway with steep vertical grades 

and sharp horizontal curves. Based upon accident data from a 3-year before period 

and a 1-year after period, Wong found a 72-percent reduction in wet-pavement 

accidents, while only finding a reduction of about 7 percent in dry-pavement 

accidents. Wong concluded that pavement grooving was effective in reducing wetpavement accidents. 

Zipkes (1976) analyzed the frequency of accidents and the percentage of accidents 

on wet and dry pavement surfaces during a 7-year period to evaluate the effect of 

pavement grooving.(14) Accident data were obtained for a 44-km (27-mi) section of 

highway near Geneva, Switzerland. Transverse grooves were cut into the pavement 

with varying groove distances over a 2-km (1.2-mi) section of highway. Grooving 

of the polished road surfaces reduced the hazard of accidents when drainage 

conditions were unfavorable. Zipkes indicated that the advantage of grooving is the 

reduction of water-film thickness, which leads to better contact between the tire and 

the road surface for the transmission of forces. 

Smith and Elliott (1975) evaluated the safety effectiveness of grooving 518 lane-km 

(322 lane-mi) of freeways in Los Angeles, while 1,200 lane-km (750 lane-mi) of 

ungrooved pavement were used as a control.(15) The analysis was conducted using 

2 years of before data and 2 years of after data. Only fatal and injury accidents were 

included in the evaluation. Smith and Elliott found that longitudinal pavement 

grooving resulted in a 69-percent reduction of wet-pavement accident rates. 

Sideswipe and hit object accidents were reduced to the largest extent. Pavement 

grooving did not change the dry-pavement accident rates. 

Mosher (1968) synthesized results from studies conducted by state highway 

departments on the effects of pavement grooving.(16) Information for the report was 

obtained from 17 states, including Colorado, Florida, Georgia, Idaho, Illinois, 

Indiana, Louisiana, Minnesota, Missouri, Nebraska, New York, Ohio, 

Pennsylvania, Texas, Utah, Wisconsin, and Wyoming. Some sections of highway 

16 
had longitudinal grooves, while other sections had transverse grooving. Pavement 

grooving proved very effective, reducing crashes by 30 to 62 percent. 

Farnsworth (1968) evaluated the effects of pavement grooving on five sections of 

California highways.(17) Farnsworth measured the coefficients of friction before 

grooving and after grooving and found that pavement grooving increased the 

coefficients of friction, changing the friction values from below critical to above 

critical. Analysis of accident data revealed a reduction in wet-pavement accidents at 

each of the sites. 

The NYDOT evaluated the safety effectiveness of pavement grooving based on the 

installation of grooves at 41 sites. NYDOT found that wet-road accidents were 

reduced by 55 percent, and total accidents (dry and wet) were reduced by 

23 percent. The results were statistically significant at the 95th percentile. 

Regression to the mean was not addressed in the analysis. 

Pavement grooving involves making several shallow cuts of a uniform depth, 

width, and shape in the surface of the pavement (Mosher, 1968).(16) Grooves may be 

cut longitudinally along the pavement (parallel to the direction of travel) or in the 

transverse direction (perpendicular to the direction of travel). Transverse grooving 

has been used to a lesser extent than longitudinal grooving, partially because most 

grooving equipment lends itself more readily to placing grooves parallel to the 

roadway. Grooves cut in the longitudinal direction have proven most effective in 

increasing directional control of the vehicle, while transverse grooving is most 

effective where vehicles make frequent stops, such as intersections, crosswalks, and 

toll booths. When pavements are grooved, it is important that the pavement contain 

nonpolishing aggregate. 

While studies have indicated that pavement grooving reduces wet-pavement 

accidents, there have been several concerns associated with pavement grooving 

(Mosher, 1968).(16) One concern has been the effect that pavement grooving has on 

the durability of various pavement types. For example, one of the most frequently 

asked questions by engineers in northern climates is, “What will water freezing in 

the grooves do to the concrete pavement?” In an examination of grooved pavement 

in Minnesota after one winter, there appeared to be no deterioration in the grooved 

pavement because of the freeze-thaw cycles. Concern also has been expressed about 

grooves in asphalt pavement losing their effectiveness because the material can be 

flexible enough to “flow” back together, particularly during hot weather. This 

phenomenon has been observed under certain conditions with a fairly new asphalt 

pavement or with a pavement with low aggregate content. Concern has also been 

expressed over the loss of effectiveness because of grooved pavements wearing 

down under high-traffic conditions. 

Complaints also have been received that longitudinal grooves adversely affect the 

steering of certain automobiles and motorcycles. In general, no severe problems 

have been encountered. This finding is supported by research conducted by 

Martinez (1977), who studied the effects of pavement grooving on friction, braking, 

17 
and vehicle control by computer simulation and full-scale testing.(18) Martinez 

considered automobiles, motorcycles, and automobile and towed-vehicle 

combinations in his evaluation. 

In Iowa, residents living adjacent to I-380 near Cedar Rapids complained that 

transverse grooving was the cause of an especially annoying tonal characteristic 

within the traffic noise (Ridnour and Schaaf, 1987).(19) As a result of the 

complaints, the surface texture of a section of I-380 was modified. The transverse 

grooving was replaced with longitudinal grooving. A before-after analysis of the 

traffic noise levels showed that the surface modification lowered overall traffic 

noise levels by reducing a high-frequency component of the traffic noise spectrum. 

FURTHER LITERATURE REVIEW 

A search of available literature related to the safety effects of improved skid resistance turned up 

few additional materials. The limited research available does indicate, as would be expected, that 

higher skid resistance measurements are associated with lower crash rates, particularly wet-roadrelated collisions. Studies comparing the safety improvement after specific skid resistance 

improvement treatments are particularly rare, and the data and evaluation methods typically 

poor. These limited studies do, however, indicate reductions in collisions following treatment.

Additional literature was also identified related to low-cost pavement preservation treatments 

and their properties.

Neuman et al. discuss in general terms specific countermeasures that may be implemented to 

improve skid resistance.(7) These may include changes to the pavement aggregates, adding 

overlays, or adding texture to the pavement surface. They state that the effectiveness of the 

countermeasure not only depends on the measure selected, but also varies with respect to 

location, traffic volume, rainfall propensity, road geometry, temperature, pavement structure, etc. 

They indicate that when selecting sites for skid resistance programs, it is important to somehow 

control for the amount of wet-pavement exposure. 

Torbic et al. discuss pavement grooving.(8)

 Pavement grooving is a technique by which 

longitudinal or transverse cuts are introduced on a surface to increase skid resistance and to 

reduce the number of wet-weather crashes. The grooves increase skid resistance by improving 

the drainage characteristics of the pavement and by providing a rougher pavement surface. 

Several studies showed that grooved pavements reduce wet-weather crashes between 55 and 

72 percent although the evaluation methods applied are not considered state-of-the-art by today’s 

standards. 

Lyon and Persaud evaluated the safety impacts of the New York Department of Transportation 

(NYSDOT) skid-accident reduction program.(20)

 In this program, sections of roadway with a high 

proportion of wet-road accidents are identified and are friction tested. Those locations with poor 

friction numbers are then treated with a 1.5-inch HMA resurfacing or a 0.5-inch microsurfacing 

using non-carbonate aggregates. Resurfacing is considered to be effective for 15 years while the 

microsurfacing is effective up to 7 years, depending on the existing pavement condition and 

quality of construction. Friction testing was done (using a locked-wheel skid trailer with ribbed 

tire), and readings under 32 were considered to warrant treatment. The EB before–after study 

18 
approach was applied to several crash types and both segment and intersection locations. Results 

for expected accident reductions are shown in table 1. Further results are available in the paper, 

disaggregated by area type and number of lanes for segments and traffic control type and number 

of approaches for intersections. 

Table 1. Summary of results from NYSDOT skid-accident reduction program analysis. 

Accident Type

Road Segments

(percent)

Intersection Results

(percent)

Total 24 20

Wet-road 57 57

Rear-end 17 42

Wet-road rear-end 42 68

Single-vehicle 30 n/a

Single-vehicle wet-road 60 n/a

Ivan et al. explored the relationship between wet-pavement friction and crashes to identify 

whether wet-pavement friction explains significant variation in crash frequency between similar 

locations, and whether this is particularly significant at high crash locations such as sharp curves 

and intersections.(21)

 Data for approximately 150 mi of roadway were collected. Three years of 

crash data were collected where available. The amount of friction at each location was measured 

using the locked-wheel skid trailer. Negative binomial regression models K, A, or B crashes on 

the KABCO scale were developed separately for divided and undivided roadways. Additional 

explanatory variables considered included degree of horizontal curvature, rate of change of 

vertical curvature, number of intersections and driveways, pavement width, area type (rural, 

suburban, or urban) and speed limit. Dependent variables considered included total, wet-road, 

segment related (sideswipe opposite direction, head-on fixed object, and moving object), and 

intersection related (turning same direction, turning intersecting paths, sideswipe same direction, 

angle, rear-end, and pedestrian) crashes. The model results indicated that wet-pavement friction 

is most associated with increased crashes under conditions where increased braking would be 

demanded, that is in curves and near driveways. Interestingly, increased wet-pavement friction 

was associated with more total crashes on urban undivided roads with mild curvature and on 

urban divided roads. 

Oh et al. conducted naïve and comparison group before–after studies of three experimental types 

of pavements: open graded asphalt concrete (OGAC), groove pavement (GP), and rubberized 

open graded asphalt concrete (R-OGAC).(22) Wet-pavement-related crashes were the focus. The 

findings included a 29 and 41 percent decrease for the 13 OGAC sites using the naïve and 

comparison group approaches, respectively. The sample sizes were too small to draw

conclusions for the GP and R-OGAC. Calculation of crash rates included the exposure to wet 

weather, which was collected from the closest weather recording station. Another part of the 

study found that the friction numbers are dependent on seasonal effects, including temperature, 

average monthly precipitation, and the number of dry months prior to last precipitation. 

Izevbekhai and Watson evaluated the before and after collision data for 14 concrete pavement 

sections where the pavement was overlaid or rebuilt and the new surface included a longitudinal 

turf drag, or broom drag.(23)

 Previously, transverse friction treatments (e.g., tining) had been 

applied but were discontinued owing to concerns regarding noise. The study sought to determine

19 
whether the new longitudinal treatments were as effective in providing adequate friction. 

Collisions were analyzed to see whether frequencies, collision rates (per million vehicle-mi), 

proportion of wet-weather collisions, or the ratio of wet to dry collision counts increased 

following the treatment. Differences were subjected to the Chi-squared and Mann Whitney U 

tests to measure the statistical significance of any differences between the before and after 

periods. The segments analyzed were selected to be minimally influenced by other collision risk 

factors such as curves, poor sight distance, poor surfaces, etc. The results found no significant 

differences in the various crash measures from before to after the new treatment.

Erwin conducted a naïve before-after study of resurfacing and microsurfacing projects. Results 

for microsurfacing indicate a 32-percent reduction in wet-weather collisions, 24-percent

reduction in intersection collisions, and 29-percent reduction in rear-end collisions.(24) 

Reddy et al. evaluated the application of the TyregripTM HFS system to a 300-ft section upstream 

of an on-ramp in Florida.(25) The ramp was treated because a high number of wet-weather ROR

collisions had occurred there. Skid testing confirmed that the available skid resistance was much 

higher (104) after treatment compared with 35 before. It was also observed that vehicle speeds 

decreased, as did vehicle encroachments to either shoulder. The limited time periods and single 

location did not allow for a scientific study of collisions, although they were observed to 

decrease from an average of 2.54 per year before treatment to 2 in a 1-year period after, a 

decrease of 21 percent. 

Mayora and Pina studied the relationship between skid resistance and injury collisions on twolane rural roads in Spain.(26)

 Segments including intersections were not included. Average 

sideway-force coefficient routine investigation machine (SCRIM) skid resistance measurements 

over a 5-year period were included in the analysis. Categories of alignment (e.g., tangent, radius 

> 500 m, radius 250–500 m, radius < 250 m) and categories of skid resistance (e.g., SCRIM 

≤ 40, 40 < SCRIM ≤ 45, 45 < SCRIM ≤ 50, 50 < SCRIM ≤ 55, 55 < SCRIM ≤ 60, SCRIM > 60) 

were defined for the analysis. Statistical tests were applied to see whether the mean crash rates 

differed between SCRIM categories for each alignment category tested. A before–after 

comparison group study was also conducted to assess the benefits of skid resistance 

improvements. Because the comparison group crash rate was higher (0.32 to 0.29 wet-road 

crashes), it was concluded that the treated sites were not selected based on the crash rate and 

regression-to-the-mean was not a factor. A sample of 419 segments with an average SCRIM 

value less than 50 was treated to improve the SCRIM value to more than 60. Results of crash rate 

analyses showed that both wet- and dry-road crash rates decreased as skid resistance increased. 

Wet-road crash rates were found to be significantly higher in curves than on tangents. For dryroad crashes, no differences were found between curves and tangents. It was concluded that for 

tangents and curves with a radius less than 500 m, crash rates are significantly lower when the 

SCRIM value is greater than 55. For curves with a radius greater than 500 m, the SCRIM value 

cutoff is 60. The before–after study indicates the benefits of increasing the skid resistance 

(SCRIM value) from less than 50 to greater than 60 is a 68-percent reduction in wet-road 

crashes. When considering curves only, the reduction was estimated to be 84 percent. 

Hughes studied the impacts of thin HMA resurfacing projects on crash performance for two-lane 

roads with posted speeds greater than 45 mi/h.

(27)

 The purpose of the study was to determine 

whether new resurfacing projects have any impact on safety, resulting from “the improved ride 

20 
quality and visual contrast created by new pavement markings on a smooth asphalt surface that 

could create for the driver the impression of a safer road that can be traversed at a higher speed.” 

This is commonly referred to as a “novelty effect,” which may result in more crashes initially 

after resurfacing before the effect wearing off over time. The study contrasted resurfacing 

projects that were coupled with minor or major safety improvements with those where only 

resurfacing was performed. Some of the key findings from this study were as follows:

• The results were inconclusive regarding the effect of resurfacing on crash rates. For 

some States, there was an improvement, but for others there was no benefit, or even 

an increase in crash rate following resurfacing. This is likely the result of highly 

variable conditions (and presumably paving materials) from State to State and project 

to project, which could not be comprehensively quantified for the study sites. 

• There is no evidence to suggest that resurfacing adversely affects crash frequency 

downstream from the resurfaced section of the road. 

• With respect to analysis methodology, selecting sites according to their pavement 

history (and not crash history), using a long crash history (3 to 5 years), and using a 

large sample size (e.g., long stretches of highway) help to mitigate regression-to-themean and the random nature of data. 

Li et al. evaluated the long-term friction performance of pavement preservation treatments 

commonly used by the Indiana Department of Transportation to assist in the decisionmaking 

process regarding when and where to use various preservation treatments.(28) Treatments 

evaluated included chip seals, fog seals, microsurfacing, thin and ultra-thin asphalt overlays 

(including UTBWC), and diamond grinding. Key findings for the various treatments were as 

follows: 

• For chip-sealed surfaces, the greater the friction number on the old pavement, the 

greater the friction on the new chip-sealed surface. Surface friction decrease occurred 

after 12 mo in service, and when the surface reached an age of approximately 30 mo, 

friction started to decrease continuously over time. Also, truck traffic was observed to 

affect the performance of a chip seal more significantly than AADT.

• For microsurfacing, friction increased significantly in the first 6 months and peaked 

after 12 mo. After 12 mo, friction tended to decrease continuously over time, but 

never to what might be considered an intervention level, even after 42 mo. 

• For UTBWC, friction numbers tended to peak after 6 mo of service, “about 6 mo 

earlier than conventional HMA mixes.” UTBWC provided good texture depth 

(macrotexture), much better than conventional HMA surfaces. However, significant 

friction decrease occurred over time, with up to a 34-percent decrease after 33 mo in 

service. Noticeable polishing was observed in the limestone aggregate used. 

• For thin, fine graded (4.75 mm) HMA overlays, friction is high after construction but 

decreases quickly and dramatically over time after exposure to traffic. In some 

sections, friction decreased by 25 percent after 6 mo and by 36 to 48 percent after 

21 
12 mo, depending on traffic volume. Friction tended to stabilize after 12 to 18 mo. 

The use of steel slag in these mixes is recommended for better friction performance. 

• For diamond grinding on concrete surfaces, the steady-state friction should be 

maintained over time, but was highly dependent on the aggregate properties and 

grinding texture configuration. 

Roe et al. examined the relationship between pavement surface texture and crashes based on an 

extensive analysis of texture, friction, and crash data in the United Kingdom.(29) Some of the key 

findings from this study included the following: 

• Macrotexture has a marked effect on all accidents, whether or not they involve 

skidding or whether conditions are wet or dry. High macrotexture has a beneficial 

effect in all circumstances.

• Injury accidents are less likely to occur at high texture depths. This is possibly due to 

the higher hysteresis component of friction, providing drivers with additional control 

and maneuverability in all conditions to reduce severity of the crash. 

• In terms of guidance for desirable macrotexture depth, the cross-over point falls 

between 0.6 mm and 0.8 mm, indicating that the risk of accidents is greater for roads 

with an average texture depth less than about 0.7 mm than for those above this level. 

• Crash rate rises markedly with decreasing texture less than about 1 mm, but is 

essentially constant at a low level greater than 1 mm. 

• Skidding resistance (as measured by SCRIM) is normally independent of texture 

depth, but may be reduced when the macrotexture is unusually low, for example on a 

worn surface dressing (chip seal). 

• The idea of a minimum level of macrotexture does not affect in any way the existing 

requirements for minimum levels of skidding resistance. Both macrotexture and 

skidding resistance are needed for safe roads.

Davies et al. used highway data from 1997 to 2002 in New Zealand from the entire State 

Highway network to try to look for any correlations between crash rate and road characteristics 

(traffic, texture, skid resistance, curve radius, cross-fall, roughness, and rut depth).(30) Only twolane roads were included in the analysis. Some of the key findings from this study included the 

following: 

• As traffic volume decreases, crash rate increases. This was not unexpected because 

the quality of the road reflects daily traffic.

• As skid resistance increases, the greatest percent reduction in crash rate occurs for 

wet-road crashes.

22 
• The percent reduction in crash rate is constant for a given absolute increase in skid 

resistance value, regardless of the initial skid resistance value.

• Crash risk is reduced with increasing texture, although not at a statistically significant 

level. The relationship between crash rate and texture is not strong. 

• The primary emphasis should be on increasing skid resistance rather than texture. 

• In percentage terms, increasing skid resistance (e.g., SCRIM) has a greater effect in 

reducing “wet-road crashes” than in reducing “all crashes.” 

Peshkin et al. developed guidelines for the use of pavement preservation treatments on highvolume roadways as part of a Strategic Highway Research Program 2 (SHRP2) Renewal 

research project.(31) Agencies traditionally tend to shy away from preservation on high-volume 

roadways, and this project sought to provide substantial guidance for preservation practices on 

high-volume roadways. High-volume roadways were defined under this effort as those with an 

average daily traffic of at least 5,000 and 10,000 vehicles per day for rural and urban roadways, 

respectively. This report provided valuable information on the expected design life and cost of 

various pavement preservation treatments. It also addressed some of the appropriate applications 

and risks associated with various treatments, and should serve as a ready reference for agencies 

in selecting preservation treatments. 

The literature review, while sparse, did reveal important insights to consider, including the 

following: 

• Pavement friction is dependent on seasonal effects, including temperature, average 

monthly precipitation, and the number of dry months prior to last precipitation. The 

readings must be standardized across sites and years.

• Confounding factors that influence collision risk and may interact with the safety 

effects of skid resistance include location type (segment, intersection approach, curve, 

etc.), area type, speed limit, traffic volume, rainfall propensity, roadway geometry, 

temperature, and pavement structure. 

• Expected collision reductions from friction improvements depend on both the level of 

friction prior to and after treatment. Reductions in wet-road crashes up to 

approximately 75 percent may be expected.

• Friction of various low-cost pavement treatments tends to decrease with time, and 

therefore the safety benefit may also be expected to decrease with time.

• Evaluations of improved skid resistance have typically not applied statistically 

rigorous before–after study designs.

• While good texture (depth) is important, it does not guarantee a good skid resistance 

or a safer pavement.

23 

CHAPTER 3. PAVEMENT TREATMENT TYPES

The pavement treatment types considered in this study were identified in the original proposal by 

FHWA and the project team. This list was further refined based on the treatment types that were 

provided by the various volunteer States.

PHASE VI TREATMENTS CONSIDERED

Table 2 lists the treatment types considered in the study, with the treatments that were included 

in the final analysis highlighted in bold. 

Table 2. Flexible and concrete pavement treatment strategies considered in Phase VI.

FLEXIBLE PAVEMENT 

TREATMENT STRATEGIES

CONCRETE PAVEMENT 

STRATEGIES

Thin HMA Overlay Thin HMA Overlay

Open Graded Friction Course (OGFC) Open Graded Friction Course (OGFC)

Ultra-Thin Bonded Wearing Course 

(UTBWC)

Ultra-Thin Bonded Wearing Course 

(UTBWC)

Microsurfacing Microsurfacing

Shotblasting/Abrading Shotblasting/Abrading

High Friction Surfacing (HFS) High Friction Surfacing (HFS)

Chip Seal (various binder types) Diamond Grinding

Cape Seal Grooving

Scrub Seal Next Generation Concrete Surface

Slurry Seal

Micro-Milling

Bold entries are treatment types that were included in final analysis. 

The decision was made early in the data collection effort to focus primarily on the most common 

treatments that are used throughout the United States, while also considering experimental-type 

treatments, if appropriate. As discussed previously, the majority of these treatments are typically 

used for pavement preservation purposes to extend the life of an existing pavement while also 

potentially improving skid resistance and ride quality. The notable exception is HFS, which is 

almost exclusively used for safety improvement purposes. The following sections summarize 

each of these treatment types.

Thin Hot-Mix Asphalt Overlay

Thin HMA overlays (figure 5) are commonly used to correct minor to moderate pavement 

surface defects to restore ride quality and improve friction while protecting the underlying 

pavement structure. Thin overlays may be applied to either concrete or asphalt pavements, or 

over existing surface treatments, and are typically not considered a structural layer. Industry 

convention generally defines thin overlays as no more than 1.5 to 2 inches thick, typically 

constructed as a single lift, and therefore was the criterion used by the project team under this 

effort. In the list of thin HMA overlay candidate sites, the team included “mill and fill” projects, 

25 
which included milling off the existing surface before applying the new overlay. Although the 

milling operation adds to the cost of the treatment, best practice generally dictates that the 

existing surface be milled off for best performance of the overlay. While thin overlays are not 

typically constructed explicitly to improve friction, mixtures are still designed to standards that

will ensure appropriate friction levels. 

Source: The Transtec Group, Inc.

Figure 5. Photo. Placement of thin asphalt overlay (top) and surface texture of new (bottom 

left) and worn (bottom right) dense graded asphalt surfaces.

Open Graded Friction Course 

An OGFC treatment (figure 6) is a type of thin HMA overlay, but uses an open graded or porous 

asphalt mixture that allows water to quickly drain away from the surface by flowing through the 

mixture itself. This helps to minimize sheeting or standing water on the surface and the potential 

for hydroplaning. The porosity of an OGFC can also significantly reduce tire–pavement noise 

and splash and spray potential of the pavement surface. Similar to conventional thin HMA 

overlays, OGFCs are not typically considered a structural layer and are typically used to renew 

the functional performance of a pavement, including ride quality, friction, and tire–pavement 

noise. They can be placed over pavement with minor to moderate surface defects, but not those 

with substantial distresses caused by subsurface issues. OGFCs are beneficial for locations with 

 

 

26 
high amounts of rainfall, but generally are not used in colder climates because of their poor 

performance during freeze–thaw cycles. Because a specialty asphalt mixture is used to achieve 

the open graded texture, they are typically more expensive than conventional dense-graded 

asphalt. However, superior functional performance makes this treatment desirable for highpriority urban areas.

Source: The Transtec Group, Inc.

Figure 6. Photo. Surface texture of OGFC. 

Ultra-Thin Bonded Wearing Course

UTBWC is a specialty ultra-thin asphalt overlay used to restore ride quality while sealing and 

protecting the underlying pavement. It can also be used to mitigate shallow (less than 0.5-inch) 

rutting and can help retard fatigue cracking.(32)

 UTBWC is a non-structural layer, typically only 

0.5- to 0.75-inch thick and generally uses a gap-graded aggregate and polymer-modified asphalt. 

One of the primary differences between UTBWC and conventional ultra-thin asphalt overlays is 

how the treatment is placed. An emulsion layer is applied to the pavement surface immediately 

in front of the paving screed using a self-priming paver (figure 7). The emulsion helps to seal the 

underlying pavement surface while also immediately bonding it to the new asphalt surface. 

UTBWC was originally developed as a proprietary product called NovaChip, but since the patent 

expired, several State transportation departments have developed their own specification for this 

treatment. UTBWC can be applied to existing asphalt or concrete pavement or over other surface 

treatments. The underlying pavement must be structurally sound with only minor rutting, minor 

to moderate cracking, and minor to moderate bleeding and raveling.(33) 

27 
Sources: top: Roadtec; bottom: The Transtec Group, Inc.

Figure 7. Placement of an UTBWC using a self-priming paver (top) and surface texture of 

an UTBWC (bottom).(34) 

Chip Seal (Seal Coat) 

Chip seals or seal coats are a common bituminous pavement preservation treatment used to seal 

fine cracks in the underlying pavement surface and prevent water intrusion into the underlying 

pavement structure, while sustaining or improving pavement friction.(35) Chip seals are 

constructed by first applying a bituminous membrane onto the existing pavement followed by a 

layer of aggregate or “chips,” which are dropped onto the surface then rolled to embed them in 

the binder. (See figure 8.) The bituminous membrane is typically a polymer-modified asphalt 

 

28 
emulsion, but can also be a liquid asphalt material (asphalt cement or cutback), including 

rubberized asphalt. Chip seals are typically only applied to existing asphalt pavement or 

bituminous surface treatments, but have also been used for unpaved roads. Chip seals are not a 

structural layer, but do provide a very durable wearing surface. They are susceptible to chip loss, 

which can result in flying chips and broken windshields, and are therefore not commonly used on 

heavily traveled urban roadways. However, they are commonly used on rural high-speed 

roadways, including rural interstates and State highways. There are several varieties of chip 

seals, including single, double, and triple layer treatments that may use a variety of aggregate 

sizes in the different layers, as well as different types of aggregate such as lightweight material.

Sources: top: California Chip Seal Association; bottom: The Transtec Group, Inc.

Figure 8. Photo. Placement of a chip seal (top) and surface texture of a single layer chip 

seal (bottom).

(36)

 

29 
Slurry Seal

Slurry seal is a low-cost bituminous surface treatment used to seal the underlying surface from 

water infiltration, fill surface cracks and voids, and improve friction and appearance of an 

existing pavement.

(24) Slurry seal is a mixture of emulsified asphalt, water, fine aggregate, and 

mineral filler that is mixed into a slurry and applied or screeded onto the pavement surface in a 

thin layer using squeegees or a spreader box (figure 9). Slurry seals do not provide any structural 

benefit to the pavement, but are a very cost-effective treatment for preserving the existing 

pavement surface, improving appearance, and restoring or enhancing friction. 

Source: Ace Asphalt

Figure 9. Photo. Placement of a slurry seal (top) and surface texture of a cured slurry seal 

(bottom).(37) 

Microsurfacing

Microsurfacing is a surface treatment very similar to slurry seal, but is typically a more durable 

treatment that is used for higher volume roadways. Like slurry seal, microsurfacing is a slurry 

mixture consisting of emulsified asphalt, water, fine aggregate, and mineral filler.

Microsurfacing, however, typically uses a polymer-modified asphalt emulsion, which gives it 

30 
more flexibility than a conventional asphalt binder. Microsurfacing is primarily used to mitigate 

raveling and oxidation of asphalt pavement surfaces, but also improves friction and appearance 

of both asphalt and concrete surfaces. Microsurfacing can be designed with larger aggregate for 

use in filling shallow to moderate depth ruts in asphalt pavement, and can also seal low-severity 

cracks.

(31)

 Microsurfacing is applied in a similar manner to slurry seal, using a spreader box 

behind a slurry truck. (See figure 10.) 

Sources: top: International Slurry Surfacing Association and VSS MacroPaver; bottom: Ace Asphalt

Figure 10. Microsurfacing placement (top) and surface texture of cured microsurfacing

treatment (bottom).(38,37)

 

Diamond Grinding

Diamond grinding is a process used to shave a thin layer (typically less than 0.25-inch) of the 

surface of pavements, primarily for improving ride quality, but also for restoring or improving 

skid resistance and reducing tire–pavement noise. Diamond grinding is performed using grinding 

equipment that uses a cutting head consisting of a stack of concrete cutting saw blades with 

diamond-encrusted teeth. The saw blades on the cutting head are spaced 0.08 to 0.10 inches 

apart, leaving shallow grooves in the pavement surface that provide macrotexture (figure 11). 

Although diamond grinding can be used to grind out localized roughness in asphalt pavements, it 

is most commonly used more for concrete pavements to mitigate slab curing and faulting at 

joints, and to restore surface texture. 

31 
Source: The Transtec Group, Inc.

Figure 11. Photo. Diamond ground concrete pavement surface.

Grooving 

Grooving is a treatment in which narrow grooves are sawcut into the pavement surface, typically 

in the direction of traffic, and typically 0.75-inches apart. (See figure 12.) The grooves increase 

pavement macrotexture, providing a path for bulk water drainage. Grooving is a surface 

treatment that can be used when it is undesirable to apply any topical treatment to the pavement 

surface (e.g., bituminous surface treatments) or to remove any of the pavement surface (e.g., 

milling or diamond grinding). Grooving effectively ensures a certain level of macrotexture 

regardless of how the pavement surface wears over time. Grooving is commonly used for airfield 

runways and bridge decks, but is becoming more common for highway pavements as well. 

Grooving is typically used on concrete pavements, but can also be done on asphalt. A surface 

treatment termed “Next Generation Concrete Surface,” which combines diamond grinding and 

grooving was developed over the past decade and provides a standardized solution for grooving 

while also improving ride quality.(39)

 

32 
Source: The Transtec Group, Inc.

Figure 12. Photo. Grooved concrete pavement surface (top) and Next Generation Concrete 

Surface (bottom).

Micro-Milling 

Micro-milling is a surface treatment in which a milling head is used to remove a thin layer of the 

pavement surface. Unlike diamond grinding, in which the cutting head shaves or grinds the 

surface away, micro-milling is an impact technique in which the milling teeth effectively chip 

away the pavement surface. Micro-milling differs from conventional milling in that the cutting 

head uses teeth that are spaced closely together, leaving a much less aggressive surface texture 

than conventional milling (figure 13). Whereas milling is typically used to remove pavement in 

preparation for an overlay, micro-milling leaves a much less aggressive surface texture that can 

be opened to traffic as a final surface. Although this is a promising treatment for improving 

pavement friction, it was excluded from this effort because of its very limited usage to date.

33 
Source: Pavia Systems

Figure 13. Photo. Micro-milled asphalt pavement surface.(40) 

Shotblasting/Abrading

Shotblasting/abrading is a surface treatment in which steel pellets or “shot” are fired at the 

pavement surface at high velocity to pit or abrade away a superficial layer of the pavement 

surface (figure 14). Shotblasting removes any loose material from the surface and also pits the 

surface of the aggregates to improve microtexture. It is frequently used to remove rubber or oil 

deposits on the pavement surface. This treatment is commonly used by airports to remove rubber 

deposits on runways. 

34 
Source: The Transtec Group, Inc.

Figure 14. Photo. Shotblast asphalt pavement (top) and concrete pavement (bottom).

For roadways, this treatment is more commonly used for surface preparation prior to applying 

another surface treatment (e.g., HFS) to bridge decks or highway pavements. This treatment was 

ultimately excluded from consideration under this study because of the lack of highway 

treatment sites.

Cape Seal

Cape seal is a surface treatment consisting of a chip seal followed by a slurry seal. After the chip 

seal is applied and cured, the slurry seal is used to cover the chip seal. The advantage of this 

treatment is that the chip seal seals and protects the underlying pavement, while the slurry seal 

helps to protect the chip seal, locking the chip seal aggregate in place to minimize chip/aggregate 

loss and providing a smoother final surface. Ultimately, this treatment was excluded from this 

study because of the limited number of States that use the treatment (and therefore the limited 

35 
number of lane-mi), but also because the finished surface will be similar to that of a slurry seal or 

microsurface, which are already considered separately. 

Scrub Seal

A scrub seal is a treatment in which a bituminous material (emulsion or asphalt binder) is 

literally scrubbed into the surface of a heavily cracked asphalt pavement using brushes 

(figure 15). A cover aggregate is then broadcast over the surface, in a similar manner to a chip 

seal. The scrubbing action helps to ensure the bituminous seal penetrates any cracks in the 

pavement surface to help preserve the asphalt and seal the surface from water infiltration through 

the cracks. This treatment is more suitable for heavily cracked asphalt pavements whose 

underlying pavement structure is still sound. Scrub seals were excluded from this study because 

of the limited number of lane-mi available for study, and also because the finished surface is 

effectively the same as a chip seal. Although it has slightly different applications, such as for 

more distressed pavement, the surface properties (texture and friction) should not differ 

significantly from a chip seal.

Source: FP2, Inc.

Figure 15. Photo. Emulsion material is scrubbed into the pavement surface for a scrub seal 

surface treatment.(41) 

High Friction Surfacing 

HFS is a specialty pavement treatment used specifically to restore or enhance friction. It is 

commonly used for spot treatments of curves, intersections, and steep grades where friction 

demand is higher than can be provided by conventional paving materials. HFS is installed by 

spreading a resin binder (epoxy, methacrylate, polyester, etc.) over the pavement surface 

followed by broadcasting or dropping a 1- to 3-mm abrasion and polish-resistant aggregate onto 

the resin (figure 16). Calcined bauxite, which exhibits exceptional polish resistance, is the most 

commonly used aggregate for HFS worldwide. However, similar aggregates that maintain 

excellent microtexture properties over time have also been used as the aggregate. Although a 

36 
form of HFS has been used extensively for bridge decks and provides the additional benefit of 

sealing the bridge deck surface, it does not provide any documented preservation benefit for 

pavements. However, it is one of the treatments considered in this study that is typically used 

specifically as a safety improvement. Recognizing the safety benefit of HFS, FHWA recently 

deployed HFS as a focus technology under the Every Day Counts 2 program.(42) 

Source: The Transtec Group, Inc.

Figure 16. Photo. Installation of HFS (top) and finished surfaces (bottom).

COMPARISON OF TREATMENT MACROTEXTURE PROPERTIES

The properties of the various treatments considered under this study vary widely from State to 

State and project to project. Unfortunately, it was not possible to quantify these properties for 

each treatment site considered under this study for use in the statistical analysis. Although there 

is no codified typical friction value for any given treatment, some typical macrotexture depths 

for the various treatments are provided in table 3. As discussed in chapter 2, several studies have 

documented the importance of pavement macrotexture (texture depth) and its effect on crash 

rates. Good friction, however, is not guaranteed by good macrotexture. 

37 
Table 3. Typical macrotexture depth for various pavement treatments.

Pavement Treatment Typical Macrotexture Depth(3)

Slurry Seal 0.3 to 0.6 mm

Thin Hot Mix Asphalt Overlay

0.4 to 0.6 mm (Dense Graded)

> 1.0 mm (Stone Matrix Asphalt)

Microsurfacing 0.5 to 1.0 mm

Diamond Grinding 0.7 to 1.2 mm

Grooving 0.9 to 1.4 mm

Ultra-Thin Bonded Wearing Course 

(UTBWC) > 1.0 mm

Chip Seal (various binder types) > 1.0 mm

Open Graded Friction Course 

(OGFC) 1.5 to 3.0 mm

High Friction Surfacing (HFS) > 1.5 mm

HMA = Hot mix asphalt

38 
CHAPTER 4. DATA COLLECTION

OVERVIEW OF DATA COLLECTION PROCESS

Because this was retrospective study, gathering data on existing low-cost pavement treatments 

was the most time-intensive aspect. The project relied heavily on the PFS volunteer States to 

provide the types and quantities of treatments available for analysis. The PFS volunteer States 

worked hard to provide information on the treatments, crash data, and roadway information. The 

following sections briefly outline the process used by the project team to gather the data 

necessary for this study. 

Contact ELCSI-PFS Volunteer States to Identify Treatment Types and Availability of 

Crash and Roadway Data 

The data collection process began by contacting the various ELCSI-PFS volunteer States to 

determine what treatment types would be available for evaluation under this study and to 

ascertain the availability of crash data for these sites. Because the treatments considered under 

this study were not necessarily installed as safety improvements, it was necessary for the State 

transportation department safety engineers to work closely with the pavements/materials/

maintenance engineers to identify what treatments are typically used and exact locations for 

those treatments. 

Request Treatment Locations and Crash and Roadway Data 

After the team had narrowed down the list of treatment types and the State transportation 

departments that were able to provide data, the team formally requested a list of treatment 

locations, crash data, and roadway data from the participating State transportation departments. 

This information was provided to the project team in various formats and further compiled into a 

consistent format by the project team for assessing the sample size.

Narrow List of Treatment Types and States Based on Sample Size

Based on the quantity (number and length) of treatment sites and availability and quality of crash 

data from the States, the team was able to further narrow the list of treatments and states. Having 

an adequate sample size for each treatment and for each State was important for evaluating the 

performance of each treatment, but also for being able to compare performance of the treatments 

between States. The only exception to this occurred with HFS, for which there are a relatively 

small number of installations nationwide, let alone in a given State. 

Verify Treatment Locations, Installation Date, and Underlying Surface 

Once a final list of treatment locations was provided, the project team worked with each State to 

verify the location and installation date for the purpose of mining crash data for each location. 

Pavement treatments considered in this study are typically installed over several months. For this 

reason, the installation year was effectively masked off when analyzing the crash data, leaving 

the years prior to the installation year as the “before” period, and the years after the installation 

year as the “after” period. The team worked with each State to verify that no other surface 

treatment or significant construction had been applied to the study locations during the after 

39 
period. The team also worked with the States to verify the underlying pavement surface type. 

Most States were able to provide pavement or maintenance history records for the routes being 

studied for this purpose. These records were used to reconcile any discrepancies observed 

between the pavement surface type listed in the crash data records and the expected pavement 

type.

Identify Reference Sites and Collect Crash Data

A critical aspect of an EB before–after analysis is accounting for regression-to-the-mean, 

changes in traffic volumes, and time trends in crash reporting, by analyzing reference sites in 

addition to the actual treatment sites. This proved to be a difficult task as the treatments being 

considered are typically installed over long sections of a roadway that may have several 

variations in geometry, shoulders, median, roadway width, and traffic, among other factors. The 

project team used information in the roadway data file for each treatment site to identify 

reference site candidates that had characteristics as similar as possible to the treatment locations. 

One very important characteristic, because it relates to the pavement surface, is the pavement 

type for the reference sites. The team used a process similar to that described above to verify the 

pavement surface type at the reference sites. Once valid reference sites were identified, the 

project team collected crash data for the reference sites for use in the analysis.

Collect additional Data Collection: Climatic Data, Cost Information, and Materials 

Information 

Additional data that were collected for each treatment site included historical climatic data, cost 

information for the various treatment types, and information on the materials used for each 

treatment. Climatic data was collected to assess any effects that climate (precipitation and 

temperature) may have on a particular treatment. Because the treatments were installed in a 

variety of climates, there is the potential that climatic conditions, precipitation in particular, 

could have affected crash frequencies and treatment effectiveness. 

Cost information was collected for use in the BC analysis. Because of the large number of 

treatment sites, it was not possible to collect information on the exact cost of each particular 

treatment or project. Rather, the team looked at recent statewide bid averages to determine a 

rough unit cost for each treatment in each state.

Materials information was compiled from standard specifications and/or standard practices for 

each of the treatment types in each State. Of particular interest were any differences in the 

component materials (aggregates, binders) and tests used to measure various properties of these 

materials. As with cost information, it was not possible to collect materials information on each 

individual treatment site; rather the team simply looked at differences in standard specifications 

for each. 

DATA COLLECTION ITEMS

Table 4 summarizes the list of items originally proposed for collection. It was not possible to 

collect this level of detail for all of the treatment sites because of the limited information 

maintained by each State for specific projects. However, those items that were actually collected

are discussed in more detail later. 

40 
Table 4. List of potential data collected for treatment sites.

Crash Data1 

• Number of crashes

• Crash classification (PDO, Injury, Fatal)

• Passenger information

• Vehicle type

• Pavement conditions

• Ambient conditions

• Reported driver action

• Direction of travel

• Reference location (milepost)

• Other factors affecting crash classification (alcohol, seat belt use, etc.)

Roadway Data

• Roadway classification (rural, urban, interstate, non-interstate)

• Traffic volume and directional/lane distribution

• Number of lanes

• Median information

• Geometrics (grade, curvature, superelevation, cross-slope)

• Roadway terrain

• Shoulder information

• Roadside features

• Safety devices/features (striping, signage, guardrails, etc.)

Pavement File Data

• Original/existing pavement type (pre-treatment)

• Original/existing pavement construction dates

• Pavement materials for original/existing pavement

• Surface treatment strategy type

• Construction dates for treatment strategy

• Construction methods and equipment used for treatment strategy

• Length of treatment strategy section

• Material properties for treatment strategy (includes, but is not limited to, 

binder type/properties, aggregate properties) 

• Pavement condition (rutting, polished surface, etc.)

• Skid Resistance Measurement (SRV and test type)

Climatic Data

• Average temperatures (by month)

• Average rainfall (by month)

• Crash-specific weather (if available) 1

Minimum of 3 years preceding application of the treatment and 3 years following application, as available.

PDO = Property damage only

SRV = Skid resistance value

SUMMARY OF DATA BY STATE

Table 5 and table 6 show the number of treatment sites and/or mi of treatments provided by each 

of the volunteer States for asphalt and concrete pavements, respectively. Note that these are 

overall treatment quantities after eliminating nonviable sites, not broken down by facility type. 

41 
Table 5. Treatment strategies and quantities provided, by State, for asphalt pavements.

State Treatments

Approximate

Mi/Number of Sites

California

• Chip seal (rubberized and conventional)

• Thin HMA overlay (rubberized and conventional)

• OGFC (rubberized and conventional)

• UTBWC

• Microsurfacing

• Slurry seal

• HFS

948 mi

581 mi

404 mi

57 mi

72 mi

134 mi

7 sites

Colorado • HFS 2 sites

Connecticut • UTBWC 3 sites

Kansas • HFS 2 sites

Kentucky • HFS 25 sites

Michigan • HFS 5 sites

Minnesota

• Chip seal

• Thin HMA overlay

• Microsurfacing

274 mi

204 mi

57 mi

Mississippi

• Scrub seal

• Chip seal

• HFS

52 mi

29 sites

1 site

Montana • Chip seal

• HFS

15 sites

2 sites

North Carolina

• Chip seal (single, double)

• Thin HMA overlay

• OGFC

• UTBWC

• Slurry seal

765 mi

3,154 mi

42 mi

68 mi

5 mi

Pennsylvania

• Chip seal (single, double)

• Thin HMA overlay

• UTBWC

• Microsurfacing

570 mi

1 mi

7 mi

159 mi

South Carolina • HFS 6 sites

Tennessee

• Chip seal

• Cape seal

• Thin HMA overlay

• Microsurfacing

• Scrub seal

• UTBWC

• HFS

7 sites

9 sites

29 sites

52 sites

4 sites

2 sites

6 sites

42 
State Treatments

Approximate

Mi/Number of Sites

Wisconsin • Chip seal

• HFS

22 sites

1 site

West Virginia • HFS 24 sites

HMA = Hot mix asphalt

OGFC = Open graded friction course

HFS = High-friction surfacing

UTBWC = Ultrathin Bonded wearing course

Table 6. Treatment strategies and quantities provided, by state, for concrete pavements.

State Treatments

Approximate Mi/ 

Number of Sites

California

• OGFC (rubberized and conventional)

• Diamond grinding

• Grooving

12 mi

85 mi

5 mi

Kansas • HFS 2 sites

Michigan • HFS 4 sites

Minnesota

• Thin HMA overlay

• Microsurfacing

• Diamond grinding

5 mi

37 mi

8 mi

North Carolina • UTBWC

• Diamond Grinding

26 mi

24 mi

Pennsylvania

• Thin HMA overlay

• Microsurfacing

• UTBWC

• Diamond grinding

7 mi

5 mi

13 mi

33 mi

OGFC = Open graded friction course

HFS = High-friction surfacing

HMA = Hot mix asphalt

UTBWC = Ultrathin bonded wearing course

FINAL TREATMENTS AND STATES

Although the team wanted to include all sites and States in the analysis, project timeline and 

resource limitations made it necessary to focus efforts on analyzing treatments in the States that 

were able to provide a variety of treatments, for both concrete and asphalt pavement, and a 

significant quantity of each. Therefore, treatment sites included in the final analysis for 

conventional (i.e., non-HFS) treatments were the sites provided by California, Minnesota, North 

Carolina, and Pennsylvania. For HFS treatments, the sites from roughly half of the States that 

provided them were included in the final analysis. 

Conventional treatment sites provided by States that were not included in the final analysis were 

submitted to FHWA for in-house studies. These included treatment sites provided by 

Connecticut (UTBWC), Mississippi (cape seal), Montana (chip seal), Tennessee (various 

treatments), and Wisconsin (chip seal). 

43 
CONVENTIONAL TREATMENT DATA COLLECTION

This section describes the process followed to collect and query the data required for the study 

and provides summary statistics for these data for each of the four States included in the 

conventional treatment analysis. 

Pennsylvania 

Data for Pennsylvania were provided by the Pennsylvania Department of Transportation 

(PennDOT). These data included geometric, pavement, traffic, and crash data for the entire State. 

The data are linked together using the District Number, County Number, State Route Number, 

Segment Number, and Segment Offset variables.

Geometric Data

Geometric data were obtained from the PennDOT Roadway Management System and included 

the following variables: 

• Access control. 

• Divided/undivided. 

• Divided width. 

• Surface width. 

• Speed limit. 

• Number of lanes. 

• Urban versus rural environment. 

• Surface type. 

• Shoulder type. 

• Shoulder width. 

• Shoulder paved width. 

Note that for access-controlled roadways, the data are directional, that is, each record 

corresponds to only one direction of travel. For non-access-controlled roadways, the data 

encompass both directions of travel. 

PennDOT also provided a list of segments where some change in the route/ segment/offset 

segmentation system had been made between 2003 and 2011. This is significant because data 

from different years are being queried and if the segmentation has changed, then the data cannot 

be matched correctly. This was evident for some locations that appeared to have very large 

changes in traffic volume from 1 year to the next. Segments with a change were not used for 

analysis.

Pavement Data

PennDOT provided a file with pavement history statewide. For each section of roadway, data on 

up to 10 layers of pavement is included. The data indicate the year of resurfacing and the surface 

type.

44 
Traffic Data

Traffic data in the form of AADT were obtained from PennDOT from 2003 to 2010. The percent 

of trucks in the traffic mix was also provided. Because the study period extends to 2011 and 

these counts were not available, it was decided to apply the 2010 AADTs to 2011 as well. 

Crash Data 

The PennDOT Crash Database is maintained by the Bureau of Highway Safety and Traffic 

Engineering’s Crash Information Systems & Analysis Division. Data from 2003 to 2011 were 

provided. The compiled crash data contain many variables related to the location, time, and 

characteristics of each crash. The following notes relate to the exclusion or inclusion of crash 

data and definition of crash types: 

• Included only non-intersection-related mainline crashes (if intersect_type = “midblock”). 

• Excluded crashes where harm_event = “hit deer” or “hit other animal”. 

• Excluded crashes where road condition = “snow”, “slush”, “ice”, or “ice patches”. 

• Defined injury crashes as those where fatal_count or tot_inj_count is greater than 0. 

• Defined ROR crashes as those where relation_to_road = “Outside trafficway”. 

• Defined wet crashes as those where road_condition = “wet”. 

Construction Data

PennDOT does not maintain a central file with construction information. For the treatment sites 

used the PennDOT staff did not indicate any other construction had taken place within the study 

period. It is possible some other works may have occurred; however, with the large mileage of 

treatment and reference sites available, if some works did occur at some locations, the impact on 

the results of the analysis would be negligible.

Treatment Sites

Sites on the initial list of treatment sites eligible for the study were subsequently removed for two 

reasons.

• Sites with a missing volume between 2003 and 2011 were removed. 

• Sites with a change in the segmentation between 2003 and 2011 were removed. 

Reference Sites

Reference sites were identified by first identifying road segments in the same county and with 

the same route number as the treated sites. Then the various geometric and traffic variables were 

compared to ensure that the range of values for these variables were consistent. The pavement 

data were linked to these data, and any segments that were resurfaced between 2003 and 2011 

were removed.

45 
Similar to the treated sites, potential reference sites were excluded if any year had a missing 

traffic volume count or the segmentation had changed between 2003 and 2011. 

After querying the data, it was found that some sites have highly variable AADTs between years. 

After consultation with PennDOT, no satisfactory explanation was found for such systemic 

variation. To avoid biases in the SPFs to be developed due to incorrect traffic volume data it was 

decided to eliminate any segment where the AADT for any single year deviated from the 

segment average for the entire study period by 25 percent or more.

Table 7 through table 9 provide summary statistics for the treatment and reference site data in 

Pennsylvania.

46 
Table 7. Summary statistics for Pennsylvania treatment site geometry. 

Treatment Type

Site 

Total 

Length 

(mi)

Pavement Type

(mi) Width (ft)

Lanes

No. of 

(mi) Area Type (mi)

Divided/Undivide

(mi) 

Chip Seal TwoLane

570.00

AC—346.78

CS—217.78

Micro—5.42

SS—0.02

Min—16

Max—44

Mean—21.30

2—568.82

3—1.49

Rural—418.06

Urban—151.95

Divided—570.00

Undivided—0.00

Grinding

Diamond 

TwoLane

0.47 PCC—0.47

Min—24

Max—24

Mean—24

2—0.47 Rural—0.00

Urban—0.47

Divided—0.00

Undivided—0.47

Freeway 32.29 PCC—32.29

Min—24

Max—36

Mean—24.77

2—32.19

3—0.09

Rural—23.37

Urban—8.92

Divided—32.29

Undivided—0.00

Microsurfacing

TwoLane

152.26 AC—152.26

Min—18

Max—56

Mean—27.82

2—143.00

3—9.27

Rural—128.53

Urban - 23.74

Divided—0.00

Undivided—152.26

Freeway 6.47 AC—6.47

Min—24

Max—52

Mean—25.71

2—6.47 Rural—2.04

Urban—4.43

Divided—6.47

Undivided—0.00

Microsurfacing 

on PCC Freeway 5.47 PCC—5.47

Min—24

Max—36

Mean—25.71

2—5.47 Rural—1.56

Urban—3.91

Divided—5.47

Undivided—0.00

Thin HMA Freeway 0.50 AC—0.50

Min—24

Max—24

Mean—24

2—0.50 Rural—0.50

Urban—0.00

Divided—0.50

Undivided—0.00

Thin HMA on 

PCC Freeway 6.95 PCC—6.95

Min—24

Max—24

Mean—24

2—6.95 Rural—0.00

Urban—6.95

Divided—6.95

Undivided—0.00

UTBWC TwoLane

7.49 AC—7.49

Min—21

Max—48

Mean—26.28

2—7.21

3—0.28

Rural—6.77

Urban—0.72

Divided—0.00

Undivided—7.49
Treatment

Site 

Type

Total 

Length 

(mi)

Pavement Type

(mi) Width (ft)

No. of 

Lanes

(mi) Area Type (mi)

Divided/Undivide

(mi) 

UTBWC on 

PCC

TwoLane

4.11 PCC—4.11

Min—24

Max—24

Mean—24

2—9.73 Rural—0.71

Urban—3.40

Divided—0.00

Undivided—4.11

Freeway 9.73 PCC—9.73

Min—24

Max—24

Mean—24

2—4.11 Rural—2.73

Urban—6.99

Divided—9.73

Undivided—0.00

Avgshldwid = Average of left and right shoulder width

AC = Asphalt

CS = Chip Seal

Micro = Microsurfacing

SS = Slurry Seal 

Max = Maximum

Min = Minimum

PCC = Portland cement concrete

HMA = Hot mix asphalt

UTBWC = Ultrathin bonded wearing course

48 
Table 8. Summary statistics for Pennsylvania treatment site AADT and crashes. 

ELCSI Treatment

Type Type

Site

AADTBefore

AADT After

Years 

Before

Years After

totrateb

totratea

injrateb

injratea

rorrateb

rorratea

wetrateb

Chip Seal TwoLane Min 239 233 3.00 3.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

Chip Seal TwoLane Max 19,533 17,689 5.00 5.00 60.82 117.58 60.82 88.18 37.79 28.91 32.26 

Chip Seal TwoLane Mean 2,618 2,534 4.29 3.71 0.69 0.75 0.40 0.38 0.14 0.14 0.16 

Diamond Grinding 

on PCC Freeway Min 10,240 8,844 3.00 3.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

Diamond Grinding 

on PCC Freeway Max 109,316 105,526 5.00 5.00 18.24 51.87 12.16 51.87 3.04 0.67 6.08 

Diamond Grinding 

on PCC Freeway Mean 41,544 38,322 4.81 3.19 1.27 1.69 0.71 0.99 0.12 0.01 0.31 

Diamond Grinding 

on PCC

TwoLane Min 4,568 4,444 4.00 4.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

Diamond Grinding 

on PCC

TwoLane Max 4,568 4,444 4.00 4.00 0.97 0.00 0.00 0.00 0.97 0.00 0.00 

Diamond Grinding 

on PCC

TwoLane Mean 4,568 4,444 4.00 4.00 0.48 0.00 0.00 0.00 0.48 0.00 0.00 

Microsurfacing Freeway Min 11,328 11,683 3.00 3.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

Microsurfacing Freeway Max 74,759 69,328 5.00 5.00 18.56 15.22 10.74 11.68 1.36 2.91 5.37 

Microsurfacing Freeway Mean 30,530 27,060 4.64 3.36 1.96 2.11 0.75 1.18 0.06 0.07 0.23 

Microsurfacing TwoLane Min 679 1,056 3.00 3.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

Microsurfacing TwoLane Max 18,838 17,519 5.00 5.00 62.99 70.55 22.92 70.55 32.09 23.52 43.29 

Microsurfacing TwoLane Mean 5,752 5,633 3.93 4.07 1.38 1.51 0.72 0.83 0.22 0.18 0.48 
ELCSI Treatment

Type

Site

Type

AADT

efore B

fter A AADT

Years 

Before

Years After

totrateb

totratea

injrateb

injratea

rorrateb

rorratea

wetrateb

Microsurfacing on 

PCC Freeway Min 10,5478,702 3.00 3.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

Microsurfacing on 

PCC Freeway Max 74,759 74,332 5.00 5.00 13.15 14.06 5.82 5.27 2.11 1.76 8.76 

Microsurfacing on 

PCC Freeway Mean 52,465 49,886 4.33 3.67 3.55 3.67 1.82 1.48 0.33 0.11 1.86 

Thin HMA Freeway Min 31

,394 29,257 4.00 4.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

Thin HMA Freeway Max 31

,394 29,257 4.00 4.00 0.00 0.62 0.00 0.62 0.00 0.00 0.00 

Thin HMA Freeway Mean 31

,394 29,257 4.00 4.00 0.00 0.15 0.00 0.15 0.00 0.00 0.00 

Thin HMAon PCC Freeway Min 26

,012 27,145 4.00 4.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

Thin HMAon PCC Freeway Max 26

,012 27,145 4.00 4.00 1.67 9.96 1.66 3.32 1.66 3.32 1.67 

ThinHMAon PCC Freeway Mean 26

,012 27,145 4.00 4.00 0.49 1.16 0.22 0.39 0.08 0.20 0.13 

UTBWC Two

-

Lane Min2,5081,936 3.00 4.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

UTBWC Two

-

Lane Max 15,536 14,823 4.00 5.00 63.75 38.34 47.81 29.39 19.17 15.94 47.81 

UTBWC Two

-

Lane Mean 12,134 12,036 3.98 4.02 6.40 3.98 2.80 1.86 1.09 0.37 4.35 

50 
ELCSI Treatment

Type Type

Site

AADTBefore

AADT After

Years 

Before

Years After

totrateb

totratea

injrateb

injratea

rorrateb

rorratea

wetrateb

UTBWC on PCC Freeway Min 21,043 9,958 3.00 3.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

UTBWC on PCC Freeway Max 50,811 39,889 5.00 5.00 8.02 4.85 3.16 4.85 0.39 0.52 4.01 

UTBWC on PCC Freeway Mean 40,657 29,144 3.49 4.51 0.61 0.47 0.22 0.24 0.01 0.01 0.16 

UTBWC on PCC TwoLane Min 4,392 3,645 5.00 3.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

UTBWC on PCC TwoLane Max 8,218 9,093 5.00 3.00 7.64 3.34 3.82 1.67 0.77 0.49 0.30 

UTBWC on PCC TwoLane Mean 6,067 5,898 5.00 3.00 0.79 0.47 0.50 0.21 0.11 0.03 0.04 

ELCSI = Evaluation of Low-Cost Safety Improvements

totrateb = total crash rate per mi

AADT = Average Annual Daily Traffic

-yr in before period

totratea = total crash rate per mi-yr in after period

injrate, wetrate, rorrate, wetrorrate = injury, wet-road, ROR, and wet-road ROR crash rates per mi-year, respectively

Max = maximum

Min = minimum

UTBWC = Ultrathin bonded wearing course

HMA = Hot mix asphalt

PCC = Portland cement concrete
Table 9. Summary statistics for Pennsylvania reference sites.

Site 

Type

Total 

Length 

(mi)

Pavement 

Type (mi)

Surface 

Width (ft)

No. of 

Lanes (mi)

Area 

Type (mi)

Divided/Undivided 

(mi)

Avgshldwid 

(ft) AADT

Years

totrate 

TwoLane 7,064.58

AC—

7,002.91

PCC—

61.67

Max—9

Min—82

Mean—

24.5

2—6,957.77

3—106.81

Rural—

4,919.88

Urban—

2,144.70

Divided—0.00

Undivided—

7,064.58

Max—0 

Min—15

Mean—3.19

201

31,715

5,717

9

9

9

1.52 0

Freeway 563.97

AC—

298.53

PCC—

265.45

Max—19

Min—67

Mean—

25.86

2—522.27

3—41.70

Rural—

338.49

Urban—

225.48

Divided—563.97

Undivided—0.00

Max—0 

Min—12

Mean—6.91

1819

174,962

34,788

9

9

9

2.42 

Avgshldwid = Average of left and right shoulder width

AADT = Average Annual Daily Traffic

totrate = total crash rate per mi-year

injrate = injury crash rate per mi-year

wetrate = wet-road crash rate per mi-year;

rorrate = ROR crash rate per mi-year

wetrorrate = wet-road ROR crash rate per mi-year)

AC = Asphalt concrete

PCC = Portland cement concrete

HMA = Hot mix asphalt

52 
North Carolina 

Data for North Carolina were provided by the North Carolina Department of Transportation 

(NCDOT) and the Highway Safety Information System (HSIS). The data provided by NCDOT 

included a list of potential treatment sites and construction data, including paving projects. The 

data provided by HSIS included geometric, traffic, and crash data. The data are linked together 

using the County Number, Route Number, and Milepost variables.

Geometric Data

Geometric data were obtained from HSIS and included the following variables: 

• Urban versus rural environment.

• Median width.

• Shoulder type.

• Surface width.

• Terrain.

• Median type.

• Speed limit.

• Surface type.

• Number of lanes.

• Roadway class.

• Shoulder width.

• Paved shoulder width.

Pavement Data 

NCDOT indicated which of the treated site segments were asphalt or concrete pavements and 

provided pavement project history files for helping to verify the underlying pavement type. For 

the reference sites, the surface type variable was relied on as the existing pavement type.

Traffic Data

Traffic data in the form of AADT were obtained from HSIS from 2000 to 2010. The percent of 

trucks in the traffic mix was also provided. 

After querying the data, it was found that some sites experience very high variability in traffic 

counts from year to year. After discussing with HSIS staff, it was concluded that this may be 

owing in large part to re-mileposting of the roadway segments from year to year. By comparing 

the roadway characteristics of a sample of these sites, it was found that the functional class often 

changed from year to year. In an attempt to eliminate as many of these re-mileposted locations as 

possible, the team removed any segment from the data for which the functional class or number 

of lanes changed from 2000 to 2010. 

There was an additional concern with the 2009 AADTs in that many of these counts seemed out 

of line with the preceding and following years’ AADT count. In particular, on divided roadways, 

the 2009 AADT appears to be roughly half of that reported in either the preceding or following 

53 
year. The suspicion is that in 2009, the AADT for one direction may be what is reported. No 

resolution was found for the data, and it was decided to substitute the 2010 AADTs for 2009. 

Crash Data 

The crash data were provided by HSIS for 2000 to 2010. The compiled crash data contain many 

variables related to the location, time, and characteristics of each crash. The following notes 

relate to the exclusion or inclusion of crash data and definition of crash types: 

• Exclude intersection-related mainline crashes (if loctype is any type of intersection 

crash). 

• Excluded crashes where acctype = “animal”. 

• Excluded crashes where rdsurf = “snow”, “slush”, or “ice”. 

• Defined injury crashes as those where severity = fatal, A class, B class, or C class 

injury. 

• Defined ROR crashes as those where acctype = “ran-off-road right”, “ran-off-road 

left”, or “ran-off-road straight”. 

• Defined wet crashes as those where rdsurf=“wet”. 

Construction Data

Construction history data was provided by NCDOT for 2000 to 2010. This list includes 

resurfacing and other projects, as well as concrete pavement ratings for verifying which sites 

were concrete. 

Treatment Sites

Sites on the initial list of treatment sites eligible for the study were removed if they had an 

AADT under 500. As was discussed under Traffic Data, some segments are believed to have 

been re-mileposted but no record is available indicating the old and new mileposts for the same 

section of road. In an attempt to eliminate as many of these re-mileposted locations as possible, 

any segment for which the functional class or number of lanes changed from 2000 to 2010 was 

removed from the data.

Reference Sites

Reference sites were identified by first identifying road segments in the same county and with 

the same route number as the treated sites. Then the various geometric and traffic variables were 

compared to ensure that the range of values for these variables were consistent. The pavement 

data and construction data were linked to these data, and any segments that were resurfaced or 

had other construction works between 2000 and 2010 were removed. 

Similar to the treated sites, potential reference sites were excluded if the functional class or 

number of lanes changed from 2000 to 2010. 

54 
Similar to the Pennsylvania data, it was found that some sites have highly variable AADTs 

between years. To avoid biases in the safety performance functions (SPF) to be developed due to 

incorrect traffic volume data, it was decided to eliminate any segment for which the AADT for 

any single year deviated from the segment average for the entire study period by 25 percent or 

more.

Table 10 through table 12 provide summary statistics for the treatment and reference site data in 

North Carolina. 

55 
Table 10. Summary statistics for North Carolina treatment site geometry.

Treatment Site Type

Total 

Length

(mi)

Pavement 

Type (mi)

Surface 

Width (ft)

No. of 

Lanes (mi) Area Type (mi) Divided/Undivided 

Chip Seal

Two-Lane 763.74 AC—763.74

PCC—0.00

Min—14

Max—52

Mean—20.04

2—763.74 Rural—737.15

Urban—26.58

Divided—1.52

Undivided—762.22

Multilane 1.47 AC—1.47

PCC—0.00 

Min—24

Max—42

Mean—25.29

4—1.47 Rural—0.12

Urban—1.35

Divided—1.47

Undivided—0.00

Diamond 

Grinding Freeway 24.09 AC—0.00

PCC—24.09 

Min—24

Max—40

Mean—24.29

4—24.09 Rural—13.11

Urban—10.98

Divided—24.09

Undivided—0.00

Microsurfacing

Two-Lane 31.91 AC—31.91

PCC—0.00

Min—18

Max—52

Mean—25.99

2—31.91 Rural—31.89

Urban—0.02

Divided—0.00

Undivided—31.91

Multilane 4.14 AC—4.14

PCC—0.00

Min—26

Max—36

Mean—29.33

3—1.13

4—3.01

Rural—4.14

Urban—0.00

Divided—0.00

Undivided—4.14

Freeway 3.09 AC—3.09

PCC—0.00 

Min—24

Max—24

Mean—24

4—3.09 Rural—3.09

Urban—0.00

Divided—3.09

Undivided—0.00

OGFC

Multilane 0.95 AC—0.95

PCC—0.00 

Min—24

Max—60

Mean—54

4—0.95 Rural—0.95

Urban—0.00

Divided—0.09

Undivided—0.86

Freeway 40.96 AC—40.96

PCC—0.00 

Min—24

Max—54

Mean—32.69

4—34.47

6—5.88

8—0.61

Rural—30.89

Urban—10.07

Divided—40.96

Undivided—0.00

Slurry Seal Two-Lane 5.08 AC—5.08

PCC—0.00 

Min—20

Max—44

Mean—25.32

2—5.08 Rural—5.08

Urban—0.00

Divided—0.00

Undivided—5.08

56 
Treatment Site Type

Total 

Length

(mi) Type 

Pavement 

(mi)

Surface 

Width (ft)

No. of 

Lanes (mi) Area Type (mi) Divided/Undivided 

Thin HMA

Two-Lane 2,865.59 AC—2,865.59

PCC—0.00

Min—10

Max—100

Mean—23.24

2 -2,865.50

Rural—

Urban

2,332.59

—533.00

Divided—8.82

Undivided—2,856.77

Multilane 201.34 AC—201.34

PCC—0.00

Min—12

Max—92

Mean—39.65

3—5.77 

4—183.32

6—12.03

8—0.23

Rural—73.54

Urban—127.81

Divided—136.32

Undivided—65.02

Freeway 87.15 AC—87.15

PCC—0.0.0 

Min—23

Max—50

Mean—31.56

4—60.74 

6—16.78

8—9.64

Rural—41.48

Urban—45.67

Divided—87.15

Undivided—0.00

UTBWC

Two-Lane 19.42 AC—19.42

PCC—0.00 

Min—22

Max—35

Mean—23.15

2—19.42 Rural—15.55

Urban—3.87

Divided—0.00

Undivided—19.42

Multilane 4.67 AC—4.67

PCC—0.00 

Min—24

Max—66

Mean—47.22

4—1.18

6—3.49 

Rural—0.00

Urban—4.67

Divided—1.61

Undivided—3.06

Freeway 44.36 AC—44.36

PCC—0.00 

Min—22

Max—70

Mean—33.13

4 –33.00 

6—6.72

8—4.65

Rural—17.82

Urban—26.54

Divided—44.36

Undivided—0.00

UTBWC on 

PCC

Two-Lane 1.56 AC—0.00

PCC—1.56

Min—22

Max—22

Mean—22

2—1.56 Rural—0.00

Urban—1.56

Divided—0.00

Undivided—1.56

Freeway 24.29 AC—0.00

PCC—24.29 

Min—24

Max—70

Mean—36.69

4—15.54 

6—5.65

8—3.10

Rural—14.50

Urban—9.79

Divided—24.29

Undivided—0.00

Avgshldwid = Average of left and right shoulder width

AC = Asphalt

OGFC 

Min = Minimum

Max = Maximum

PCC = Portland cement concrete

= Open graded friction course

UTBWC = Ultrathin 

HMA = Hot mix asphalt

bonded wearing course
Table 11. Summary statistics for North Carolina treatment site AADT and crashes.

ELCSI

Treatment

Type Site Type

AADT Before

AADT After

Years 

Before

Years After

totrateb

totratea

injrateb

injratea

rorrateb

rorratea

Chip Seal Multilane Min 10,995 11,500 6.00 3.00 0.00 0.00 0.00 0.00 0.00 0.00 0.

Chip Seal Multilane Max 35,173 33,333 7.00 4.00 127.41 125.00 46.33 41.67 3.57 9.26 19

Chip Seal Multilane Mean 26,689 25,797 6.93 3.07 20.41 32.02 6.91 10.18 0.54 0.81 3.

Chip Seal Two-Lane Min 500 500 2.00 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0.

Chip Seal Two-Lane Max 24,368 32,000 8.00 8.00 41.85 100.00 31.25 50.00 31.25 7.58 17

Chip Seal Two-Lane Mean 1,980 1,947 5.74 4.26 0.83 0.97 0.41 0.40 0.10 0.04 0.

Diamond 

Grinding on 

PCC

Freeway Min 31,889 33,000 9.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.

Diamond 

Grinding on 

PCC

Freeway Max 53,889 74,000 9.00 1.00 411.11 100.00 77.78 19.23 11.11 19.23 55

Diamond 

Grinding on 

PCC

Freeway Mean 42,701 44,581 9.00 1.00 17.19 9.17 4.31 1.79 0.63 0.56 2.

Microsurfacing Freeway Min 20,206 23,676 7.00 2.00 1.14 0.00 0.00 0.00 0.00 0.00 0.

Microsurfacing Freeway Max 30,314 32,000 8.00 3.00 95.24 15.15 12.99 6.33 10.42 4.44 10

Microsurfacing Freeway Mean 26,779 27,168 7.40 2.60 21.71 5.78 3.04 2.00 2.91 0.44 2.

Microsurfacing Multilane Min 4,242 4,500 8.00 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0.

Microsurfacing Multilane Max 8,032 8,200 8.00 2.00 1.48 1.69 0.62 0.93 0.00 0.00 0.

Microsurfacing Multilane Mean 6,708 6,967 8.00 2.00 0.76 0.60 0.20 0.28 0.00 0.00 0.

Microsurfacing Two-Lane Min 628 620 7.00 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0.

Microsurfacing Two-Lane Max 9,539 12,667 8.00 3.00 8.24 250.00 4.46 10.10 4.76 3.70 6.

Microsurfacing Two-Lane Mean 4,912 4,751 7.30 2.70 1.09 3.18 0.53 0.44 0.20 0.05 0.

OGFC Freeway Min 17,563 17,000 2.00 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0.

58 
ELCSI

Treatment

Type Site Type

AADT Before

AADT After

Years 

Before

Years After

totrateb

totratea

injrateb

injratea

rorrateb

rorratea

OGFC Freeway Max 158,656 160,333 8.00 8.00 465.61 345.68 116.40 98.77 25.97 30.30 125

OGFC Freeway Mean 66,006 69,228 5.95 4.05 39.74 40.91 12.16 10.34 2.21 1.20 9.

OGFC Multilane Min 14,961 13,313 2.00 3.00 0.00 0.00 0.00 0.00 0.00 0.00 0.

OGFC Multilane Max 31,371 29,000 7.00 8.00 7.94 11.11 4.76 3.79 0.00 0.28 1.

OGFC Multilane Mean 17,696 16,094 2.83 7.17 1.70 3.32 0.98 1.14 0.00 0.05 0.

Slurry Seal Two-Lane Min 1,396 1,500 8.00 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0.

Slurry Seal Two-Lane Max 3,771 3,400 8.00 2.00 9.62 12.50 9.62 0.89 0.00 0.00 0.

Slurry Seal Two-Lane Mean 2,370 2,536 8.00 2.00 1.04 0.81 0.82 0.08 0.00 0.00 0.

Thin HMA Freeway Min 6,270 6,200 2.00 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0.

Thin HMA Freeway Max 117,500 124,500 8.00 8.00 1083.33 958.33 333.33 208.33 100.00 68.63 421

Thin HMA Freeway Mean 52,143 55,330 5.44 4.56 27.75 29.25 9.09 8.19 2.56 2.80 6.

Thin HMA Multilane Min 984 2,200 2.00 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0.

Thin HMA Multilane Max 61,962 63,000 8.00 8.00 1657.14 1500.00 557.14 333.33 333.33 74.07 328

Thin HMA Multilane Mean 19,323 19,588 5.04 4.96 18.15 20.70 6.26 6.03 0.92 0.96 3.

Thin HMA Two-Lane Min 500 520 2.00 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0.

Thin HMA Two-Lane Max 27,767 31,354 8.00 8.00 500.00 388.89 100.00 125.00 41.67 83.33 66

Thin HMA Two-Lane Mean 4,927 5,058 5.14 4.86 2.25 3.03 0.90 1.08 0.15 0.22 0.

UTBWC Freeway Min 10,325 11,000 2.00 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0.

UTBWC Freeway Max 153,167 162,000 8.00 8.00 1033.33 2150.00 333.33 400.00 83.33 100.00 150

UTBWC Freeway Mean 69,054 74,410 6.65 3.35 43.51 63.15 13.13 13.06 2.46 3.53 7.

UTBWC Multilane Min 4,932 12,000 4.00 3.00 0.00 0.00 0.00 0.00 0.00 0.00 0.

UTBWC Multilane Max 41,833 51,667 7.00 6.00 155.88 94.55 61.76 35.26 10.00 7.49 26

UTBWC Multilane Mean 22,717 23,799 6.16 3.84 38.07 25.72 14.94 9.72 1.63 0.66 5.

UTBWC Two-Lane Min 2,704 2,000 6.00 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0.
ELCSI

Treatment

Type Site Type

AADT Before

AADT After

Years 

Before

Years After

totrateb

totratea

injrateb

injratea

rorrateb

rorratea

UTBWC Two-Lane Max 11,216 7,873 8.00 4.00 47.62 142.86 23.81 71.43 1.85 1.23 23

UTBWC Two-Lane Mean 5,730 4,536 7.05 2.95 3.10 3.47 1.80 1.80 0.06 0.02 0.

UTBWC on 

PCC Freeway Min 22,625 23,000 8.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.

UTBWC on 

PCC Freeway Max 107,250 113,000 9.00 2.00 701.09 2150.00 138.59 400.00 25.00 100.00 111

UTBWC on 

PCC Freeway Mean 66,031 67,988 8.27 1.73 37.67 72.15 9.19 12.58 2.87 5.54 6.

UTBWC on 

PCC Two-Lane Min 6,420 5,325 6.00 4.00 0.00 0.00 0.00 0.00 0.00 0.00 0.

UTBWC on 

PCC Two-Lane Max 6,938 8,000 6.00 4.00 5.85 41.67 2.92 41.67 0.00 0.00 2.

UTBWC on 

PCC Two-Lane Mean 6,530 6,582 6.00 4.00 1.19 4.08 0.31 3.63 0.00 0.00 0.

ELCSI = Evaluation of Low-Cost Safety Improvements

AADT = Average annual daily traffic

totrate = total crash rate per mi-year

injrate = injury crash rate per mi-year

wetrate = wet-road crash rate per mi-year;

rorrate = ROR crash rate per mi-year

wetrorrate = wet-road ROR crash rate per mi-year) 

totrateb = total crash rate per mi-yr in before period

totratea = total crash rate per mi-yr in after period

injrate, wetrate, rorrate, wetrorrate = injury, wet-road, ROR, and wet-road ROR crash rates per mi-year, respectively

MAX = maximum

MIN = minimum

PCC = Portland cement concrete

OGFC = Open graded friction course

HMA = Hot mix asphalt

UTBWC = Ultrathin bonded wearing course

60 
Table 12. Summary statistics for North Carolina reference sites.

T

Site 

ype

Total 

Length

(mi) T

Pavement

ype (mi)

Surface 

Width (ft)

Lanes

No. of 

(mi)

Area 

Type (mi)

Divided/

Undivided 

(mi)

Avgshldwid 

(ft) AADT

Years

totrate

TwoLane 3,773.27

AC—

PCC

3,764.57

—8.70

Min—12

Max—131

Min—24.48

2—3,773.27

Rural—

Urban

2,909.00

—

864.26

Divided—

—

Undivided

0.00

3,773.27

Min—0 

Max—38.5

Min—5.53 5,564

58,013

508

11

11

11

1.56 

Multilane 384.04

AC—

PCC

382.98

 –1.06

Min—12

Max—125

Min—.35

3—8.44

4—354.03

6—6.80

8—14.65

12—0.12

Rural—

Urban

137.79

—

246.25 

Divided—

—

Undivided

259.57

124.47

Min—0

Max—30

Min—3.17 14,717

58,013

974

11

11

11

9.85 

Freeway 6.80 AC—5.16

PCC—1.64

Min—24

Max—120

Min—53.14

2—0.46

4—1.97

6—0.07

8—3.92

16—0.38 

Rural—

Urban

4.00

—

2.80

Divided—

—

Undivided

6.80

0.00

Min—0

Max—21.5

Min—8.39 38,706

105,400

2,713

11

11

11

11.71 

Avgshldwid = Average of left and right shoulder width

totrate = total crash rate per mi

AADT = Average annual daily traffic

-year

injrate = injury crash rate per mi-year

wetrate = wet-road crash rate per mi-year;

rorrate = ROR crash rate per mi-year

wetrorrate = wet-road ROR crash rate per mi-year)

M

PCC = Portland cement concrete

AC = Asphalt concrete

ax = maximum

Min = minimum
California

Data for California were provided by the California Department of Transportation (Caltrans) and 

HSIS. The data provided by Caltrans included a list of potential treatment sites based on 

pavement maintenance and preservation projects from 2004 through 2007. The data provided by 

HSIS included geometric, traffic, and crash data. The data are linked together using the District 

Number, County Number, Route Number, and milepost variables. 

Geometric Data

Geometric data were obtained from HSIS and included the following variables: 

• Number of lanes.

• Median type.

• Median width.

• Access control.

• Terrain.

• Design speed.

• Rural versus urban environment.

• Surface type.

• Shoulder width.

• Paved width.

• Surface width.

• Lane width.

• Divided versus undivided.

• Roadway class.

Traffic Data

Traffic data in the form of AADT were obtained from HSIS from 2000 to 2009. 

Crash Data 

The crash data were provided by HSIS for 2000 to 2009. The compiled crash data contain many 

variables related to the location, time, and characteristics of each crash. The following notes 

relate to the exclusion or inclusion of crash data and definition of crash types: 

• Exclude intersection-related mainline crashes (if int_rmp = “ramp intersection”,

“mid-ramp”, “ramp entry”, “ramp area/intersection street”, “in intersection”, or

“outside intersection (non-state route) within 250 ft.”).

• Excluded crashes where veh_invl = “animal”.

• Excluded crashes where rdsurf = “snowy, icy”.

• Injury crashes defined as those where severity = fatal, A class, B class, or C class

injury.

62 
• ROR crashes defined as those where miscact1 for any involved vehicle = “ran-offroad”.

• Wet crashes defined as those where rdsurf=“wet”.

Construction Data

Construction history data were provided by Caltrans for 2000 to 2009 as a statewide list of 

capital improvement projects. This list includes resurfacing and other projects to verify that no 

other construction had been completed at the treatment site locations. 

Treatment Sites

Sites on the initial list of treatment sites eligible for the study were removed if they underwent 

other significant construction during the study period. Other sites were moved when the roadway 

was a divided road but the treatment was only applied in one direction. In an attempt to eliminate 

any re-mileposted locations, those segments for which the post mile prefix variable “psmilprf” 

changed during the study period were removed. The “psmilprf” variable indicates that a segment 

has been reposted, realigned, or overlaps with another segment. 

Reference Sites

Reference sites were identified by first identifying road segments in the same district and with 

the same route number as the treated sites. Then the various geometric and traffic variables were 

compared to ensure that the range of values for these variables were consistent. The construction 

data were linked to these data, and any segments that were resurfaced or had other construction 

works between 2000 and 2009 were removed. Similar to the treated sites, potential reference 

sites were excluded if the post mile prefix variable “psmilprf” changed during the study period. 

Table 13 through table 15 provide summary statistics for the treatment and reference site data in 

California.

63 
Table 13. Summary statistics for California treatment site geometry.

Treatment Site Type

Total 

Length

(mi)

Pavement 

Type (mi)

Surface 

Width (ft)

No. of Lanes

(mi) Area Type (mi) Divided/Undiv

(mi) 

Chip Seal

Two-Lane 863.15 AC—863.15

PCC –0.00

Min—12

Max—50

Mean—23.91

2—863.15 Rural—849.05

Urban—14.10

Divided—11.16

Undivided—851

Multilane 70.33 AC—70.33

PCC—0.00

Min—12

Max—60

Mean—30.17

3—10.92 

4—59.29

5—0.12

Rural—67.45

Urban—2.88

Divided—55.28

Undivided—15.

Freeway 14.77 AC—14.77

PCC—0.00

Min—24

Max—39

Mean—26.22

4—14.77 Rural—12.86

Urban—1.91

Divided—14.77

Undivided—0.0

Diamond 

Grinding

Two-Lane 0.64 AC—0.00

PCC—0.64

Min—24

Max—26

Mean—25

2—0.64 Rural—0.00

Urban—0.64

Divided—0.12

Undivided—0.5

Multilane 8.07 AC—0.00 

PCC—8.07

Min—20

Max—36

Mean—26.15

4—7.01

6—1.06

Rural—6.15

Urban—1.92

Divided—8.07

Undivided—0.0

Freeway 76.26 AC—0.00 

PCC—76.26

Min—24

Max—84

Mean—46.03

4—4.54

5—0.34

6—28.44

>6—42.94

Rural—12.68

Urban—63.58

Divided—76.26

Undivided—0.0

Grooved Freeway 5.00 AC—0.00

PCC—5.00

Min—24

Max—24

Mean—24

4—5.00 Rural—5.00

Urban—0.00

Divided—5.00

Undivided—0.0

Microsurfacing

Two-Lane 45.58 AC—45.58

PCC—0.00

Min—12

Max—42

Mean—24.47

2—45.58 Rural—44.23

Urban—1.35

Divided—0.90

Undivided—44.

Multilane 23.45 AC—23.45

PCC—0.00

Min—12

Max—48

Mean—26.20

3—0.45 

4—22.53

5—0.33

6—0.15

Rural—15.94

Urban—7.52

Divided—23.00

Undivided—0.4

Freeway 3.27 AC—3.27 

PCC—0.00

Min—24

Max—48

28.47

4—3.27 Rural—1.06 

Urban—2.21

Divided—3.27

Undivided—0.0

OGFC Two-Lane 232.00 AC—232.00

PCC—0.00

Min—12

Max—40

Mean—23.33

2—232.00 Rural—208.65

Urban—23.35

Divided—7.32

Undivided—224

64 
Treatment Site 

Type

Total 

Length

(mi) Type 

Pavement 

(mi)

Surface 

Width (ft)

No. of Lanes

(mi) Area 

Type (mi) Divided/Undiv

(mi) 

Multilane 59.66 AC

—59.66

PCC—0.00

Min—12

Max—60

Mean—28.30

3—1.75 

4—54.79

5—2.57 6—0.56

Rural—16.60

Urban—43.07

Divided—51.81

Undivided—7.8

Freeway 124.37 AC

—112.23

PCC –12.14

Min—20

Max—72

Mean—35.72

2—1.36

3—0.10

4—85.83

5—2.10

6—7.63

>6—15.21

Rural—59.69

Urban—52.54

Divided—124.3

Undivided—0.0

Slurry Seal

Two-Lane 100.24 AC

—100.24

PCC—0.00

Min—12

Max—56

Mean—24.35

2 - 100.24 Rural

—93.70

Urban—6.54

Divided—0.11

Undivided—100

Multilane 15.45 AC

—15.45 

PCC—0.00

Min—12

Max—64

Mean—34.48

3—3.25

4—12.20

Rural—9.17

Urban—6.29

Divided—8.56

Undivided—6.9

Freeway 18.56 AC

—18.56

PCC—0.00

Min—24

Max—39

Mean—25.01

2—0.20 

4—17.99 6—0.37

Rural—12.00

Urban—6.56

Divided—18.56

Undivided—0.0

Thin HMA

Two-Lane 347.63 AC

—347.63

PCC—0.00

Min—11

Max—84

Mean—23.79

2—347.63 Rural

—321.85

Urban—25.78

Divided—8.38

Undivided—339

Multilane 71.87 AC

—71.87

PCC—0.00

Min—12

Max—64

Mean—30.52

3—14.29

4—45.29

5—1.02

6—8.31

>6—2.96

Rural—29.80

Urban—42.07

Divided—50.98

Undivided—20.

Freeway 164.06 AC

—161.25

PCC—2.81

Min—24

Max—105

Mean—45.12

3—0.51

4—75.78

5—25.18

6—18.80

>6—43.79

Rural—83.01

Urban—81.05

Divided—164.0

Undivided—0.0
Treatment Site Type

Total 

Length

(mi)

Pavement 

Type (mi)

Surface 

Width (ft)

No. of Lanes

(mi) Area Type (mi) Divided/Undiv

(mi) 

UTBWC

2 Lane 10.31 AC—10.31

PCC—0.00

Min—22

Max—24

Mean—23.78

2—10.31 Rural—6.29

Urban—4.03

Divided—0.00

Undivided—10.

Multilane 16.37 AC—16.37 

PCC—0.00

Min—12

Max—60

Mean—27.31

3—2.17 

4—13.24

6—0.96

Rural—2.66

Urban—13.71

Divided—13.86

Undivided—2.5

Freeway 30.37 AC—30.37 

PCC—0.00

Min—20

Max—70

Mean—29.19

2—0.74

4—24.07

5—1.98

6—2.35

>6—1.23

Rural—22.22

Urban—8.15

Divided—30.37

Undivided—0.0

Avgshldwid = Average of left and right shoulder width

AC = Asphalt

PCC = Portland cement concrete

Max = Maximum 

Min = Minimum

OGFC = Open graded friction course

HMA = Hot mix asphalt

UTBWC = Ultrathin bonded wearing course

66 
Table 14. Summary statistics for California treatment site AADT and crashes. 

ELCSI 

Treatment Type Site Type

AADT Before

AADT After

Years 

Before

Years After

totrateb

totratea

injrateb

injratea

rorrateb

rorratea

Chip Seal Freeway Min 12,743 12,800 5.00 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0

Chip Seal Freeway Max 33,000 35,750 7.00 4.00 47.62 8.06 47.62 3.02 1.88 1.16 2

Chip Seal Freeway Mean 20,703 22,068 6.56 2.44 5.26 1.49 2.96 0.38 0.23 0.20 0

Chip Seal Multilane Min 1,280 1,175 4.00 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0

Chip Seal Multilane Max 22,600 25,075 7.00 5.00 31.61 39.47 20.83 9.62 20.83 39.47 6

Chip Seal Multilane Mean 7,357 7,711 4.73 4.27 2.03 1.97 0.75 0.64 0.58 0.51 0

Chip Seal Two-Lane Min 170 190 4.00 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0

Chip Seal Two-Lane Max 24,792 29,157 7.00 5.00 100.00 250.00 50.00 250.00 50.00 33.33 7

Chip Seal Two-Lane Mean 3,424 3,648 5.35 3.65 1.34 1.14 0.56 0.61 0.40 0.27 0

Diamond Grinding 

on PCC Freeway Min 14,283 12,800 4.00 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0

Diamond Grinding 

on PCC Freeway Max 278,200 273,000 7.00 5.00 530.36 298.70 169.64 118.42 45.45 20.00 8

Diamond Grinding 

on PCC Freeway Mean 138,133 147,383 4.86 4.14 45.33 42.30 15.06 14.16 1.67 0.67 4

Diamond Grinding 

on PCC Multilane Min 11,641 12,500 5.00 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0

Diamond Grinding 

on PCC Multilane Max 63,400 66,500 7.00 4.00 29.65 28.57 10.78 8.06 8.04 14.29 5

Diamond Grinding 

on PCC Multilane Mean 20,481 22,827 6.15 2.85 4.40 4.38 1.94 1.53 0.76 0.74 0

Diamond Grinding 

on PCC Two-Lane Min 38,751 48,000 7.00 2.00 0.00 2.19 0.00 0.00 0.00 0.00 0

Diamond Grinding 

on PCC Two-Lane Max 39,303 48,000 7.00 2.00 4.39 7.81 1.88 4.17 0.31 0.00 1

Diamond Grinding 

on PCC Two-Lane Mean 39,081 48,000 7.00 2.00 1.46 4.72 0.63 1.39 0.10 0.00 0
ELCSI 

Treatment Type Site Type

AADT Before

AADT After

Years 

Before

Years After

totrateb

totratea

injrateb

injratea

rorrateb

rorratea

Grooved on PCC Freeway Min 27,500 28,333 5.00 4.00 0.00 0.00 0.00 0.00 0.00 0.00 0

Grooved on PCC Freeway Max 29,000 28,500 5.00 4.00 46.15 23.94 18.46 9.31 3.19 5.32 6

Grooved on PCC Freeway Mean 27,875 28,458 5.00 4.00 13.44 5.72 3.96 1.86 1.56 1.06 1

Microsurfacing Freeway Min 17,117 18,900 7.00 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0

Microsurfacing Freeway Max 24,588 28,300 7.00 2.00 11.90 23.81 3.23 2.62 2.76 2.65 1

Microsurfacing Freeway Mean 18,527 20,862 7.00 2.00 3.50 4.04 0.92 0.50 0.74 0.30 0

Microsurfacing Multilane Min 4,500 3,825 4.00 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0

Microsurfacing Multilane Max 48,838 51,500 7.00 5.00 46.78 46.78 32.16 40.00 9.43 2.88 5

Microsurfacing Multilane Mean 23,114 25,163 5.08 3.92 5.27 4.49 3.37 2.68 0.45 0.26 0

Microsurfacing Two-Lane Min 2,880 2,425 5.00 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0

Microsurfacing Two-Lane Max 37,452 40,173 7.00 4.00 41.67 77.78 41.67 66.67 13.89 16.67 8

Microsurfacing Two-Lane Mean 7,455 8,164 5.97 3.03 3.61 3.87 2.21 2.51 1.03 1.03 0

OGFC Freeway Min 13,620 13,525 4.00 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0

OGFC Freeway Max 191,750 217,000 7.00 5.00 333.33 500.00 142.86 166.67 28.57 41.67 4

OGFC Freeway Mean 70,918 80,723 5.80 3.20 18.38 19.31 6.48 6.24 2.24 1.28 1

OGFC Multilane Min 2,420 2,338 4.00 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0

OGFC Multilane Max 52,800 63,500 7.00 5.00 100.00 102.27 50.00 37.04 13.89 7.14 5

OGFC Multilane Mean 21,150 21,912 4.95 4.05 8.23 8.01 3.01 2.78 0.66 0.39 1

OGFC Two-Lane Min 324 308 4.00 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0

OGFC Two-Lane Max 37,122 40,870 7.00 5.00 58.27 119.05 25.00 45.45 25.00 11.73 3

OGFC Two-Lane Mean 8,261 8,756 5.44 3.56 3.43 3.65 1.24 1.43 0.72 0.50 0

OGFC on PCC Freeway Min 82,500 79,333 5.00 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0

OGFC on PCC Freeway Max 234,429 228,000 7.00 4.00 276.69 400.00 82.14 300.00 50.00 9.62 6

OGFC on PCC Freeway Mean 165,748 165,411 5.78 3.23 57.23 51.96 20.27 20.65 2.47 0.65 7

Slurry Seal Freeway Min 66,75 6,990 4.00 5.00 0.00 0.00 0.00 0.00 0.00 0.00 0

68 
ELCSI 

Treatment Type Site Type

AADT Before

AADT After

Years 

Before

Years After

totrateb

totratea

injrateb

injratea

rorrateb

rorratea

Slurry Seal Freeway Max 87,750 71,200 4.00 5.00 31.25 28.57 31.25 25.00 31.25 25.00 4

Slurry Seal Freeway Mean 24,661 26,202 4.00 5.00 2.43 3.60 1.36 1.14 1.05 0.97 0

Slurry Seal Multilane Min 2,156 2,650 4.00 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0

Slurry Seal Multilane Max 19,650 27,013 7.00 5.00 58.59 50.00 25.00 24.59 3.38 1.99 7

Slurry Seal Multilane Mean 11,682 12,952 5.03 3.97 5.71 7.16 2.09 2.28 0.23 0.10 0

Slurry Seal Two-Lane Min 321 324 4.00 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0

Slurry Seal Two-Lane Max 27,000 32,067 7.00 5.00 200.00 100.00 100.00 35.29 100.00 5.00 1

Slurry Seal Two-Lane Mean 7,772 8,100 5.29 3.71 4.73 3.64 1.96 1.24 0.85 0.21 0

Thin HMA Freeway Min 11,640 13,175 4.00 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0

Thin HMA Freeway Max 322,000 332,000 7.00 5.00 600.00 642.86 266.67 200.00 45.45 100.00 9

Thin HMA Freeway Mean 130,126 135,859 5.29 3.71 44.76 42.16 11.66 12.27 0.83 0.67 3

Thin HMA Multilane Min 3,075 3,122 4.00 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0

Thin HMA Multilane Max 96,333 120,667 7.00 5.00 500.00 457.14 125.00 228.57 7.94 13.33 3

Thin HMA Multilane Mean 31,308 33,006 5.36 3.64 18.06 19.08 6.68 7.75 0.52 0.44 1

Thin HMA Two-Lane Min 88 190 4.00 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0

Thin HMA Two-Lane Max 48,943 48,875 7.00 5.00 159.09 78.95 41.67 55.56 8.57 14.71 4

Thin HMA Two-Lane Mean 7,142 7,774 5.27 3.73 3.76 3.82 1.48 1.52 0.43 0.58 0

Thin HMA on 

PCC Freeway Min 14,538 15,180 5.00 4.00 0.00 0.00 0.00 0.00 0.00 0.00 0

Thin HMA on 

PCC Freeway Max 140,250 147,600 5.00 4.00 97.78 83.33 35.56 35.71 9.37 4.55 2

Thin HMA on 

PCC Freeway Mean 108,858 114,462 5.00 4.00 31.63 27.75 10.00 9.17 2.58 0.66 5
ELCSI 

Treatment Type Site Type

AADT Before

AADT After

Years 

Before

Years After

totrateb

totratea

injrateb

injratea

rorrateb

rorratea

UTBWC Freeway Min 7,600 6,725 5.00 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0

UTBWC Freeway Max 137,000 158,000 7.00 4.00 153.85 100.00 46.15 35.71 22.22 33.33 1

UTBWC Freeway Mean 45,655 51,350 5.59 3.41 13.00 12.63 3.90 4.04 1.79 1.52 1

UTBWC Multilane Min 5,820 6,107 5.00 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0

UTBWC Multilane Max 51,361 55,750 7.00 4.00 200.00 250.00 83.33 62.50 5.16 20.83 6

UTBWC Multilane Mean 31,378 35,065 5.76 3.24 23.38 18.53 8.77 6.36 0.54 0.54 2

UTBWC Two-Lane Min 5,820 6,112 5.00 3.00 0.00 0.00 0.00 0.00 0.00 0.00 0

UTBWC Two-Lane Max 29,444 30,379 6.00 4.00 15.96 25.86 9.72 6.94 3.57 4.46 3

UTBWC Two-Lane Mean 13,342 13,361 5.44 3.56 4.89 5.51 1.72 2.17 0.72 0.89 0

ELCSI = Evaluation of Low-Cost Safety Improvements

AADT = Average annual daily traffic

totrateb = total crash rate per mi-yr in before period 

totratea = total crash rate per mi-yr in after period

injrate = injury rate

wetrate = wet-road rate

rorrate = ROR rate 

wetrorrate = wet-road ROR crash rates per mi-yr 

MAX = maximum

MIN = minimum

PCC = Portland cement concrete

OGFC = Open graded friction course

HMA = Hot mix asphalt

UTBWC = Ultrathin bonded wearing course

70 
Table 15. Summary statistics for California reference sites.

Site Type

Total 

Length 

(mi) Type 

Pavement 

(mi)

Surface 

Width (ft) Lanes

No. of 

(mi)

Area Type 

(mi)

Divided/

Undivided 

(mi)

Avgshldwid 

(ft) AADT

Years

totrate

Two-Lane 4,733.98

AC—

PCC

4,683.23

—

50.745

Min—7 

Max—32

Mean—11.79

2—4,733.98

Rural—

Urban

4,480.36

—

253.61

Divided—

Undivided

74.80

—

4,659.18

Min—0 

Max—26 

Mean—4.90 5,768

57,800

107

10

10

10

1.50 

Multilane 445.50

AC—

PCC

416.53

—

28.97

Min—10

Max—22

Mean—12.09

3—115.61

4—272.74

5—5.38

6—51.19

> 6—0.58

Rural—

Urban

104.62

—

338.98

Divided—

Undivided

292.64

—

150.96

Min—0

Max—10

Mean—4.29 33,701

102,444

4,447

10

10

10

9.89 

Freeway 1,311.31

AC—

PCC

740.20 

—

571.10

Min—8 

Max—24

Mean—12.04

2—27.95

3—17.96

4—881.64

5—30.41

6—172.06

> 6—181.29

Rural—

Urban

854.19

—

457.12

Divided—

Undivided

1,311.31

—

0.00

Min—0

Max—22

Mean—7.52 75,518

326,778

1,995

10

10

10

12.69 

Avgshldwid = Average of left and right shoulder width

totrateb = total crash rate per mi

AADT = Average annual daily traffic

-yr in before period

totratea = total crash rate per mi-yr in after period

injrate = injury rate

wetrate = wet-road rate

rorrate = ROR rate 

wetrorrate = wet-road ROR crash rates per mi-yr

Max = Maximum

Min = Minimum

PCC = Portland cement concrete

AC = Asphalt concrete
Minnesota

Data for Minnesota were provided by the Minnesota Department of Transportation (MnDOT) 

and HSIS. The data provided by MnDOT included a list of potential treatment sites and data on 

recent paving projects. The data provided by HSIS included geometric, traffic, and crash data. 

The data are linked together using the District Number, Route Number, and milepost variables.

Geometric Data

Geometric data were obtained from HSIS and included the following variables: 

• Shoulder width.

• Shoulder type.

• Surface width.

• Surface type.

• Median width.

• Median type.

• Surface type.

• Surface width.

• Number of lanes.

• Lane width.

• Roadway class.

• Rural versus urban environment.

Pavement Data

MnDOT provided resurfacing information during the period 2000 to 2010. 

Traffic Data

Traffic data in the form of AADT and commercial vehicle AADT were obtained from HSIS for

2000 to 2010. 

Crash Data 

The crash data were provided by HSIS for 2000 to 2010. The compiled crash data contain many 

variables related to the location, time, and characteristics of each crash. The following notes 

relate to the exclusion or inclusion of crash data and definition of crash types: 

• Include only nonintersection-related mainline crashes (if loc_type = 0, 1, 8, 9, 10, 11,

25, 90, 99 (various non-intersection categories)).

• Excluded crashes where acctype = “collision with deer” or “collision with other

animal”.

• Excluded crashes where rdsurf = “snow”, “slush”, “ice/packed snow”, or

“snow/slush”.

72 
• Injury crashes defined as those where severity = fatal, A class, B class, or C class

injury.

• ROR crashes defined as those where accdigm = “ran off road left side” or “ran off

road right side”.

• Wet crashes defined as those where rdsurf=“wet”.

Construction Data

MnDOT used the highway pavement management system to verify construction history for the 

treatment sites and reference sites and ensure that no other pavement treatment had occurred at 

these locations during the study period. 

Treatment Sites

The list of treatment sites was provided by MnDOT. 

Reference Sites

Reference sites were identified by first identifying road segments in the same county and with 

the same route number as the treated sites. Then the various geometric and traffic variables were 

compared to ensure that the range of values for these variables were consistent. The pavement 

data were linked to these data, and any segments that were resurfaced or had other construction 

works between 2000 and 2010 were removed. 

Table 16 through table 18 provide summary statistics for the treatment and reference site data in 

Minnesota. 

73 
Table 16. Summary statistics for Minnesota treatment site geometry.

Treatment Site Type

Total 

Length

(mi)

Pavement 

Type (mi) Width (ft)

No. of 

Lanes (mi)

Area Type 

(mi)

Divided/Undivide

(mi)

Chip Seal

Two-Lane 251.17

AC—

251.17

PCC –

0.00

Min—12

Max—24

Mean—12.78

2—251.17 Rural—249.60

Urban—1.57

Divided—7.60

Undivided—243.56

Multilane 22.81

AC—

22.81

PCC—

0.00

Min—12

Max—16

Mean—12.08

3—3.36

4—19.44 

Rural—19.78 

Urban—3.03

Divided—18.51

Undivided—4.3

Diamond 

Grinding Freeway 7.99

AC—0.00

PCC—

7.99

Min—12

Max—13

Mean—12.07

4—3.92 

5—2.49

6—1.39

8 - 0.19

Rural—0.81

Urban—7.19

Divided—7.99

Undivided—0.00

Microsurfacing

Two-Lane 27.37

AC—

27.37

PCC—

0.00

Min—12

Max—12

Mean—12

2—27.37 Rural—27.02

Urban—0.35

Divided—0.00

Undivided—27.37

Multilane 29.68

AC—

29.68

PCC—

0.00

Min—12

Mean—18

12.41

4—29.68 Rural—11.06

Urban—18.63

Divided—29.68

Undivided—0.00

Microsurfacing 

on PCC

Two-Lane 15.63

AC—0.00

PCC—

15.63

Min—12

Max—12

Mean—12

2—15.63 Rural—14.49

Urban—1.14

Divided—0.00

Undivided—15.63

Multilane 0.32

AC—0.00

PCC—

0.32

Min—13

Max—13

Mean—13

4—0.32 Rural—0.00

Urban—0.32

Divided—0.32

Undivided—0.00

Freeway 21.40

AC—0.00

PCC—

21.40

Min—22

Max—22

Mean—22

4—20.16 

5—0.36

6—0.89

Rural—10.00

Urban—11.401

Divided—21.40

Undivided—0.00

74 
Treatment Site Type

Total 

Length

(mi) Type (mi)

Pavement 

Width (ft)

No. of 

Lanes (mi)

Area Type 

(mi)

Divided/Undivide

(mi)

Thin HMA

Two-Lane 198.05

AC—

S

Chip

133.12

eal—

64.93

Min—12

Max—14

Mean—12.01

2—198.05 Rural—194.08

Urban—3.97

Divided—0.00

Undivided—198.05

Multilane 5.64

AC—5.42

S

Chip

eal—

0.23

Min—12

Max—13

Mean—12.33

4—5.64 Rural—3.66

Urban—1.99

Divided—5.64

Undivided—0.00

Avgshldwid = Average of left and right shoulder width

HMA = Hot mix asphalt

Max = maximum 

Min = minimum

PCC = Portland cement concrete

AC = Asphalt concrete
Table 17. Summary statistics for Minnesota treatment site AADT and crashes. 

ELCSI Treatment

Type Site Type

AADT 

Before

AADT 

After

Years 

Before

Years After

totrateb

totratea

injrateb

injratea

rorrateb

rorratea

Chip Seal Multilane Min 961 924 8.00 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0.

Chip Seal Multilane Max 28,219 35,658 8.00 2.00 125.00 100.00 37.50 50.00 2.60 8.93 5.

Chip Seal Multilane Mean 12,111 14,029 8.00 2.00 4.35 6.54 1.52 1.91 0.22 0.30 0.

Chip Seal Two-Lane Min 314 317 5.00 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0.

Chip Seal Two-Lane Max 10,141 12,282 8.00 2.00 6.67 17.44 4.41 6.41 1.98 5.38 4.

Chip Seal Two-Lane Mean 3,088 3,248 7.96 2.00 0.29 0.26 0.14 0.11 0.09 0.11 0.

Diamond Grinding 

on PCC Freeway Min 23,752 27,000 6.00 3.00 0.00 0.00 0.00 0.00 0.00 0.00 0.

Diamond Grinding 

on PCC Freeway Max 74,139 72,785 7.00 4.00 60.44 64.10 19.23 32.05 19.23 12.82 19

Diamond Grinding 

on PCC Freeway Mean 54,539 54,179 6.66 3.34 6.53 5.01 1.71 2.09 1.44 1.04 1.

Microsurfacing Multilane Min 14,422 12,272 7.00 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0.

Microsurfacing Multilane Max 51,122 55,853 8.00 3.00 55.00 60.61 17.86 41.67 5.68 11.90 10

Microsurfacing Multilane Mean 33,585 33,870 7.58 2.42 6.11 6.53 2.09 2.44 0.64 0.62 1.

Microsurfacing Two-Lane Min 1,926 1,826 8.00 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0.

Microsurfacing Two-Lane Max 6,258 6,190 8.00 2.00 2.84 33.33 2.00 5.49 2.84 33.33 2.

Microsurfacing Two-Lane Mean 3,647 3,523 8.00 2.00 0.32 0.62 0.14 0.18 0.19 0.59 0.

Microsurfacing on 

PCC Freeway Min 36,036 40,122 1.00 4.00 0.00 0.00 0.00 0.00 0.00 0.00 0.

Microsurfacing on 

PCC Freeway Max 76,350 84,331 6.00 6.00 18.52 58.64 4.85 12.35 4.85 9.26 3.

Microsurfacing on 

PCC Freeway Mean 54,854 60,194 4.32 5.27 3.48 7.30 0.93 1.80 0.56 1.30 0.

Microsurfacing on 

PCC Multilane Min 32,896 35,536 8.00 2.00 2.34 1.56 0.39 0.00 1.17 0.00 0.

76 
ELCSI Treatment

Type Site Type

AADT 

Before

AADT 

After

Years 

Before

Years After

totrateb

totratea

injrateb

injratea

rorrateb

rorratea

Microsurfacing on 

PCC Multilane Max 32,896 35,536 8.00 2.00 2.34 1.56 0.39 0.00 1.17 0.00 0.

Microsurfacing on 

PCC Multilane Mean 32,896 35,536 8.00 2.00 2.34 1.56 0.39 0.00 1.17 0.00 0.

Microsurfacing on 

PCC Two-Lane Min 2,684 2,775 4.00 6.00 0.00 0.00 0.00 0.00 0.00 0.00 0.

Microsurfacing on 

PCC Two-Lane Max 3,342 3,628 4.00 6.00 0.22 0.99 0.22 0.66 0.08 0.33 0.

Microsurfacing on 

PCC Two-Lane Mean 2,964 3,038 4.00 6.00 0.06 0.25 0.04 0.16 0.02 0.12 0.

Thin HMA Multilane Min 5,026 6,228 8.00 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0.

Thin HMA Multilane Max 8,489 6,853 8.00 2.00 11.54 9.09 9.62 7.69 5.77 0.53 0.

Thin HMA Multilane Mean 6,249 6,601 8.00 2.00 2.16 2.30 1.28 1.01 0.88 0.15 0.

Thin HMA Two-Lane Min 69 61 7.00 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0.

Thin HMA Two-Lane Max 6,119 6,630 8.00 3.00 3.57 5.05 3.57 1.05 3.57 3.66 3.

Thin HMA Two-Lane Mean 1,684 1,724 7.63 2.37 0.17 0.12 0.06 0.03 0.06 0.05 0.

ELCSI = Evaluation of Low-Cost Safety Improvements

AADT = Average annual daily traffic

totrateb = total crash rate per mi-yr in before period

totratea = total crash rate per mi-yr in after period

injrate = injury rate

wetrate = wet-road rate

rorrate = ROR rate 

wetrorrate = wet-road ROR crash rates per mi-yr

HMA = Hot mix asphalt

PCC = Portland cement concrete

MIN = minimum

MAX = maximum
Table 18. Summary statistics for Minnesota reference sites.

Site Type

Total 

Length

(mi)

Pavement 

Type (mi)

Surface 

Width (ft)

No. of 

Lanes

(mi)

Area 

Type 

(mi)

Divided/

Undivide

d (mi)

Avgshldwid 

(ft) AADT

Years

totrate

Two-Lane 922.86

AC—

868.79

PCC—

54.07

Min—10

Max—24

Mean—12.39

2—922.86

Rural—

906.21 

Urban—

16.65

Divided—

0.00

Undivided

—922.86

Min—0 

Max—12

Mean—7.04

59

12,823

3,292

11

11

11

0.18 0

Multilane 188.73

AC—

158.35

PC—

30.38

Min—8 

Max—18

Mean—12.08

3—3.82

4—181.95

5—1.82

6—0.85

> 6—0.28

Rural—

114.99 

Urban—

73.74

Divided—

183.15

Undivided

—5.58

Min—0 

Max—10

Mean—2.86

688

55,074

22,515

11

11

11

2.36 0

Freeway 81.52

AC—

58.55

PCC—

22.97

Min—11

Max—14

Mean—12.21

3—0.15

4—66.26

5—3.75

6—8.48

> 6—2.88 

Rural—

57.37 

Urban—

24.15

Divided—

81.52

Undivided

—0.00

Min—0 

Max—10

Mean—4.56

17,597

104,188

49,061

11

11

11

2.59 0

Avgshldwid = Average of left and right shoulder width

AADT =Average annual daily traffic

totrateb = total crash rate per mi-yr in before period

totratea = total crash rate per mi-yr in after period

injrate = injury rate

wetrate = wet-road rate

rorrate = ROR rate 

wetrorrate = wet-road ROR crash rates per mi-yr

AC = Asphalt concrete

PCC = Portland cement concrete

78 
CHAPTER 5. ANALYSIS

ANALYSIS OBJECTIVES

As discussed, the objective of this analysis was to estimate the effect of various low-cost 

pavement treatments on crashes using treatments from several States. These treatments were 

installed primarily for pavement preservation and not necessarily for safety improvement. As

presented in chapter 3, the following treatments were evaluated:

• Chip seal (single, double, and triple layer).

• Diamond grinding (concrete pavement only).

• Grooved concrete pavement.

• Microsurfacing (asphalt and concrete pavement).

• OGFC (asphalt and concrete pavement).

• Slurry seal (asphalt pavement).

• Thin HMA (asphalt and concrete pavement).

• UTBWC (asphalt and concrete pavement).

The basic objective of the crash data analysis was to estimate the change in target crashes. Only 

nonintersection, nonanimal related crashes and crashes not involving snow or ice were 

considered. Crash types examined included the following: 

• Total.

• Injury.

• ROR.

• Wet-Road.

• Dry-Road.

• Wet-Road ROR.

Further questions of interest examined included the following: 

• Do effects vary by level of traffic volumes?

• Do effects vary by underlying pavement type?

• Do effects vary by posted speed limit or by urban-rural environments?

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

• Do effects vary by State and road class?

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

and severity?

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

tasks, including the need to do the following: 

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

small changes in safety for some crash types.

79 
• Carefully select reference sites to properly account for changes in safety not due to 

the treatments, including regression-to-the-mean, traffic volume changes, and time 

trends. 

• Properly account for traffic volume changes. 

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

facilitate broader applicability of the research products. 

As discussed in chapter 4, roadway, pavement data, traffic volume, and crash data were acquired 

for sites in Pennsylvania, Minnesota, North Carolina, and California to facilitate the analysis. 

The States also provided information related to the installation of the pavement improvement 

(i.e., location and date). 

ANALYSIS METHODOLOGY

The general analysis methodology applied is the EB before–after approach. The methodology is 

well documented by Hauer.

(43)

 The advantages of the EB method include the following: 

• Properly accounting for regression-to-the-mean.

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

differences between the before and after periods.

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

• Providing a foundation for developing guidelines for estimating the likely safety 

consequences of contemplated installations. 

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

amalgamating data and results from diverse jurisdictions.

The approach comprises three basic steps: 

• Step 1: Predict what safety would have been in the “after” period had the status-quo 

been maintained.

• Step 2: Estimate what the actual safety was in the “after” period. 

• Step 3: Compare the two. 

The EB procedure requires the calibration of SPFs, as outlined in the next section, relating 

crashes of different types and severities to traffic flow and other relevant factors for each 

jurisdiction for locations without the treatment, with appropriate adjustments for temporal 

effects. This will enable the simultaneous accounting for temporal and possible regression-tothe-mean effects, as well as those related to changes in traffic volume.

80 
DEVELOPMENT OF SAFETY PERFORMANCE FUNCTIONS

Fundamental to the EB approach is the use of the SPFs to represent the conditions before

installation. Where sufficient data are available for a reference population of sites similar to 

those treated, it is desirable to calibrate these functions directly for the jurisdiction and analysis 

period of interest. 

Data required for SPF development are: crash, traffic, and geometric data for a sample of 

reference sites that are similar to those for which the SPF would be applied. The data are 

required for each year of the analysis period (i.e., the period of before and after at the treatment 

sites). 

The direct calibration of SPFs was accomplished with generalized linear modeling (GLM) using 

the R software package. This procedure allows the specification of a negative binomial error 

structure, which is now recognized as more appropriate for crash counts than the normal 

distribution that is assumed in conventional regression modeling. The GLM procedure also 

estimates the overdispersion parameter k of the negative binomial distribution that is used in the 

EB estimation. Crash counts at locations in the reference group are used as estimates of the 

dependent variable, which is the expected number of crashes per year by type and severity, while 

corresponding road characteristics and traffic data are used as estimates of the independent 

variables. 

SPECIFICS OF THE EMPIRICAL BAYES BEFORE-AFTER EVALUATION

Overall Safety Effects 

In the EB evaluation of the effect of a treatment, the change in safety for a given crash type at a 

treated site is given by the equation in figure 17:

Figure 17. Equation. Change in safety for a given crash type at a treated site.

Where: 

B = expected number of crashes that would have occurred in the after period without the 

treatment

A = number of reported crashes in the after period. 

Because of changes in safety that may result from changes in traffic volume, from regression-tothe-mean, and from trends in crash reporting and other factors, the count of crashes before a 

treatment by itself is not a good estimate of B—a reality that has now gained common 

acceptance.(43)

 Instead, B is estimated from an EB procedure in which a safety performance

function 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 a treatment site 

being analyzed.(43) The sum of these annual SPF estimates (P) is then combined with the count of 

crashes (x) in the before period at the treatment site to obtain an estimate of the expected number 

of crashes (m) before the treatment. This estimate of m is shown in figure 18: 

B − A

81 
Figure 18. Equation. Estimate of the expected number of crashes before treatment.

The weight w is estimated using the equation in figure 19: 

Figure 19. Equation. Estimate of weight.

Where: 

k = overdispersion parameter of the negative binomial distribution that is assumed for the crash 

counts used in estimating the SPF. The value of k is estimated from the SPF calibration process 

with the use of a maximum likelihood procedure. 

A factor is then applied to m from the equation in figure 18 to account for the length of the after 

period, differences in traffic volumes between the before and after periods, and other unknown 

differences between these two periods accounted for by using the yearly factors of the SPF. 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 B. The 

procedure also produces an estimate of the variance of B, the expected number of crashes that 

would have occurred in the after period without the treatment. 

The estimate of B is then summed over all sites in a treatment group of interest (to obtain Bsum) 

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

of B is also summed over all sites in the group of interest. 

The index of safety effectiveness ( ) is estimated using the equation in figure 20: 

Figure 20. Equation. Estimate of the index of safety effectiveness.

The standard deviation of θ is given by the equation in figure 21: 

Figure 21. Equation. Standard deviation of the estimated index of safety effectiveness.

The percent change in crashes is in fact 100(1− ); thus, a value of = 0.70 with a standard 

deviation of 0.12 indicates a 30-percent reduction in crashes with a standard deviation of 12 

percent.

Effects on Different Severity and Impact Types

The methodology is essentially the same as outlined earlier. The difference is that crashes of 

interest are used along with SPFs specific to these crash types. 

m = w(P) + (1 − w)(x)

w = 1/(1 + kP)

θ

θ = (Asum /Bsum )/{1 + [Var(Bsum )/BSum 2 ]}

Stddev(θ) = [θ2{[Var(Asum)/Asum2] + [Var(Bsum)/Bsum2]} / [1 + Var(Bsum)/Bsum2]

2

]

0.5

θ θ

82 
Effects of Design, Traffic, Operational, and Safety Characteristics

Where samples were large enough, the study sought to isolate sites with certain levels of a given 

variable and to estimate the separate effects for each level by road class. In the case of 

continuous variables, such as traffic volume, aggregation was attempted over specified ranges of 

that variable, and regression models were estimated to relate the safety effect to the value of that 

variable. These include the effects of the following: 

• Annual precipitation levels. 

• Level of safety before installation measured as the expected crash frequency. 

• Traffic volume levels. 

Differential Effects Over Time

The EB procedure facilitated the estimation of differential effects over time for certain treatment 

types. This was important given the belief that the effects of some treatments deteriorate over 

time. A minor adjustment to the procedure allowed the investigation of effects for each year 

starting immediately after installation as opposed to calendar years. This refinement was

necessary to examine the effects over time, for example at 1, 2, and 3 years after installation, to 

the extent that the small sample sizes facilitated this investigation.

SAFETY PERFORMANCE FUNCTIONS

This section presents the SPFs developed. The SPFs are used in the EB methodology to estimate 

the expected number of crashes in the after period without treatment. 

GLM was used to estimate model coefficients and assumed a negative binomial error 

distribution, which is consistent with the state of research in developing these models. 

Alternative models were evaluated by comparing the magnitude and statistical significance of the 

variables included as well as the value of the overdispersion parameter, which in itself, is a 

reliable goodness-of-fit measure, with a smaller overdispersion parameter indicating a model that 

better captures the overdispersion in the data. 

Separate SPFs were developed for each State and for different site and crash types. SPFs were 

not estimated for dry-road crashes because logically the EB estimates for these crashes could be 

derived as the difference between the estimates for total and wet-road crashes.

Pennsylvania 

The model form for the Pennsylvania SPFs is shown in figure 22: 

Figure 22. Equation. Model form for Pennsylvania SPFs. 

Crashes/mile-year = exp(ln(α)+β2Urbrur+β3Shldwid)(AADT) β1

83 
Where: 

AADT = Average Annual Daily Traffic

Urbrur = 1 if rural environment; 0 if urban 

Shldwid = average shoulder width in ft 

, the s, and the overdispersion parameter, k, are parameters estimated in the modeling 

process. 

Table 19 provides the parameter estimates and standard errors.

Table 19. SPF parameter estimates and standard errors for Pennsylvania treatment sites.

Site Type

Crash Type

ln( ) (s.e.) 1 (s.e.) 2 (s.e.) 3 (s.e.)

Overdispersion 

Parameter, 

k (s.e.)

Controlled Access

Total

-9.2972

(0.4158)

1.0147

(0.0391)

-0.3707

(0.0548)

-0.0865

(0.0094)

0.4626

(0.0261)

Uncontrolled Access -6.4756

(0.0701)

0.8174

(0.0078)

-0.1116

(0.0157)

-0.0589

(0.0027)

0.5155

(0.0092)

Controlled Access

Injury

-9.4717

(0.4935)

0.9569

(0.0462)

-0.3968

(0.0650)

-0.0810

(0.0110)

0.4787

(0.0373)

Uncontrolled Access -7.2450

(0.0841)

0.8252

(0.0094)

-0.0495

(0.0181)

-0.0465

(0.0031)

0.5024

(0.0120)

Controlled Access

ROR

-5.1206

(1.1354)

0.2664

(0.1068)

-0.2769

(0.1362) n/a 1.3620(0.2820)

Uncontrolled Access -5.4010

(0.1325)

0.4927

(0.0150)

-0.3326

(0.0298)

-0.0831

(0.0055)

1.1056

(0.0377)

Controlled Access

Wet-Road

-9.0427

(0.7568)

0.8363

(0.0710)

-0.3389

(0.0981)

-0.0860

(0.0168)

1.0787

(0.0968)

Uncontrolled Access -7.0113

(0.1265)

0.7174

(0.0142)

-0.1386

(0.0279)

-0.0753

(0.0049)

1.2829

(0.0323)

Controlled Access Wet-Road 

ROR

Apply model for Wet-Road with a factor of 7 percent

Uncontrolled Access -6.1144

(0.2440)

0.4433

(0.0278)

-0.4028

(0.0549)

-0.1064

(0.0106)

3.7394

(0.1846)

s.e. = Standard error

ROR = Run-off road

North Carolina 

The model form for the North Carolina SPFs is shown in figure 23: 

Figure 23. Equation. Model form for North Carolina SPFs.

Where: 

AADT = Average Annual Daily Traffic

Urbrur = 0 if rural environment; 1 if urban 

α β

α β β β

Crashes/mile-year = exp(ln(α)+β2Urbrur)(AADT) β1

84 
, the s, and the overdispersion parameter, k, are parameters estimated in the modeling 

process. 

Table 20 provides the parameter estimates and standard errors.

Table 20. SPF parameter estimates and standard errors for North Carolina treatment sites.

Site Type Crash Type ln( ) (s.e.) 1 (s.e.) 2 (s.e.)

Overdispersion 

Parameter,

k (s.e.)

Freeway

Total

-8.9291

(1.7994)

1.1483

(0.1749) n/a 1.4036(0.2603)

Two-Lane -6.4036

(0.1230)

0.8662

(0.0136)

-0.4370

(0.0237)

0.8155

(0.0158)

Multilane Divided -12.2644

(0.7668)

1.5219

(0.0779)

-0.8490

(0.0763)

1.3674

(0.0546)

Multilane Undivided -10.4562

(0.8357)

1.3715

(0.0872)

-1.1596

(0.1126)

1.3972

(0.0711)

Freeway

Injury

-12.6732

(4.0381)

1.3625

(0.3622)

0.5611

(0.6506)

1.6429

(0.3745)

Two-Lane -6.6692

(0.1344)

0.7780

(0.0149)

-0.2506

(0.0254)

0.6535

(0.0181)

Multilane Divided -13.7169

(0.8460)

1.5502

(0.0857)

-0.5474

(0.0817)

1.1284

(0.0581)

Multilane Undivided -12.4483

(0.9165)

1.4516

(0.0953)

-0.9979

(0.1283)

1.1527

(0.0776)

Freeway

ROR

-18.6447

(7.8055)

1.7776

(0.6959)

-0.5034

(1.1830)

3.1947

(1.2079)

Two-Lane -6.1174

(0.2650)

0.5272

(0.0296)

-0.6736

(0.0496)

1.7266

(0.0828)

Multilane Divided -11.7038

(1.3211)

1.1004

(0.1332)

-0.2530

(0.1276)

1.1807

(0.1455)

Multilane Undivided -10.1531

(1.3697)

0.9828

(0.1421)

-0.6875

(0.1960)

1.0338

(0.1615)

Freeway

Wet-Road

-12.8639

(2.4304)

1.3526

(0.2293) n/a 1.2069(0.3233)

Two-Lane -7.6283

(0.1773)

0.8035

(0.0196)

-0.3784

(0.0328)

0.8486

(0.0306)

Multilane Divided -16.3897

(1.0357)

1.7516

(0.1047)

-0.6639

(0.0991)

1.3400

(0.0836)

Multilane Undivided -14.7279

(1.1002)

1.6208

(0.1142)

-1.5177

(0.1696)

1.3161

(0.1053)

Freeway

Wet-Road 

ROR

Apply model for Wet-Road with a factor of 18 percent

Two-Lane Apply model for Wet-Road with a factor of 9 percent

Multilane Divided Apply model for Wet-Road with a factor of 5 percent

Multilane Undivided Apply model for Wet-Road with a factor of 4 percent

s.e. = Standard error

ROR = Run-off road

α β

α β β

85 
California

The model form for the California SPFs is shown in figure 24: 

Figure 24. Equation. Model form for California SPFs. 

Where: 

AADT = Average Annual Daily Traffic

Urbrur = 0 if rural environment; 1 if urban 

Surftype = 1 if asphalt; 0 if concrete

Medwid = median width in ft 

Avgshldwid = average of left and right shoulder width in ft 

Lanewid = lane width in ft 

Terrain = flat, rolling, or mountainous

Divided = 0 if undivided; 1 if divided 

, the s, and the overdispersion parameter, k, are parameters estimated in the modeling 

process. 

Table 21 provides the parameter estimates and standard errors.

Crashes/mile-year =

exp(ln(α)+β2Urbrur+β3Surftype+β4Medwid+β5Avgshldwid+β6Lanewid+

β7Terrain+β8Divided)(AADT) β1

α β

86 
Table 21. SPF parameter estimates and standard errors for California treatment sites

Site Type Type

Crash ln(α) 

(s.e.) β1 (s.e.) β2 (s.e.) β3 (s.e.) β4 (s.e.) β5 (s.e.) β6 (s.e.) β7 (s.e.) 

Freeway

Total

-9.1423

(0.2374)

1.1329

(0.0202)

-0.3610

(0.0362)

-0.1118

(0.0277)

-0.0034

(0.0005)

-0.0290

(0.0026) n/a n/a 

Two-Lane -6.0686

(0.1417)

0.9022

(0.0132)

-0.5306

(0.0394) n/a n/a -0.0278

(0.0022)

-0.0240

(0.0075)

Flat: -0.0613 

Rolling

(0.0319)

: 0 

Mountainous: 

0.2955 (0.0297)

Multilane -8.5596

(0.3578)

1.0851

(0.0331)

-0.4479

(0.0547)

0.3783

(0.0897)

-0.0083

(0.0011)

-0.0188

(0.0043) n/a n/a 

Freeway

Injury

-9.0776

(0.2467)

1.0198

(0.0209)

-0.3414

(0.0367)

-0.0972

(0.0274)

-0.0032

(0.0005)

-0.0215

(0.0026) n/a n/a 

Two-Lane -6.0802

(0.1581)

0.8215

(0.0143)

-0.3919

(0.0418) n/a n/a -0.0298 

(0.0024)

-0.0404

(0.0089)

Flat: -0.1469 

Rolling

(0.0341)

: 0 

Mountainous: 

0.3413 (0.0316)

Multilane -9.1947

(0.3745)

1.0635

(0.0348)

-0.3623

(0.0560)

0.3041

(0.0898)

-0.0083

(0.0011)

-0.0213

(0.0043) n/a n/a 

Freeway

ROR

-3.1731

(0.3680)

0.2930

(0.0317)

-0.0818

(0.0527)

0.3059

(0.0434)

0.0030

(0.0009)

-0.0080

(0.0049) n/a n/a 

Two-Lane -4.3617

(0.2191)

0.5560

(0.0166)

0.2162

(0.0531)

0.1872

(0.1100) n/a -0.0448

(0.0029)

-0.0852

(0.0117)

Flat: -0.3181 

Rolling

(0.0408)

: 0 

Mountainous: 

0.3464 (0.0356)

Multilane -6.7850

(0.4649)

0.5544

(0.0447)

1.1378

(0.0682)

0.3380

(0.1181) n/a n/a n/a n/a 
Site Type

Crash 

Type

ln(α) 

(s.e.)β1 (s.e.)β2 (s.e.)β3 (s.e.)β4 (s.e.)β5 (s.e.)β6 (s.e.)β7 (s.e.)

Freeway

Wet-Road

-9.1377

(0.4243)

0.9896

(0.0365)

-0.3465

(0.0623)

-0.1355

(0.0176)

-0.0106

(0.0009)

-0.0437

(0.0042) n/a n/a 

Two-Lane

-8.9863

(0.2988)

1.0546

(0.0276)

-0.3104

(0.0716) n/a n/a

-0.0544

(0.0043)

-0.0616

(0.0161)

Flat:-0.2713 

(0.0616)

Rolling: 0 

Mountainous: 

0.6234 (0.0539)

Multilane

-11.6521

(0.5810)

1.1771

(0.0545)

0.2091

(0.0858)

0.4193

(0.1421)

-0.0158

(0.0019)

-0.0338

(0.0069) n/a n/a 

Freeway

Wet-Road 

ROR

-4.3927

(0.6470)

0.2670

(0.0542)

-0.2842

(0.0976)

0.2958

(0.0767)

-0.0053

(0.0015) n/a n/a n/a 

Two-Lane

-7.4394

(0.4285)

0.7843

(0.0372)

0.2588

(0.1105) n/a n/a

-0.0809

(0.0064)

-0.1126

(0.0262)

Flat:-0.7034 

(0.0921)

Rolling: 0 

Mountainous: 

0.6254 (0.0710)

Multilane

-9.2772

(0.9370)

0.6312

(0.0858)

1.8060

(0.1584)

0.4104

(0.2662)

-0.0108

(0.0029)

-0.0420

(0.0136) n/a n/a 

s.e. = Standard error

ROR = Run-off road

88 
Minnesota

The model form for the Minnesota SPFs is: 

Figure 25. Equation. Model form for Minnesota SPFs.

Where: 

AADT = Average Annual Daily Traffic

Pavetype = 1 if asphalt; 0 if PCC

Lanes = 0 if 4 or fewer lanes; 1 if greater than 4

Urbrur = 0 if rural; 1 if urban

Lanewid = lane width in ft 

, the s, and the overdispersion parameter, k, are parameters estimated in the modeling 

process. 

Table 22 provides the parameter estimates and standard errors.

Crashes/mile-year = exp(ln(α) + β2Pavetype + β3Lanes + β4Urbrur + β5Lanewid)(AADT)β1

α β

89 
Table 22. SPF parameter estimates and standard errors for Minnesota treatment sites

Site Type Crash Type ln( ) (s.e.) 1 (s.e.) 2 (s.e.) 3 (s.e.) 4 (s.e.) 5 (s.e.)

Ov

P

Freeway

Total

-11.4349

(1.5748)

1.2288

(0.1446)

-0.4417

(0.1619) n/a n/a n/a 

Two-Lane -7.8919

(0.5019)

0.9612

(0.0502)

0.2932

(0.1460) n/a -0.4795(0.1612)

-0.0701

(0.0207)

Multilane -5.4122

(0.8177)

0.7111

(0.0799) n/a n/a -1.2037(0.0841) n/a 

Freeway

Injury

-12.3445

(1.4706)

1.1764

(0.1372) n/a n/a n/a n/a 

Two-Lane -8.7986

(0.6043)

0.8716

(0.0602)

0.7464

(0.2139) n/a -0.4302(0.1932)

-0.0473

(0.0240)

Multilane -8.4136

(0.9822)

0.8947

(0.0955) n/a n/a -0.9435(0.0987) n/a 

Freeway

ROR

-8.3215

(3.1955)

0.7673

(0.2850) n/a 0.5970(0.3234) n/a n/a 

Two-Lane -9.1717

(0.6691)

0.7388

(0.0609)

0.9637

(0.2400) n/a 0.6745(0.2985)

-0.0432

(0.0261)

Multilane -8.2183

(0.9628)

0.7435

(0.0934) n/a n/a 0.1862(0.0994) n/a 

Freeway

Wet-Road

-15.1559

(1.9839)

1.3883

(0.1853) n/a n/a n/a n/a 

Two-Lane -12.8744

(0.9323)

1.1757

(0.1033)

0.7831

(0.3736) n/a n/a -0.0606(0.0379)

Multilane -7.8879

(1.3467)

0.7692

(0.1310) n/a n/a -1.2985(0.1388) n/a 

Freeway

Wet-Road ROR

-6.9575

(2.7605)

0.5212

(0.2604) n/a n/a n/a n/a 

Two-Lane Use model for Wet-road with factor of 55 percent

Multilane -9.8883

(1.4965)

0.7710

(0.1492) n/a n/a n/a n/a 

s.e. = Standard error

ROR = Run-off road

n/a = Not applicable

α β β β β β

90 
USE OF CLIMATE DATA

Objective 

In the study work plan, climatic data were identified as of interest for the study. The hypothesis 

was that climate conditions are likely related to the risk of crashes that may be treatable through 

improved pavement friction conditions. 

The study design for developing CMFs is applying the EB before–after methodology. In this 

approach, factors that may affect expected crash frequencies but that are not related to the 

treatment of interest are accounted for through the use of SPFs. This is done is by calibrating the 

SPFs using a reference group and determining yearly factors that represent time trends in crashes 

owing to demographics, reporting trends, weather, etc. These SPFs also include as many 

geometric-related variables and traffic exposure variables as possible so that changes in traffic 

are accounted for and predictions are as site-specific as possible. 

To directly include weather-related measurements in the EB analysis, these variables would need 

to be used in the SPFs. This would in fact be attractive because site-specific differences between 

the before and after periods in temperature and/or precipitation could be accounted for when 

predicting expected crashes without treatment. 

The climate data of interest included average monthly temperatures and average monthly 

precipitation. When using any data that change over time, there is a need to aggregate up to a 

reasonable level of analysis while leaving the data as disaggregated as possible so that variation 

is still observed. It was felt that using monthly data provided a reasonable balance between these 

two needs. 

The feasibility of including average temperatures and precipitation was explored using the 

reference group from North Carolina. 

Source of Data

As mentioned above, the two variables that were identified for treatment site climate data were 

temperature and precipitation. Temperature data consisted of average monthly temperatures for 

each month during the before and after analysis period for each site. Precipitation data consisted 

of actual monthly precipitation during the analysis period for each site. 

The project team initially examined the feasibility of collecting climate data for each individual 

treatment site by selecting an appropriate weather station for each site. Considering the 

thousands of treatment sites and reference sites, this would have required a tremendous amount 

of effort to first identify viable weather stations, and then link each treatment site to a station. A 

second option was to collect climate data by county, because the county in which each treatment 

site is located is known. This did not prove to be a feasible option either because there are no 

known sources for climate data by county. 

The most viable option identified by the team was to collect weather data by National Climatic

Data Center (NCDC) divisions. Each State is broken down into several divisions (up to 10 per 

State) encompassing several counties each, with the borders of the divisions generally (but not 

91 
always) following county boundaries. The NCDC uses an algorithm to compile and summarize 

climate data by division using the various weather stations within the division. This helps to 

eliminate uncertainty associated with the reliability of individual weather stations in the NCDC 

network. 

Therefore, in compiling climate data for this effort, the project team established the NCDC 

division for each treatment or reference site based on the county in which each site is located. 

The monthly temperature and precipitation data for each division during the before and after 

analysis time period are then obtained from NCDC using the Land-Based Station Data.

Methodology and Results

The pilot test of climate data involved reestimating the SPFs using the reference group from 

North Carolina and comparing with the previously estimated SPFs. The difference now is that 

the unit of analysis is the monthly crash count rather than the sum of the observed crashes over 

the study period. The modeling applied the General Estimating Equations regression approach, 

which is required to account for temporal correlations that arise because each site is in the data as 

a separate observation for each month. 

The evaluation of the new SPFs included a comparison with the earlier SPFs, the magnitude and 

significance of the estimated parameters, and a comparison of the estimated overdispersion 

parameters with and without the climate data.

The development of SPFs was attempted for total, injury, and ROR crashes. These were 

attempted for freeway, two-lane, multilane divided, and multilane undivided roads. Table 23

shows which SPFs were successful and which were not for the monthly data. Of the 12 SPFs 

attempted, no SPF was successfully calibrated for 7 categories. For the five categories for which 

an SPF was possible using the monthly data there was no improvement in the goodness of fit of 

the model for three. For the remaining two the improvement in goodness of fit was only slight. 

Table 23. Summary of pilot test results of including climatic data. 

Crash Type Freeway Two-Lane Multilane Divided

Multilane 

Undivided

Total Slight improvement 

using climate data No SPF calibrated No improvement using climate data

No improvement 

using climate data

Injury Slight improvement 

using climate data No SPF calibrated No improvement using climate data No SPF calibrated

ROR No SPF calibrated No SPF calibrated No SPF calibrated No SPF calibrated

ROR = Run-off road

SPF = Safety performance function

Conclusions on the Use of Climate Data 

The use of monthly data makes the estimation of SPFs difficult because of the preponderance of 

zero counts. It was found that when using monthly data, SPFs could not be estimated for 7 of 

12 site type/crash groups. 

92 
For the SPFs estimated with monthly data, the difference in model fit as measured by the 

overdispersion parameter between those SPFs with and without the climate data variables is 

negligible.

Considering the difficulty in estimating SPFs using monthly data and the negligible improvement 

in model fit using climate data where those SPFs were possible, it is was not recommended to 

further consider climate data in the reference group SPFs. 

An additional concern with the monthly data is that the effects of climate are likely correlated to 

traffic volumes. The volume variable available is the average for the entire year. Fluctuations in 

volume throughout the year would be expected (e.g. the summer driving season), and these are 

likely correlated to average temperatures and precipitation. Unfortunately, average daily traffic 

volumes by month are not available. 

93 

CHAPTER 6. BEFORE–AFTER ANALYSIS

AGGREGATE RESULTS

Table 24 through table 31 provide the estimated CMFs and standard errors for the various 

treatments, broken down by crash type, State, and road class. A general discussion follows the 

presentation of all of the aggregate results. 

Chip Seal Results

The results are shown in table 24. For multilane roads, there are significant benefits overall for 

wet-road crashes, due largely to reductions in California. There was an estimated increase in dryroad crashes on these roads, which contributed to a significant (5-percent level) increase in total 

crashes.

Table 24. Estimates of CMFs for chip seal treatment. 

Group Mi

Crashes 

After

Estimated CMF (standard error)

Total 

Crashes

Injury 

Crashes

ROR

Crashes

WetRoad 

Crashes

DryRoad 

Crashes

Wet-Road 

ROR

Crashes

California 948 3,272 0.908 

(0.020)

0.892

(0.028)

0.870

(0.032)

0.830

(0.053)

0.918

(0.022)

0.709

(0.074)

Minnesota 274 179 1.255 

(0.103)

1.005

(0.134)

1.271

(0.173)

1.604

(0.312)

1.201

(0.108)

0.862

(0.355)

North 

Carolina 765 2,149 1.011 (0.029)

1.011

(0.039)

0.655

(0.066)

0.937

(0.055)

1.027

(0.033)

0.682

(0.141)

Pennsylvania 570 1,271 0.949

(0.031)

0.959

(0.041)

1.053

(0.069)

0.999

(0.062)

1.256

(0.044)

1.004

(0.125)

All Freeway 15 94 0.832

(0.102)

0.570

(0.119)

0.638

(0.202)

Too few 

crashes

0.948

(0.122)

Too few 

crashes

All 

Multilane 95 619 1.147(0.059)

1.105

(0.085)

0.959

(0.094)

0.775

(0.116)

1.206

(0.066)

0.373

(0.157)

Multilane 

California 70 425 1.046(0.065)

1.039

(0.093)

0.935

(0.098)

0.423

(0.096)

1.141

(0.075)

0.222

(0.130)

Multilane 

Minnesota 23 94 1.519(0.178)

1.067

(0.221)

1.214

(0.342)

Too few 

crashes

1.412

(0.186)

0.997

(0.705)

Multilane 

North 

Carolina

1 100

1.385

(0.172) 

1.656

(0.327) 

1.004

(0.708) 

Too few 

crashes

1.390

(0.188) 

Too few 

crashes

All TwoLane

2448 6,158 0.939

(0.015)

0.934

(0.020)

0.883

(0.028)

0.950

(0.035)

0.937

(0.017)

0.829

(0.062)

Two-Lane 

California 863 2,753 0.892(0.022)

0.884

(0.030)

0.865

(0.034)

0.927

(0.063)

0.888

(0.023)

0.775

(0.083)

Two-Lane 

Minnesota 251 85 1.050(0.121)

0.960

(0.166)

1.285

(0.199)

1.092

(0.349)

1.045

(0.129)

Too few 

crashes

Two-Lane 

North 

Carolina

764 2,049

0.997

(0.029) 

0.995

(0.040) 

0.650

(0.066) 

0.650

(0.066) 

1.014

(0.034) 

0.666

(0.141) 

Two-Lane 

Pennsylvania 570 1,271 0.949(0.031)

0.959

(0.041)

1.053

(0.069)

0.999

(0.062)

0.933

(0.036)

1.004

(0.124)

CMF = Crash modification factor

ROR = Run-off road

95 
For chip seal on two-lane roads, there was a small benefit overall (significant at the 10-percent

level) for wet-road crashes due mainly to reductions in California and North Carolina. For dryroad crashes, there was a small benefit overall (significant at the 5-percent level) due mainly to 

reductions in California and Pennsylvania. These benefits contribute to an overall benefit for all 

crashes and States combined for chip seal on two-lane roads.

There were too few crashes on freeways with this treatment to obtain a definitive result, although 

there are indications of an overall benefit for total crashes.

Diamond Grinding Results 

For diamond grinding, the results in table 25 indicate that there was an overall benefit 

(significant at the 5-percent level) for both wet- and dry-road crashes, which resulted in a 

significant overall benefit for total crashes.

Table 25. Estimates of CMFs for diamond grinding treatment. 

CMF = Crash modification factor

ROR = Run-off road

Thin HMA Results

For thin HMA, the results in table 26 indicate that there were benefits (significant at the 

5-percent level) for wet-road crashes for multilane roads and freeways, and no effect overall for 

dry-road crashes. (For the latter crash type, there was an increase in California and a decrease in 

North Carolina, both results significant at the 5-percent level.) 

Group Mi

Crashes 

After

Estimated CMF (standard error)

Total 

Crashes

Injury 

Crashes

ROR

Crashes

Wet-Road 

Crashes

Dry-Road 

Crashes

Wet-Road 

ROR

Crashes

California 85 12,267 0.950

(0.012)

0.973

(0.020)

0.606

(0.043)

0.866

(0.037)

0.957

(0.012)

0.703

(0.113)

Minnesota 8 119 0.899

(0.099)

1.127

(0.204)

1.221

(0.256)

Few 

crashes

0.792

(0.098)

Few crashes North 

Carolina

24 139 0.641

(0.057)

0.525

(0.091) Few crashes 0.576(0.058)

Pennsylvania 33 105 0.720

(0.081)

0.769

(0.115)

0.106

(0.106)

0.480

(0.136)

0.898

(0.104)

All Freeway 141 12,518 0.943

(0.011)

0.967

(0.020)

0.642

(0.043)

0.869

(0.036)

0.950

(0.012)

0.869

(0.120)

Freeway

California

76 12,155 0.951

(0.012)

0.975

(0.020)

0.595

(0.044)

0.862

(0.037)

0.959

(0.012)

0.700

(0.115)

All Multilane 8 108 Insufficient sites

All Two-Lane 1 4 Insufficient sites

96 
Table 26. Estimates of CMFs for thin HMA treatment. 

CMF = Crash modification factor

ROR = Run-off road

For two-lane roads, the thin HMA treatment was associated with highly significant increases 

overall in both wet- and dry-road crashes, a pattern that was consistent between California and 

North Carolina, the two States with large enough samples for a definitive result.

OGFC Results

For OGFC, the results in table 27 indicate a negligible effect on wet-road crashes for multilane 

and two-lane roads, but increases in dry-road crashes resulted in significant increases (5-percent

level) in total crashes for these road types. By contrast, for freeways, there was a small but 

significant (5-percent level) decrease in total crashes, due in large part to highly significant and 

Group Mi

Crashes 

After

Estimated CMF (standard error)

Total 

Crashes

Injury 

Crashes

ROR

Crashes

WetRoad 

Crashes

Dry-Road 

Crashes

Wet-Road 

ROR

Crashes

California 584 20,275 1.091

(0.010)

1.087

(0.017)

0.972

(0.034)

0.938

(0.032)

1.104

(0.011)

0.772

(0.075)

Minnesota 204 43 0.907

(0.148)

0.963

(0.220)

1.103

(0.243)

0.531

(0.310)

0.957

(0.163)

0.750

(0.533)

North Carolina 3,154 39,579 1.073

(0.009)

1.125

(0.014)

1.278

(0.033)

1.069

(0.018)

1.074

(0.010)

0.999

(0.047)

Pennsylvania 7 29 1.102

(0.252)

0.906

(0.294)

1.471

(0.931)

0.674

(0.367)

1.401

(0.335)

No crashes

All Freeway 259 18,323 1.021

(0.011)

0.986

(0.018)

0.973

(0.042)

0.910

(0.028)

1.039

(0.012)

0.797

(0.065)

Freeway 

California 164 13,326 1.043(0.012)

1.019

(0.021)

0.666

(0.040)

0.903

(0.038)

1.054

(0.013)

0.551

(0.091)

Freeway North 

Carolina 87 5,068 0.967(0.023)

0.908

(0.037)

1.405

(0.097)

0.914

(0.039)

0.990

(0.029)

0.871

(0.083)

Freeway 

Pennsylvania 7 29 Insufficient crashes

All Multilane 279 15,776 0.988

(0.013)

1.021

(0.021)

1.420

(0.066)

0.865

(0.028)

1.010

(0.015)

1.149

(0.108)

Multilane 

California 72 4,241 1.188(0.027)

1.191

(0.040)

1.051

(0.098)

0.955

(0.075)

1.209

(0.028)

0.680

(0.195)

Multilane 

Minnesota 6 7 Very few sites and crashes

Multilane 

North Carolina 201 11,528 0.930(0.015)

0.956

(0.025)

1.566

(0.086)

0.853

(0.031)

0.946

(0.017)

1.222

(0.122)

All Two-Lane 3,411 25,827 1.194

(0.011)

1.247

(0.016)

1.180

(0.031)

1.256

(0.023)

1.181

(0.013)

1.007

(0.054)

Two-Lane 

California 348 2,808 1.203(0.031)

1.167

(0.043)

1.262

(0.062)

1.018

(0.083)

1.223

(0.033)

0.993

(0.137)

Two-Lane 

Minnesota 198 36 0.930(0.165)

0.881

(0.222)

1.042

(0.244)

Too few 

crashes

Too few 

crashes

Too few 

crashes

Two-Lane 

North Carolina 2,866 22,983 1.193(0.012)

1.258

(0.017)

1.146

(0.036)

1.273

(0.024)

1.175

(0.014)

1.013

(0.058)

Two-Lane 

Pennsylvania 0 0 No sites

97 
substantial reduction in wet-road crashes with no change in dry-road crashes for California and 

North Carolina combined. 

Table 27. Estimates of CMFs for open OGFC treatment. 

CMF = Crash modification factor

ROR = Run-off road

Grooving Results 

For grooving, there were two few sites to obtain a definitive result as indicated in table 28. 

Table 28. Estimates of CMFs for grooving treatment. 

CMF = Crash modification factor

ROR = Run-off road

Microsurfacing Results

The results are shown on table 29. For two-lane roads, there was a decrease in wet-road crashes 

and an increase in dry-road crashes overall (both results significant at the 5-percent level) 

resulting in a net increase in total crashes that was also significant at the 5-percent level. This 

trend was mainly due to results from Pennsylvania, which had the largest sample. For North 

Group Mi

Crashes 

After

Estimated CMF (standard error)

Total 

Crashes

Injury 

Crashes

ROR

Crashes

Wet-Road 

Crashes

Dry-Road 

Crashes

Wet-Road 

ROR

Crashes

California 416 9,525 1.060

(0.014)

1.032

(0.014)

0.974

(0.036)

0.997

(0.039)

1.068

(0.015)

0.807

(0.080)

North 

Carolina 42 2,231 0.748(0.028)

0.743

(0.049)

0.485

(0.083)

0.506

(0.036)

0.875

(0.038)

0.306

(0.077)

All Freeway 165 8,571 0.945

(0.015)

0.934

(0.025)

0.816

(0.041)

0.685

(0.031)

1.008

(0.017)

0.482

(0.066)

Freeway 

California 124 6,354 1.041(0.017)

1.004

(0.027)

0.873

(0.046)

0.920

(0.046)

1.055

(0.018)

0.643

(0.099)

Freeway 

North 

Carolina

41 2,217 0.747

(0.028) 

0.746) 

(0.049) 

0.481

(0.082) 

0.508

(0.036) 

0.873

(0.038) 

0.307

(0.076) 

All Multilane

(almost all 

California)

61 1,734 1.092

(0.036)

0.959

(0.051)

1.028

(0.100)

0.981

(0.086)

1.108

(0.039)

1.114

(0.246)

All TwoLane

(California

only)

232 1,451 1.109

(0.037)

1.128

(0.053) 

1.107

(0.067) 

1.038

(0.089) 

1.120

(0.162) 

0.878

(0.141) 

Group Mi

Crashes 

After

Estimated CMF (standard error)

Total 

Crashes

Injury 

Crashes

ROR

Crashes

WetRoad 

Crashes

DryRoad 

Crashes

WetRoad 

ROR

Crashes

California

(All Freeway) 5 119 0.776

(0.087)

0.746

(0.148)

0.674

(0.186)

2.034

(0.466)

(Few 

crashes)

0.615

(0.079)

1.311

(0.696)

(Few 

crashes)

98 
Carolina, the sample was small but there are weak indications of decreases on both wet- and dryroad crashes. For California, by contrast, the indication is that there was an increase in both wetand dry-road crashes for microsurfacing on two-lane roads. 

Table 29. Estimates of CMFs for microsurfacing treatment. 

CMF = Crash modification factor

ROR = Run-off road

For freeways, the results for microsurfacing were inclusive (i.e., there were no statistically 

significant effects), likely a result of the small sample size. For multilane roads, there was a 

decrease in wet-road crashes (significant at the 5-percent level) and a negligible effect on total 

and dry-road crashes.

Slurry Seal Results

For slurry seal, which was mostly on two-lane roads, almost all of which were in California, the 

results in table 30 indicate that there were benefits for wet-road crashes and weak (i.e., 

statistically insignificant) indications of a benefit for dry-road crashes.

Group Mi

Crashes 

After

Estimated CMF (standard error)

Total 

Crashes

Injury 

Crashes

ROR

Crashes

WetRoad 

Crashes

Dry-Road 

Crashes

Wet-Road 

ROR

Crashes

California 72 766 1.078

(0.049)

1.120

(0.067)

1.016

(0.094)

1.061

(0.153)

1.079

(0.052)

0.712

(0.217)

Minnesota 94 626 1.108

(0.065)

1.026

(0.092)

1.226

(0.136)

0.944

(0.127)

1.140

(0.074)

1.105

(0.247)

North Carolina 39 89 0.765

(0.090)

0.958

(0.158)

0.440

(0.186)

0.604

(0.160)

0.810

(0.106)

0.505

(0.366)

Pennsylvania 164 865 1.067

(0.045)

1.123

(0.062)

1.077

(0.117)

0.775

(0.070)

1.419

(0.065)

1.173

(0.219)

All Freeway 40 518 1.075

(0.071)

1.036

(0.103)

1.169

(0.152)

0.963

(0.128)

1.101

(0.084)

1.178

(0.278)

All Multilane 58 580 1.006

(0.052)

0.972

(0.071)

0.925

(0.125)

0.785

(0.116)

1.039

(0.058)

Few 

crashes(12)

All Two-Lane 273 1,263 1.090

(0.038)

1.180

(0.053)

1.114

(0.082)

0.867

(0.071)

1.142

(0.044)

1.018

(0.171)

Two-Lane 

California 46 443 1.300(0.076)

1.419

(0.110)

1.140

(0.122)

1.810

(0.314)

1.255

(0.077)

Few 

crashes(8)

Two-Lane 

Minnesota 43 23 Insufficient crashes

Two-Lane 

North Carolina 32 60 0.718(0.102)

0.838

(0.161)

Few 

crashes

0.516

(0.177)

0.769

(0.120) Few crashes

Two-Lane 

Pennsylvania 152 737 1.040(0.047)

1.099

(0.064)

1.088

(0.121)

0.761

(0.075)

1.129

(0.059)

1.122

(0.217)

99 
Table 30. Estimates of CMFs for slurry seal treatment. 

CMF = Crash modification factor

ROR = Run-off road

UTBWC Results

The results are shown in table 31. For freeways, there was a small and marginally significant 

benefit overall for wet-weather crashes, due largely to the California treatments, which had a 

substantial and significant benefit. There was no effect for dry weather and for total crashes 

when this is considered. 

On two-lane roads, there was a substantial and highly significant benefit for wet-road crashes 

and a smaller, but significant (10-percent level), benefit for dry-road crashes.

Table 31. Estimates of CMFs for UTBWC treatment. 

CMF = Crash modification factor

ROR = Run-off road

Group Mi

Crashes 

After

Estimated CMF (standard error)

Total 

Crashes

Injury 

Crashes

ROR

Crashes

WetRoad 

Crashes

DryRoad 

Crashes

Wet-Road 

ROR

Crashes

California 134 1,084 0.936

(0.037)

0.888

(0.052)

0.669

(0.059)

0.736

(0.091)

0.959

(0.039)

0.621

(0.143)

North Carolina 5 5 0.843

(0.403)

0.710

(0.520) Insufficient crashes

All Freeway 19 200 Insufficient crashes

All Multilane 15 192 Insufficient crashes

All Two-Lane 

(almost all 

California)

105 697 0.931

(0.044) 

0.972

(0.068) 

0.578

(0.067) 

0.802

(0.126) 

0.943

(0.047) Few crashes

Group Mi

Crashes 

After

Estimated CMF (standard error)

Total 

Crashes

Injury 

Crashes

ROR

Crashes

Wet-Road 

Crashes

Dry-Road 

Crashes

Wet-Road 

ROR 

Crashes

California 57 1,937 0.961

(0.027)

0.982

(0.046)

1.075

(0.098)

0.925

(0.083)

0.964

(0.029)

0.802

(0.208)

North Carolina 94 3,940 0.954

(0.019)

0.860

(0.032)

1.260

(0.093)

0.978

(0.043)

0.948

(0.021)

0.926

(0.109)

Pennsylvania 21 104 0.641

(0.073)

0.632

(0.100)

0.502

(0.198)

0.330

(0.082)

0.962

(0.118)

0.634

(0.381)

All Freeway 109 4,365 0.994

(0.019)

0.875

(0.031)

1.139

(0.070)

0.947

(0.041)

1.005

(0.021)

0.917

(0.102)

Freeway 

California 30 850 1.017(0.044)

1.061

(0.078)

1.170

(0.129)

0.761

(0.102)

1.049

(0.048)

0.896

(0.274)

Freeway North 

Carolina 69 3,484 0.994(0.021)

0.871

(0.036)

1.317

(0.100)

0.985

(0.046)

0.996

(0.024)

0.945

(0.113)

Freeway 

Pennsylvania 10 31 Insufficient crashes

All Multilane 21 103 Insufficient crashes

All Two-Lane 43 440 0.872

(0.051)

0.956

(0.081)

0.908

(0.169)

0.694

(0.103)

0.905

(0.058)

0.550

(0.254)

100 
Summary of Aggregate Results 

In summary, the combined results for all treatment types (except grooving, for which there were 

very few sites) suggest that the treatments resulted in benefits for wet-road crashes, with a few 

exceptions. The exceptions were for thin HMA on two-lane roads for both California and North 

Carolina, the two States with large enough samples for a definitive result, and for OGFC for twolane and multilane roads, for which the effect was negligible. 

For dry-road crashes, crashes increased for microsurfacing (except for North Carolina), thin 

HMA and OGFC on two-lane roads, and OGFC and chip seal on multilane roads. There were 

indications of a benefit for UTBWC, chip seal, and slurry seal on two-lane roads, and diamond 

grinding on freeways. 

The estimated CMFs for treatments by road and crash type may be considered for use in the 

Highway Safety Manual and the CMF clearinghouse.

Disaggregate Results

Effect of Age of Treatment

For some of the pavement treatments, it was of interest to investigate the possible change in 

safety effects as the pavement ages. Traffic and weather play a significant role in wearing 

pavements down over time, generally leading to a reduction in pavement texture and reduction in 

friction.(28) The cause can be a complex interaction of factors but intuitively, we understand that 

aggregates abrade, polish, and are broken off of the pavement surface, bituminous binders can 

bleed to the surface of a pavement over time, ruts can form, and porous surfaces can become 

clogged. Although there have not been many studies to confirm this link between treatment age 

and safety, the project team wanted to evaluate whether there is any correlation in the data 

analyzed. 

For the following identified treatments, the effect of age was investigated where the sample size 

allows for wet-road crashes:

• Chip seal on two-lane roads. 

• Chip seal disaggregated by single versus double/triple seal. 

• Diamond grinding on freeways. 

• OGFC on two-lane roads. 

• OGFC on freeways. 

Table 32 presents the CMF estimates for all years of data and for years 1 to 3 for chip seal on 

two-lane roads. The results indicate that the positive safety effect of chip seal treatment on wetweather crashes is greatest in the first year following treatment, with a declining benefit 

thereafter. This result is not entirely surprising for chip seal treatments. The two common 

“failure” mechanisms of chip seals are chip loss (raveling) and bleeding, both of which result in 

reduced surface texture and reduced friction, particularly in the wheelpaths where traffic has the 

most impact on the performance of the treatment. Figure 26 shows an example of an 

approximately 5-year-old chip seal on a heavily traveled roadway, with the loss of texture and 

friction apparent in the wheelpaths. Although it is not possible to say with certainty that this is 

101 
the explanation of the results observed from this study (because each treatment site was not 

specifically investigated), the trend is consistent with observed performance of chip seals over 

time. 

Table 32. Estimates of CMFs for chip seal treatment for wet-road crashes on two-lane 

roads by period after treatment. 

Group

Estimated CMF (standard error) by period after treatment

All Years Year 1 Year 2 Year 3

All TwoLane0.950

(0.035)

0.830

(0.055)

0.872

(0.060)

0.952

(0.067)

CMF = Crash modification function

Source: The Transtec Group, Inc.

Figure 26. Photo. Example of wear in wheelpaths over time for chip seal treatments, 

reducing surface texture and friction. 

Table 33 provides the results for chip seal on all road types disaggregated by single versus 

double/triple seal applications. Data on single/double/triple seal were only available for North 

Carolina and Pennsylvania. For single applications, there is some indication that the safety 

benefit is greater in the first year after treatment than in later years; however, there is no such 

trend for double/triple seals. 

102 
Table 33. Estimates of CMFs for single and multi-layer chip seal treatment for wet-road 

crashes (NC and PA only) by period after treatment. 

Chip Seal Type

Estimated CMF (standard error) by period after treatment

All Years Year 1 Year 2 Year 3

Single 1.015

(0.063)

0.845

(0.098)

1.115

(0.119)

1.029

(0.113)

Double/Triple 0.924

(0.055)

0.882

(0.098)

0.890

(0.102)

0.680

(0.097)

CMF = Crash modification function

Table 34 provides the results for diamond grinding on freeways. There is no clear time trend to 

be seen for the first 4 years.

Table 34. Estimates of CMFs for diamond grinding treatment for wet-road crashes on 

freeways by period after treatment. 

Group

Estimated CMF (standard error) by period after treatment

All Years Year 1 Year 2 Year 3 Year 4

Freeways 0.869

(0.036)

0.916

(0.054)

0.779

(0.058)

0.923

(0.074)

0.940

(0.077)

CMF = Crash modification function

Table 35 provides the results for OGFC on freeways and two-lane roads. For freeways, there 

appears to be a trend of a decreasing CMF (increasing benefit) as the pavement age increases for 

the first 4 years. For two-lane roads, however, the trend is the opposite, and the benefits are seen 

to decline as the pavement ages.

Table 35. Estimates of CMFs for OGFC treatment for wet-road crashes on freeways and 

two-lane roads by period after treatment. 

Group

Estimated CMF (standard error) by period after treatment

All Years Year 1 Year 2 Year 3 Year 4

Freeway 0.685

(0.031)

0.846

(0.050)

0.810

(0.051)

0.618

(0.051)

0.573

(0.060)

Two-Lane

1.038

(0.089)

0.975

(0.130)

1.148

(0.150)

1.237

(0.188) few crashes

CMF = Crash modification function

Effect of Other Factors

A thorough disaggregate analysis was undertaken in which multiple variable regression 

modeling was used to investigate the effects on the CMF of a number of factors, including 

AADT, precipitation, expected crash frequency before treatment, and environment (urban/rural). 

The primary objective was to investigate whether CMFunctions could be developed to capture 

the effects of these factors and more precisely estimate CMFs for prospective treatments.

In the end, the CMFunctions developed were not robust enough to recommend them. The 

direction of effect for attempted variables was not always consistent, and the statistical 

significance of estimated parameters tended to be poor. Nevertheless, there were useful insights 

103 
that suggest that it would be worthwhile to pursue the development of robust CMFunctions in 

future research. These insights suggest that there appears to be a relationship between CMFs and 

AADT and sometimes precipitation, urban versus rural setting, and crash frequency. However, 

the direction of the effect varies by crash type and treatment, so the future research will need to 

reconcile (i.e., explain), these apparent inconsistencies. 

Appendix A summarizes the approach to CMFunction development and presents some of the 

more promising results. 

104 
CHAPTER 7. HIGH FRICTION SURFACING TREATMENT

INTRODUCTION

The HFS treatment strategy was analyzed separately from the conventional pavements because 

of the nature of HFS treatments. Unlike the conventional treatments discussed in previous 

chapters, HFS is used almost exclusively for safety improvement (friction restoration or 

enhancement) purposes and not for pavement preservation or rehabilitation. In addition, with few 

exceptions, HFS treatments are used primarily for spot treatments of ramps or individual curves, 

rather than over longer sections of a roadway. As such, the data collection and analysis 

procedures differ somewhat from the conventional treatments. 

DATA COLLECTION

HFS is a relatively new pavement treatment in the United States, at least in terms of systemic 

use. A limited number of States (including ELCSI-PFS States) have HFS treatments, and there 

are generally only a few treatments in those States.

States with HFS treatments were identified by the project team, FHWA, and PFS contacts, and 

also through related efforts such as the FHWA Surface Enhancements at Horizontal Curves 

(SEAHC) study. Data requested for HFS treatments were the same as that summarized in 

Table 4, previously. However, challenges with data collection for these sites included the 

following: 

• Obtaining traffic data for ramps—Many States do not routinely collect traffic data on 

ramps, but rather just the mainline highway leading into or away from a ramp.

• Obtaining accurate crash data for ramps—Crash data for ramps can be difficult to 

obtain owing to inconsistencies in how the crash data are coded when recorded. They 

may be coded for the roadway (and associated milepoints) leading into the ramp or 

for the roadway leading away from the ramp. 

• HFS treatment information—Knowing what material was used (specifically the 

aggregate type), the exact limits of the treatment, and the dates of installation is 

needed. Many HFS treatments were installed as demonstration or trial projects, and 

detailed records were not available. Some may have been removed prematurely or 

overlaid with another treatment. 

• Identifying reference sites—HFS treatments are most commonly applied to curves or 

ramps with high crash rates that may be unique in geometry, location, traffic, etc. 

Finding similar sites to use as reference sites can be very difficult, if not impossible. 

Invariably, reference sites will likely have lower crash rates because the main 

criterion for selecting treatment locations was higher crash rates.

• Collecting roadway data for HFS sites—It was often necessary to use satellite 

imagery (Google EarthTM) to verify the limits of an HFS treatment, lane and shoulder 

widths, and radius of curvature. 

105 
• Friction data—Before and after friction data were available for the SEAHC sites, 

including data from 1 and 3 years after installation. Unfortunately, friction data were 

not available for the remaining sites or for any of the reference sites, precluding the 

use of friction data in the analysis. 

SUMMARY OF HFS TREATMENT DATA COLLECTION

Below are summaries of the data collection process for each of the volunteer States that provided 

candidate sites. The complete list of HFS treatment sites is provided in Appendix B. 

Colorado 

HFS treatment sites were on curves and were part of the FHWA SEAHC demonstration 

program. Treatment sites were originally selected by the Colorado Department of Transportation 

(CDOT) based on high crash rates at those curves. CDOT provided before and after crash data 

for the treatment sites. Roadway data (traffic volume, number of lanes, lane width, shoulder type 

and width, median type and width) were collected from the SEAHC project information and 

from an online CDOT roadway information database. 

Reference sites were selected as segments of the roadway upstream and downstream from the 

treatment sites. Segments were selected based on similar traffic volume, number and width of 

through lanes, shoulder type and width, and median type. CDOT provided crash data for the 

same before and after periods as the treatment sites for these segments of roadway. 

Kansas 

HFS treatment sites were part of the FHWA SEAHC demonstration program and included two 

curves and two ramps. Treatment sites were originally selected by the Kansas Department of 

Transportation (KDOT) based on high crash rates at those locations. KDOT provided before and 

after crash data for the treatment sites and traffic information. Roadway information (pavement 

type, number and width of lanes, shoulder type and width) was collected from the SEAHC 

demonstration project information. 

Reference sites were identified by KDOT based on similar roadway characteristics to the 

treatment sites (traffic and roadway geometry). KDOT provided crash data for the reference sites 

for the same before and after periods as the treatment sites. 

Kentucky

HFS treatment site data were provided by the Kentucky Transportation Cabinet (KTC). KTC 

provided the list of treatment locations and summary before and after crash data. Roadway 

information (traffic volumes, number and width of lanes, shoulder type and width, and median 

information) were collected from KTC’s online Highway Information System (HIS) database, 

and crash data were obtained from the Kentucky State Police Collision Analysis online database. 

Reference sites were identified using roadway data from the HIS database. For treatments on 

curves, segments of the roadway upstream and downstream from the treatment sites with similar 

characteristics (traffic volume, number and width of through lanes, shoulder type and width, and 

106 
median type) were identified as reference sites. For treatments on ramps, ramps with similar 

geometry in the vicinity of the treatment sites were selected. Crash data were collected through 

the Kentucky State Police Collision Analysis database. 

Michigan

HFS treatment sites were part of the FHWA SEAHC demonstration program and from various 

safety improvement projects by the Michigan Department of Transportation (MDOT). Treatment 

sites were originally selected by MDOT based on high crash rates at the curve and ramp 

locations identified. MDOT provided before and after crash data for the treatment sites as well as 

roadway information (traffic volume, underlying pavement type, and treatment length). 

Additional information for lane and shoulder widths was estimated using satellite imagery 

(Google EarthTM).

Reference sites were identified by MDOT based on similarity in roadway characteristics to the 

treatment sites. MDOT provided before and after crash data for the reference sites for the same 

time periods as the treatment sites. 

Montana 

HFS treatment sites were part of the FHWA SEAHC demonstration program. Treatment sites 

were originally selected by Montana Department of Transportation (MDT) based on high crash 

rates at the two locations. MDT provided before and after crash data for the treatment sites, while 

roadway data (traffic volume, number of lanes, lane width, shoulder type and width, median type 

and width) were collected from the SEAHC demonstration project information. 

Reference sites were identified by MDT based on similar roadway characteristics to the 

treatment sites. MDT provided crash data for the reference sites for the same before and after 

periods as the treatment sites.

South Carolina

HFS treatment sites were provided by the South Carolina Department of Transportation 

(SCDOT) from various safety improvement projects using HFS treatments. SCDOT provided the 

treatment site locations, before and after crash data, traffic, and underlying pavement 

information. 

Reference sites were identified by SCDOT based on similar roadway characteristics to the 

treatment sites. SCDOT provided reference site locations and before and after crash data for the 

selected reference sites. 

Tennessee

HFS treatment sites were identified by Tennessee Department of Transportation (TDOT) from 

safety improvement projects completed by TDOT. Six treatment locations were originally 

provided, but two were intersection approaches and not considered in the analysis. TDOT 

provided the treatment locations, and roadway information (traffic volumes, lane width, shoulder 

type and width, and median information) was collected from the online Tennessee Roadway 

107 
Information Management System (TRIMS) maintained by TDOT. Detailed before and after 

crash data were also obtained from the TRIMS database. 

Reference sites were identified using the roadway data from the TRIMS database. Segments of 

the same highway upstream and downstream from each of the treatment sites with similar 

characteristics (traffic volume, number and width of through lanes, shoulder type and width, and 

median type) were identified as reference sites. Before and after crash data were collected 

through the TRIMS database. 

Wisconsin 

One HFS treatment installed under the FHWA SEAHC demonstration program in 2011 was 

provided by the Wisconsin Department of Transportation (WisDOT). WisDOT provided before 

and after crash data for the treatment site, and roadway data (traffic volume, number of lanes, 

lane width, shoulder type and width, median type and width) were collected from the SEAHC 

demonstration project information. 

Other States

Various other HFS treatments were provided by several States, but were not included in the final 

analysis because of insufficient crash data, information on the treatment site itself, or a lack of 

reference sites.

California—Caltrans provided a list of 48 completed and planned HFS treatments in the State. 

Of those, seven were selected as potential candidates for analysis. Unfortunately, a lack of crash 

data for each of these sites (due in part to most being less than 2 years old) and a lack of 

reference sites precluded their use in the analysis.

Iowa—Four sites that were installed as part of safety improvement projects in 2012 by the Iowa 

Department of Transportation were identified. A lack of raw crash data for these sites and 

reference sites for the analysis precluded their use. 

Louisiana—One HFS site, installed as a safety improvement project in 2010, was provided by 

the Louisiana Department of Transportation. Crash data were provided for the site, as well as 

traffic and roadway information. However, because the treatment location was an elevated 

structure (bridge deck), and reference sites could not be identified, it was not included in the 

analysis.

Mississippi—One HFS site, installed as a safety improvement project in 2008, was identified by 

the Mississippi Department of Transportation, and before and after crash data were provided. 

However, a lack of reference sites (due to the unique characteristics of the treatment site) 

precluded its inclusion in the analysis. 

Texas—Two HFS sites installed as safety improvement projects, and tested under the FHWA 

SEAHC program, were identified for Texas. A lack of crash data and reference sites precluded 

the use of these sites in the analysis.

108 
West Virginia—West Virginia Department of Transportation provided a list of 24 horizontal 

curve HFS treatment sites installed as safety improvement projects. Before and after crash data 

and reference sites could not be obtained for these sites, and many of them were just over 1 year 

old, precluding their use in the analysis. 

SUMMARY OF HFS TREATMENT SITES

Table 36 and table 37 provide a summary of the treatment and reference site data that were 

collected and used in the study. 

Table 36. Summary statistics of HFS treatment site data collected. 

PCC = Portland cement concrete

Table 37. Summary statistics of HFS comparison site data collected. 

Site Type Sites by State

Sites by Road 

Classification

Sites by Pavement 

Type

Ramps

KS—14

MI—39

MT—8

SC—38

Urban—36

Rural—6

Unknown—49

Asphalt—23

PCC—36

Unknown—32

Curves

CO—8

KS—17

KY—117

MI—13

MT—13

SC—11

TN—27

unknown unknown 

PCC = Portland cement concrete

Site 

Type Sites by State

Sites by Road 

Classification

Sites by 

Pavement 

Type

Crashes 

per SiteYear 

Before

Crashes 

per SiteYear After

Wet-Road 

Crashes 

per SiteYear 

Before

Wet-Road 

Crashes 

per SiteYear After

Ramps

Kansas—2

Kentucky—2

Michigan—6

Montana—1

South Carolina—6

Wisconsin—1

Urban—17

Rural—1

Asphalt—12

PCC—5

Chip Seal—1

Min—0.00

Max—28.68

Mean—6.10

Min—0.00

Max—10.50

Mean—2.77

Min—0.00

Max—12.25

Mean—3.32

Min—0.00

Max—3.00

Mean—0.57

Curves

Colorado—2

Kansas—2

Kentucky—28

Michigan—1

Montana—1

South Carolina—1

Tennessee—4

Urban—4

Rural—35

Asphalt—38

Chip Seal—1

Min—0.25

Max—17.00

Mean—2.93

Min—0.00

Max—16.00

Mean—1.90

Min—0.00

Max—14.00

Mean—1.62

Min—0.00

Max—4.00

Mean—0.49

109 
ANALYSIS OBJECTIVES

Similar to the conventional treatments, the objective of this analysis was to estimate CMFs for 

the safety effect of HFS treatments using data from the States listed above. Treatment sites 

identified were either freeway ramps or individual curves. The treatments were generally applied 

because of a perceived problem with friction-related crashes.

The basic objective of the crash data analysis was to estimate the change in target crashes. Only 

nonintersection, nonanimal related crashes, and crashes not involving snow or ice were 

considered. Crash types examined total and wet-road crashes. Because of the limited sample 

sizes, other crash types were not investigated. Even for wet-road crashes, the sample size 

becomes small, but because these constituted the primary target crash type, they were analyzed 

separately. It should be noted that because HFS treatments are installed in a shorter period of 

time than conventional treatments, only the month in which the treatment was applied was 

masked off from the before and after periods, rather than the entire year.

ANALYSIS METHODOLOGY

The data collection for HFS treatments proved more difficult than for conventional treatments, 

particularly for treatments on ramps. For States that could provide untreated reference site data, 

the number of such sites was often limited, and, in many cases, traffic (i.e., AADT) information 

was missing. For South Carolina, fewer years of crash data were provided for reference sites than 

for treatment sites. 

The lack of available data prohibited the application of the robust EB before–after methodology 

at this time. In the interim, both naïve before–after and comparison group (C-G) before–after 

studies were conducted with the limited data available, and guarded conclusions made on the 

basis of the results, given the methodological issues with these studies. Even so, not all of the 

treatment sites with before and after data had comparison sites for a C-G study. Below is a 

description of the methodology for the naïve and C-G studies, taken from Gross et al.(44) 

Naïve Study Approach 

The simple before–after study, also referred to as the naïve before–after study, is a comparison of 

the number of crashes before and after treatment. The CMF for a given crash type at a treated 

site is estimated by first summing the observed crashes for the treatment site for the two time 

periods (assumed equal). The notation for these summations is summarized as follows: 

K = the observed number of crashes in the before period for the treatment group.

L = the observed number of crashes in the after period for the treatment group.

The expected number of crashes for the treatment group that would have occurred in the after 

period without treatment is estimated using the equation in figure 27: 

Figure 27. Equation. Estimated number of crashes that would have occurred in the after 

period with no treatment in the naïve study.

B = K(Years After/Years Before)

110 
The variance of B is estimated using the equation in figure 28: 

Figure 28. Equation. Estimated variance of B in the naïve study.

The CMF and its variance are estimated using the equations in figure 29 and figure 30. 

Figure 29. Equation. Estimated CMF in the naïve study.

Figure 30. Equation. Estimated CMF variance in the naïve study.

This method assumes that the number of crashes before the treatment is a good estimate of the 

expected crashes that would have occurred without the treatment. This assumption is in fact 

problematic because it does not take into account any other factors that can affect this estimate, 

such as changes in traffic volume and external causal factors. Most critically, sites that receive 

treatments such as HFS are typically selected on the basis of a high crash count, which 

introduces a regression to the mean (RTM) error whereby, without any treatment, the total 

number of crashes would have naturally declined in the after period. Thus, the results of a naïve 

before–after study can be biased toward overestimating the benefit of HFS treatment (i.e., 

underestimating the CMF).

C-G Study Approach

The before‐and‐after study using the C-G method is similar to the simple before‐and‐after study. 

It uses a comparison group of untreated sites to compensate for the external causal factors that 

could affect the change in the number of collisions. It does this by assuming that the ratio of 

crashes between the before and after period of the untreated sites would have been the same for 

the treated sites. Therefore, any external changes that would have changed the number of crashes

in the after period throughout the area would be accounted for. 

The CMF for a given crash type at a treated site is estimated by first summing the observed 

crashes for both the treatment and comparison groups for the two time periods (assumed equal). 

The notation for these summations is summarized as follows: 

K = the observed number of crashes in the before period for the treatment group.

L = the observed number of crashes in the after period for the treatment group. 

M = the observed number of crashes in the before period in the comparison group. 

N = the observed number of crashes in the after period in the comparison group.

The comparison ratio (N/M) indicates how crash counts are expected to change in the absence of 

treatment (i.e., owing to factors other than the treatment of interest). This is estimated from the 

comparison group as the number of crashes in the after period divided by the number of crashes 

in the before period. The expected number of crashes for the treatment group that would have 

occurred in the after period without treatment is estimated using the equation in figure 31: 

Var(B) = K(Years After/Years Before)2

CMF = (L/B)/{1 + [Var(B)/B2]}

Variance{CMF} = [CMF2{[1/L] + [Var(B)/B2]}/[1 + Var(B)/B2]2]

111 
Figure 31. Equation. Estimated number of crashes that would have occurred in the after 

period with no treatment in the C-G study.

If the comparison group is ideal, the variance of B is estimated using the equation in figure 32: 

Figure 32. Equation. Estimated variance of B in the C-G study.

The CMF and its variance are estimated as using the equations in figure 33 and figure 34: 

Figure 33. Equation. Estimated CMF in the C-G study.

Figure 34. Equation. Estimated CMF variance in the C-G study.

This method, like the naïve method, does not account for RTM because it does not account for 

the natural reduction in crashes in the after period that would occur for the sites with abnormally 

high numbers of crashes, which would characterize the sites typically selected for HFS 

treatments. Thus, again, the results would likely be biased toward overestimating the benefit of 

HFS treatment (i.e., underestimating the CMF).

RESULTS

Results are provided in table 38 and table 39 for both the naïve and C-G studies. As mentioned 

earlier, not all treatment sites could be analyzed using the C-G method because reference sites 

were either unavailable or lacked the required data.

As noted, the results from applying these two methods are likely biased toward underestimating 

the CMF, and thereby exaggerating crash reductions, because RTM is likely at play and is not 

accounted for. An approximate method for resolving this problem has been suggested in the 

process of developing CMFs for the Highway Safety Manual.

(45)

 That report suggests (on page 7) 

that “for a large RTM bias, where only a few sites with the highest crash frequency were treated 

out of the total population and few years of before-crash data were included in the evaluation 

study,” the biased CMF should be corrected by multiplying it by a factor of 1.25. That 

recommendation seems appropriate for this evaluation, so this correction of 1.25 was applied to 

the biased CMFs. The CMFs with this RTM correction are shown in addition to the biased ones 

in table 38 and table 39. These indicate that HFS treatments have a substantial beneficial impact 

on safety, especially for wet-road crashes.

B = K(N/M)

Var(B) = B2(1/K + 1/M + 1/N)

CMF = (L/B)/{1 + [Var(B)/B2]}

Variance{CMF} = [CMF2{[1/L] + [Var(B)/B2]}/[1 + Var(B)/B2]2]

112 
Table 38. Results for the naïve before–after study based on all sites. 

Group

No. of 

Sites

Crashes 

After

Wet-Road 

Crashes 

After

CMF

(and standard error) for 

Total Crashes

CMF

(and standard error) 

for Wet-Road Crashes

Biased

With HSM 

RTM 

Correction Biased

With HSM 

RTM 

Correction

All Ramps 27 111 19 0.387

(0.041) 0.484 0.169(0.041) 0.211

All Curves 43 104 45 0.502

(0.052) 0.628 0.298(0.048) 0.373

CMF = Crash modification function

HSM = Highway Safety Manual

RTM = Regression to the mean

Table 39. Results for the before–after C-G study for treatment sites for which comparison 

sites were available. 

Group

No. 

Sites

Crashes 

After

Wet-Road 

Crashes 

After

Total Crashes—C-G Wet-Road Crashes—C-G

Biased

With HSM 

RTM 

Correction Biased

With HSM 

RTM 

Correction

Ramps 12 77 8 0.522

(0.092) 0.653 0.111(0.042) 0.139

Curves 35 104 45 0.607

(0.067) 0.759 0.385(0.064) 0.481

CMF = Crash modification function

HSM = Highway Safety Manual

RTM = Regression to the mean

CONCLUSIONS FOR HFS TREATMENT

This analysis was limited because there were insufficient treatment and reference group data to 

conduct a state-of-the-art EB analysis. Naïve before–after study results for all treatment sites, 

and those of a C-G study of treatment sites for which comparison sites were available, were 

obtained. These results are likely biased because the HFS treatments sites were likely selected on 

the basis of high crash counts, resulting in RTM that was not accounted for with the less rigorous 

methods that could be applied. A correction based on a method used to obtain Highway Safety 

Manual CMFs from similarly biased studies was applied as an approximation. The corrected 

results suggest that HFS can be a highly effective safety treatment whose implementation should 

continue. 

Deployment of HFS as a safety countermeasure for curves and ramps is continuing in many 

States. It is strongly recommended that additional data be collected to conduct a robust EB 

before study to derive a CMF that could be recommended to practitioners and for which a BC

ratio could be confidently estimated. The future data collection should, where possible, focus on 

those States with available traffic counts in both the before and after periods and that can identify 

appropriate reference sites.

113 

CHAPTER 8. BC ANALYSIS

METHODOLOGY

The BC analysis provides a method for a comparison of the various pavement treatment options. 

By considering the cost of each treatment, typically just the installation cost, and the benefit, 

quantified in terms of crash reduction and lifespan, agencies will be equipped to better justify 

selection of one treatment over another. 

Separate analyses are provided for treatments and States for which the sample size was large 

enough and for which there was a statistically significant (5-percent level) benefit for total 

crashes. The annualized cost of the treatment is first computed using the equation in figure 35: 

Figure 35. Equation. Annualized cost pavement treatment.

Where:

C = Treatment cost. 

R = Discount rate (as a decimal). 

N = Expected service life (years).

Quantifying the actual treatment cost is one of the more difficult aspects of this analysis. 

Treatment costs vary widely from State to State and even within each State. Although statewide 

bid averages provide an estimate of the cost of the treatment itself, they do not account for any 

ancillary costs associated with the treatment (e.g., design, inspection, mobilization, maintenance 

of traffic). Therefore, calculated treatment costs were based on cost ranges published in a recent 

SHRP2 research report on pavement preservation treatments.(31) The SHRP2 report also provides 

a range for service lives of the various treatment types. For the purposes of this analysis, low-end 

to median values were used for service life based on a conservative assumption that 

rehabilitation cycle is longer than the period over which safety benefits are achieved. There is 

some support for this assumption in the results of the limited investigation of treatment effects 

over time.

Based on information from the Office of Management and Budget, the following real discount 

rates, based on the service life of a treatment, were used to determine the annual cost of the 

treatment.

(46) 

• Chip seal, HFS, and slurry seal: 5 years; -0.8 percent. 

• Thin HMA, OGFC, and UTBWC: 6 years; -0.6 percent (interpolated). 

• Diamond grinding: 8 years; -0.2 percent (interpolated). 

The most recent FHWA mean comprehensive crash costs are based on 2001 dollar values.(47) As 

recommended in that report, if crash costs are required for another year, the recommended 

adjustment procedure is to multiply the human capital costs provided in the tables by a ratio of 

the Consumer Price Index (CPI)—all items—for the year of interest divided by the CPI for 2001. 

Annual Cost = C ⋅ R

1 − (1 + R)N

115 
Based on the appropriate CPI information from the U.S. Department of Labor, the ratio of the 

2013 to the 2001 CPI is 230.08/175.1 = 1.314.(48) The 2001 unit costs for property damage only 

and fatal+injury crashes from the FHWA report were multiplied by this ratio and then weighted 

by the frequencies of these two crash types for a group in the after period to obtain and aggregate 

2013 unit cost for total crashes. 

The total crash reduction was calculated for each treatment/State group by subtracting the actual 

crashes in the after period from the expected crashes in the after period had the treatment not 

been implemented. The number of crashes saved per year was obtained by dividing the total 

crash reduction by the average number of after period years per site. The annual benefit (i.e., 

crash savings) is the product of the total crash reduction per year and the aggregate cost of a 

crash (all severities combined). The BC ratio is calculated as the ratio of the annual benefit to the 

annual cost. 

CONVENTIONAL TREATMENTS

Table 40 summarizes the results of the economic analysis of the conventional pavement 

treatment strategies evaluated in this study with a robust EB analysis. As noted earlier, this 

analysis was done for treatments and States for which the sample size was large enough and for 

which there was a statistically significant (5-percent level) benefit for total crashes. 

Table 40. Results of BC analysis for conventional treatment groups with statistically 

significant crash reductions. 

Treatment

(State) Road Type Cost/Lane-Mi Sites Mi

Crash

Reduction

per Year

Cost/

Crash

BC 

Ratio

Chip Seal (All) Two-Lane $12,320/layer 

(conventional)

$21,120

(rubberized or 

polymermodified)

5,770 2,448 107.16 $106,905 0.69

Chip Seal

(California only) Two-Lane 1,432 863 91.27 $107,687 2.06

Diamond

Grinding (All) Freeway $25,570 691 141 206.67 $77,408 5.95

Thin HMA

(North Carolina

only) Multilane $31,680 1,411 201 174.60 $74,695 3.01

OGFC (All)

Freeway $39,000

453 165 147.90 $74,633 2.10

OGFC (North 

Carolina only) 105 41 184.61 $64,173 9.15

Slurry Seal (All) Two-Lane $15,000* 248 105 14.41 $95,587 2.25

UTBWC (All) Two-Lane $35,200 187 43 18.78 $96,197 3.60

*Used the cost provided by California because almost all sites are from that State 

BC = Benefit-cost

HMA = Hot mix asphalt

OGFC = Open grade friction course

UTBWC = Ultrathin Bonded Wearing Course

116 
HFS TREATMENT

Although it was not possible to conduct a rigorous EB analysis for the HFS treatments, a cursory 

economic analysis was undertaken based on the results in table 39 for the ramps and curves for 

which a comparison group study was possible. The methodology used above for the other 

treatments was used, with the following conservative estimates:

• Service life: 5 years (typical range: 5–7 years). 

• Installation cost: $35/square yd (typical range: $25–$35/square yd). 

• Cost per crash: $64,173 (based on the lowest value in table 40). 

The results suggest the following BC cost ratios for HFS treatments:

• 3.97 (curves). 

• 11.88 (ramps). 

117 

CHAPTER 9. DISCUSSION AND CONCLUSIONS

The research was broad in scope, covering several treatments in several States, and with many 

variations in applications, but was nevertheless groundbreaking in that there is a dearth of 

definitive results on the safety effects of various pavement improvement treatments. It would be 

beneficial for the future research to now focus on individual treatments to isolate the application 

types and circumstances that are most cost-effective for safety. The results of this study will be 

useful in guiding such future efforts. 

SUMMARY

The objective of the study was to estimate the effect of various low-cost pavement treatments on 

crashes by evaluating a variety of treatments from several states. The state-of-the-art EB before–

after methodology was applied to evaluate the effects on various crash types (total, injury, wet 

road, dry road, wet-road ROR, and all ROR) of the following treatments, based on data from 

California, North Carolina, Pennsylvania, and Minnesota: 

• Chip Seal (single and double layer). 

• Diamond Grinding (concrete pavement only). 

• Grooved Concrete Pavement. 

• Microsurfacing (asphalt and concrete pavement). 

• OGFC (asphalt and concrete pavement). 

• Slurry Seal (asphalt pavement). 

• Thin HMA (asphalt and concrete pavement). 

• UTBWC (asphalt and concrete pavement). 

A preliminary, simple before–after evaluation was completed for HFS treatments based on 

limited data from several States. These data were insufficient to apply the EB method. 

The combined results for all treatment types subjected to the rigorous EB evaluation (except 

grooving, for which there were very few sites) suggest that the treatments resulted in benefits for 

wet-road crashes, with the exception of thin HMA for two-lane roads for both California and 

North Carolina, the two states with large enough samples for a definitive result, and for OGFC 

for two-lane and multilane roads, for which the effect was negligible. 

For dry-road crashes, crashes increased for microsurfacing on two-lane roads (except for North 

Carolina), thin HMA and OGFC on two-lane roads, and OGFC and chip seal on multilane roads; 

there were indications of a benefit for UTBWC, chip seal, and slurry seal on two-lane roads, and 

for diamond grinding on freeways.

The CMFs for treatments by road and crash type may be considered for use in the Highway 

Safety Manual and the CMF Clearinghouse. 

A thorough disaggregate analysis of the before–after evaluation data was undertaken in which 

regression analysis was used to investigate the effects on the CMFs of a number of variables, 

including AADT, precipitation, expected crash frequency before treatment, environment 

(urban/rural), and treatment age. In the end, the CMFunctions developed were not robust enough 

119 
to recommend them. Nevertheless, there were useful insights that suggest that it would be 

worthwhile to pursue the development of robust CMFunctions in future research. The results did 

suggest that there is a relationship between CMFs and AADT and sometimes precipitation, urban 

versus rural setting, and expected crash frequency. However, the direction of the effect is not 

always consistent, varying by crash type, site type, and treatment. Future research is needed to 

reconcile (i.e., explain) these apparent inconsistencies. 

An economic analysis was conducted for treatments and States for which the sample size was 

large enough and for which there was a statistically significant (5-percent level) benefit for total 

crashes based on the EB evaluation. The results indicate that BC ratios larger than 2.0, 

considering impacts on safety only, are attainable for the following situations: 

• Chip seal on two-lane roads (California only). 

• Diamond grinding on freeways. 

• Thin HMA on multilane roads (North Carolina only). 

• OGFC on freeways. 

• Slurry seal on two-lane roads. 

• UTBWC on two-lane roads. 

For other treatments/road classes/States, sample sizes were too small in some cases and, in other 

cases, overall safety benefits were not achieved or were statistically insignificant. 

For HFS treatments, the results of the basic before–after analysis suggest that HFS can be a 

highly safety- and cost-effective treatment for which implementation should continue. It is 

strongly recommended that additional data be collected to conduct a robust EB before study to 

derive a CMF that could be recommended to practitioners and for which a BC ratio could be 

confidently estimated. 

RELATING RESULTS TO PAVEMENT FACTORS

Several of the results from this analysis may not be intuitively obvious. Unfortunately, without 

very detailed information on the specific characteristics (friction, texture, pavement condition, 

etc.) of each particular pavement section included in this analysis, it is not possible to draw 

definitive conclusions for these observations. However, this section postulates some possible 

explanations for these results. It should be noted that these points of discussion are observations 

of the researchers and should not be construed as documented conclusions. 

When looking at the impact of pavement treatments on crashes, potential changes in driver 

behavior or driver response must be considered in addition to the effects of the treatment on 

pavement surface characteristics (texture and friction). Some potential driver responses to these 

treatments include the following: 

• Smoother and/or quieter pavement may lead to higher speeds, particularly for local 

drivers who are accustomed to the roadway and may have a sense that it is safer to 

drive faster with the new treatment in place.

(27)

 

120 
• Similarly, improvement to pavement condition (e.g., elimination of cracking, rutting, 

etc. and improvement in friction) when a pavement treatment is applied can 

potentially lead to higher speeds. 

• Porous surfaces are known to reduce splash and spray, thereby improving visibility in 

wet weather, potentially leading to a reduction in wet-weather crashes. 

In the following sections, the results reached in the study and described earlier in the report 

appear in indented blocks, followed by the possible explanations or observations by the 

researchers.

Chip Seals

For multilane roads, there are significant benefits overall for wet-road crashes, due 

largely to reductions in California. There was an estimated increase in dry-road crashes 

on these roads, which contributed to a significant (5-percent level) increase in total 

crashes. 

For chip seal on two-lane roads, there was a small benefit overall (significant at the 10-

percent level) for wet-road crashes due mainly to reductions in California and North 

Carolina. For dry-road crashes, there was a small benefit overall (significant at the 5-

percent level) due mainly to reductions in California and Pennsylvania. These benefits 

contribute to an overall benefit for all crashes and States combined for chip seal on twolane roads.

Given the aggressive nature of chip seals (good macrotexture and friction), a decrease in wetroad crashes is not surprising. However, the increase in dry-road crashes and the difference 

between multilane and two-lane roads is not readily explainable from a pavement perspective. 

Diamond Grinding

For diamond grinding, there was an overall benefit (significant at the 5-percent level) for 

both wet- and dry-road crashes, which resulted in a significant overall benefit for total 

crashes.

Concrete pavements are usually diamond ground later in their life, when the original pavement 

texture may be substantially worn or polished. Diamond grinding gives the pavement renewed 

texture and improved friction, and therefore could explain this benefit for crashes. 

Thin HMA Overlay

For thin HMA, there were benefits (significant at the 5-percent level) for wet-road 

crashes for multilane roads and freeways and no effect overall for dry-road crashes. (For 

the latter crash type, there was an increase in California and a decrease in North Carolina, 

both results significant at the 5-percent level).

A possible explanation for wet-road crash reduction is that the overlay may have eliminated 

rutting and/or flushing that existed in the old pavement. Ruts tend to hold water and can lead to 

121 
more wet-weather crashes, while flushing can significantly reduce friction in the wheelpaths, 

particularly in wet weather. Multilane and freeways (with presumably higher AADT) would 

likely exhibit more rutting and flushing. This has not been formally documented in any previous 

research, however. 

For two-lane roads, the thin HMA treatment was associated with highly significant 

increases overall in both wet- and dry-road crashes, a pattern that was consistent between 

California and North Carolina, the two States with large enough samples for a definitive 

result.

A new asphalt overlay, which would likely improve smoothness, could possibly lead to higher 

speeds and possibly more crashes in wet or dry weather. This increase in speed with 

improvement in pavement condition has not been formally researched, but has been postulated 

elsewhere.

(27,49) There is also the possibility that the cumulative effect of multiple thin overlays 

over time could lead to a nonrecoverable side-slope at the edge of the pavement, particularly on 

two-lane roads without paved shoulders. Again, however, this has not been formally documented 

in any previous research. 

OGFC

For OGFC, there was a negligible effect on wet-road crashes for multilane and two-lane 

roads, but increases in dry-road crashes resulted in significant increases (5-percent level) 

in total crashes for these road types. By contrast, for freeways, there was a small but 

significant (5-percent level) decrease in total crashes, due in large part to highly 

significant and substantial reduction in wet-road crashes with no change in dry-road 

crashes for California and North Carolina combined. 

Similar to the thin HMA overlay treatment, a smoother (and presumably quieter) OGFC may 

lead to higher speeds and potentially more crashes for multilane and two-lane roads. For 

freeways, OGFC could also possibly reduce splash and spray on heavily traveled freeways, 

reducing wet-weather crashes due to poor visibility. 

Microsurfacing

For microsurfacing on two-lane roads, there was a decrease in wet-road crashes and an 

increase in dry-road crashes overall (both results significant at the 5-percent level) 

resulting in a net increase in total crashes that was also significant at the 5-percent level. 

This trend was mainly due to results from Pennsylvania, which had the largest sample. 

For North Carolina, the sample was small but there are weak indications of decreases on 

both wet- and dry-road crashes. For California, by contrast, the indication is that there 

was an increase in both wet- and dry-road crashes for microsurfacing on two-lane roads. 

For freeways, the results for microsurfacing were inclusive, (i.e., there were no 

statistically significant effects), likely a result of the small sample size. For multilane 

roads, there was a decrease in wet-road crashes (significant at the 5-percent level) and a 

negligible effect on total and dry-road crashes.

122 
The decrease in wet-road crashes is not surprising because microsurfacing is known as a 

treatment to help improve skid resistance. There is no readily available explanation for the 

increase in dry-road crashes, however. 

Slurry Seal

For slurry seal, which was mostly on two-lane roads, almost all of which were in 

California, there were benefits for wet-road crashes and weak (i.e., statistically 

insignificant) indications of a benefit for dry-road crashes.

Similar to microsurfacing, slurry seal is known to help improve skid resistance and therefore 

would be expected to exhibit benefits for wet-road crashes.

UTBWC

For UTBWC treatment on freeways, there was a small and marginally significant benefit 

overall for wet-weather crashes, due largely to the California treatments, which had a 

substantial and significant benefit. There was no effect for dry weather and for total 

crashes when this is considered. 

On two-lane roads, there was a substantial and highly significant benefit for wet-road 

crashes and a smaller, but significant (10-percent level) benefit for dry-road crashes.

A UTBWC is similar in nature to a thin HMA overlay, and therefore similar results might be 

expected. The difference in effect for two-lane roads and freeways, however, is not readily 

explainable. 

HFS

For HFS treatments, the results of the cursory before–after analysis suggest that HFS can 

be a highly safety- and cost-effective treatment.

The crash reduction observed for the HFS treatment sites is not surprising as this treatment is 

applied specifically as a safety treatment to problem locations with high crash rates, particularly 

ROR crashes. The higher crash reduction for wet-road crashes over total crashes is also not 

surprising as this treatment provides significant improvement to both microtexture and 

macrotexture of existing pavement, which is particularly important for wet-road friction. 

Age of Treatment—Chip Seal

CMF estimates for all years of data and for years 1 to 3 for chip seal on two-lane roads 

indicate that the positive safety effect of chip seal treatment on wet-weather crashes is 

greatest in the first year following treatment, with a declining benefit thereafter.

Although it is not possible to say with certainty that this is the explanation of the results observed 

from this study, as discussed previously, the trend is consistent with the performance of chip 

seals over time—reduced friction as the treatment ages due to bleeding and/or raveling. 

123 
Data on single/double/triple seal were only available for North Carolina and 

Pennsylvania. For single applications, there is some indication that the safety benefit is 

greater in the first year after treatment than in later years; however, there is no such trend 

for double/triple seals. 

This trend for double/triple seals could be the result of improved performance (e.g., reduced chip 

loss/bleeding over time) over a single chip seal, owing to the thickness of double/triple seals. 

Age of Treatment—OGFC

For freeways, there appears to be a trend of a decreasing CMF (increasing benefits) as the 

pavement age increases for the first 4 years. For two-lane roads, however, the trend is the 

opposite, and the benefits are seen to decline as the pavement ages.

The trend observed for two-lane roads is closer to what might be expected from OGFC because

the treatment may clog over time, reducing its porosity and effectiveness in draining water from 

the surface. There is no clear explanation for the contrary effect on freeways.

124 
ACKNOWLEDGMENTS

The authors would like to gratefully acknowledge the contributions from FHWA and the various 

ELCSI PFS States that provided valuable input for the completion of this study. The authors 

specifically acknowledge the following PFS and non-PFS States for providing data that were 

used or considered for use in the completion of the study: California, Colorado, Connecticut, 

Iowa, Kansas, Kentucky, Louisiana, Michigan, Minnesota, Mississippi, Montana, North 

Carolina, Pennsylvania, South Carolina, Tennessee, Wisconsin, and West Virginia. 

125 

APPENDIX A—INVESTIGATION OF THE DEVELOPMENT OF CMFUNCTIONS

OBJECTIVES

A thorough disaggregate analysis of the before–after evaluation data was undertaken in which 

regression analysis was used to investigate the effects on the CMFs of a number of variables, 

including AADT, precipitation, expected crash frequency before treatment, and environment 

(urban/rural). The primary objective was to investigate whether CMFunctions could be 

developed to capture the effects of these factors and more precisely estimate CMFs for 

prospective treatments.

If successful, such CMFunctions would allow a user to apply a more accurate CMF that better 

reflects the specific site characteristics than an average value.

METHODOLOGY

The methodology investigated was a relatively new and innovative approach that models the 

values of the CMF using weighted linear regression.(50) Because of the preponderance of sites 

with zero crashes and short segment lengths, each individual segment could not be used as an 

observation. Rather, all segments were grouped together by ranges of the variables being 

modeled and then used to estimate a CMF and its variance for that group. Segments were not 

aggregated across States because applications may vary across States in unknown ways. Also, 

consistency in results across States would be indicative of the accuracy of the results. 

For example, if the model were only to consider an urban versus rural environment, then all 

urban sites would be used to estimate a CMF and its variance, and the same would be done for 

rural sites. This would be done separately for each State. Then the weighted linear regression 

model would be estimated using these estimates of the CMF as the dependent variable and a 

categorical variable to represent urban versus rural settings as the independent variable. The 

regression weights are assigned as the inverse of the variance of the CMF estimate.

The variable definitions for those independent variables considered are described below. For 

those variables that are continuous in nature (i.e., AADT and precipitation), the weighted mean 

for each category was used as the independent variable. The weights applied are the mile-years 

of after period data for each segment. The cutoff points for defining categories for the continuous 

variables were determined in an iterative manner and considering the goodness-of-fit of the 

estimated models and the number of observations in each category. Following are the variable 

specifications so obtained. 

RURURB

if URBAN then rururb = 1. 

if RURAL then rururb = 0. 

PTYPE

if concrete pavement then ptype = 1. 

if asphalt pavement then ptype = 0. 

127 
AADT Categories for Freeways (after period AADT) 

if AADT >= 0 and AADT < 20,000 then volcat *= 1. 

if AADT >= 20,000 and AADT < 40,000 then volcat = 2. 

if AADT >= 40,000 and AADT < 60,000 then volcat = 3. 

if AADT >= 60,000 and AADT < 80,000 then volcat = 4. 

if AADT >=80,000 and AADT < 100,000 then volcat = 5. 

if AADT >= 100,000 then volcat = 6. 

*Weighted mean AADT for each category is used as the independent variable. 

AADT Categories for Multilane Roads 

if AADT >= 0 and AADT < 10,000 then volcat *= 1. 

if AADT >= 10,000 and AADT < 20,000 then volcat = 2. 

if AADT >=20,000 and AADT < 30,000 then volcat = 3. 

if AADT >= 30,000 and AADT < 40,000 then volcat = 4. 

if AADT >= 40,000 and AADT <50,000 then volcat = 5. 

if AADT >= 50000 then volcat = 6. 

*Weighted mean AADT for each category is used as the independent variable

AADT Categories for Two-Lane Roads 

if AADT >= 0 and AADT < 5,000 then volcat* = 1. 

if AADT >= 5,000 and AADT < 10,000 then volcat = 2. 

if AADT >= 10,000 and AADT < 15,000 then volcat = 3. 

if AADT >= 15,000 then volcat=4. 

*Weighted mean AADT for each category is used as the independent variable 

Precipitation Categories (the 5-year average precipitation in after period) 

if precip <= 30 then prec = 1. 

if precip > 30 and __yr_precip <= 40 then prec* = 2. 

if precip > 40 and __yr_precip <= 45 then prec = 3. 

if precip > 45 and __yr_precip <= 50 then prec = 4. 

if precip > 50 then prec = 5. 

*Weighted mean precipitation for each category is used as the independent variable. 

ACCRATE

accrate= sum of expected crashes after without treatment/sum of mile-years of after-period data. 

128 
RESULTS

Models were attempted separately for all crash types and all road types (freeway, multilane, and 

two-lane) with varying success. Given the data demands for even estimating a single average 

CMF, it is perhaps not surprising that estimating several CMFs for categorized subsets of the 

same data proved challenging. 

Nevertheless, in general, the results did suggest that there is a relationship between CMFs and 

AADT and sometimes precipitation, urban versus rural setting, and expected crash frequency. 

However, the direction of the effect is not always consistent, varying by crash type, site type, and 

treatment. Future research will need to reconcile (i.e. explain) these apparent inconsistencies.

Some of the more promising results are provided below to illustrate the potential for developing 

CMFunctions for pavement treatments. It is not, however, recommended to use these models for 

estimating CMFs. Rather the aggregate CMFs in chapter 6 are recommended at the current time. 

Thin HMA—Freeway—Total Crashes 

For thin HMA treatments on freeways, the model in figure 36 was estimated for total crashes, 

and table 41 presents the results. 

Figure 36. Equation. Model estimated for total crashes on thin HMA treatments on 

freeways.

Table 41. Results for model for total crashes on thin HMA treatments on freeways.

Parameter Estimate

(standard error)

a 0.6720

(0.0954)

b 0.0221

(0.0065)

R-squared 0.5133

The results indicate that the CMF value increases with increasing AADT, meaning that the 

treatment is more effective at locations with lower AADTs.

OGFC—Two-Lane—Total Crashes 

For OGFC treatments on two-lane roads, the model in figure 37 was estimated for total crashes

and table 42 presents the results. 

Figure 37. Equation. Model estimated for total crashes on OGFC treatments on two-lane 

roads. 

CMF = a + b(AADT/10000)

CMF = a + b(AADT/10000) + c(precip)

129 
Table 42. Results for model for total crashes on OGFC treatments on two-lane roads. 

Parameter

Estimate

(standard error)

a 1.33347

(0.1869)

b -0.0581

(0.0823)

c -0.0100

(0.0078)

R-squared 0.4014

The results indicate that the CMF value decreases with increasing AADT, meaning that the 

treatment is more effective at locations with higher AADTs. The model also indicates that the 

CMF decreases at higher levels of precipitation, indicating that the treatment is more effective in 

areas with higher precipitation. The parameter estimates for the model, however, are of low 

statistical significance.

Diamond Grinding—Freeway—Total Crashes 

For diamond grinding treatments on freeways, the model in figure 38 was estimated for total 

crashes, and table 43 presents the results. 

Figure 38. Equation. Model estimated for total crashes on diamond grinding treatments on 

freeways.

Table 43. Results for model for total crashes on diamond grinding treatments on freeways. 

Parameter

Estimate

(standard error)

a 1.0800

(0.0415)

b -84.0876

(36.53)

c -0.0202

(0.2262)

R-squared 0.5514

The results indicate that the CMF value decreases with increasing AADT, meaning that the 

treatment is more effective at locations with higher AADTs. The model also indicates that the 

CMF decreases at higher levels of precipitation, indicating that the treatment is more effective in 

areas with higher precipitation. The parameter estimates for the precipitation variable is of low 

statistical significance however.

CMF = a + b(AADT/10000) + c(precip)

130 
APPENDIX B—LIST OF HFS TREATMENT SITES PROVIDED BY STATE

Table 44. Treatment sites by State, part 1.

State County Route Dir

Milepost 

Begin

Milepost 

End

Site 

Type Location Description

Area 

Type

Number 

Lanes

Radius of 

Curvature

Type of 

Curve

Friction Data 

(DFT 20 kph)

Pre-HFS

KS Johnson I-35 NB 28.487 28.487 ramp

Ramp from NB I-35 to 

NB I-635 urban 1 410 simple 0.519 

KS Leavenworth K5 Both 6.141 6.141 curve Curve on K5 rural 2 215 simple 0.283 

KS Sedgwick K96 EB 36 36 ramp

Ramp from EB K96 to 

EB US 54 urban 1 280 compound 0.422 

KS Wabaunsee K99 Both 36.887 36.887 curve Curve on K99 rural 2 1190 simple 0.381 

CO Boulder US36 Both 10.92 11.09 curve Curve on US 36 rural 2 330 simple 0.541 

CO Boulder CO119 Both 30.48 30.63 curve Curve on CO 119 rural 2 380 simple 0.73 

MT Missoula US93 SB 85.8 86.2 curve SB lanes for US 93 rural 2 910 simple 0.783 

MT Silver Bow I-15 NB 121 121.5 ramp

Ramp from NB I-15 to 

WB I-90 rural 1 150 compound 0.698 

MI Oakland I-75 NB 0 0.166 ramp

Ramp from NB I-75 to 

SB Baldwin Rd. urban 1 220 simple 0.491 

MI Oakland I-75 NB 0.168 0.21 ramp

Right and middle lane 

of curve along ramp 

from NB I-75 to 

Rochester Rd. urban 2 215 simple 0.359 

MI Genesee I-69 WB 0.194 0.497 ramp

Ramp from WB I-69 to 

SB I-75 urban 1 265 compound 0.403 

MI Kent I-96 WB 0 0.127 ramp

Ramp from WB I-96 to 

NB US 131 urban 1 310 simple 0.466 

MI Wayne M39 NB 0.013 0.037 ramp

First part of ramp from 

NB M-39 Ramp to EB 

US-12 urban 1 115 simple — 

MI Macomb I-94 EB 0.496 0.523 ramp

Portion of ram from EB 

I-94 ramp to NB Gratiot 

Ave urban 1 675 simple — 

MI Delta M35 EB 0.606 0.701 curve

Downhill curve on 

M-35 urban 1 515 simple — 

KY Jefferson KY-1142 Both 0.343 0.408 curve Single curve urban 2 360 s-curve — 

KY Jefferson KY-22 WB 1.38 1.46 curve Single curve urban 1 515 simple — 

KY Jefferson KY-22 WB 2.893 2.971 curve Single curve urban 1

390 EB/ 

340 WB simple — 

KY Oldham KY-22 Both 4.385 4.47 curve Single curve rural 2 475 simple — 

KY Fayette KY-922 Both 7.815 7.876 curve Single curve rural 2 390 simple — 

KY Garrard KY-1295 Both 2.788 2.908 curve Single curve rural 2 675 simple — 

KY Garrard KY-1295 Both 2.933 3.062 curve Single curve rural 2 745 simple — 

KY Garrard KY-1295 Both 4.579 4.753 curve Single curve rural 2 560 simple — 

KY Garrard KY-1295 Both 4.773 5.073 curve Single curve rural 2 575 simple — 

KY Jessamine KY-29 Both 6.438 6.516 curve Single curve rural 2 1100 simple — 

131 
State County Route Dir

Milepost 

Begin

Milepost 

End Type

Site 

Location Description Type

Area Number 

Lanes Curvature

Radius of 

Curve

Type of (DFT 20 kph)

Friction Data 

Pre-HFS 

KY Madison KY-21 Both 6.777 6.837 curve Single curve rural 2 255 simple — 

KY Madison KY-21 Both 6.861 6.912 curve Single curve rural 2 635 simple — 

KY Madison KY-21 Both 11.297 11.374 curve Single curve rural 2 405 simple — 

KY Madison KY-21 Both 12.308 12.428 curve Single curve rural 2 590 simple — 

KY Madison KY-21 Both 12.492 12.593 curve Single curve rural 2 860 simple — 

KY Madison KY-21 Both 12.807 12.922 curve Single curve rural 2 640 simple — 

KY Madison KY-21 Both 13.258 13.424 curve Single curve rural 2 470 simple — 

KY Madison US-421 Both 3.56 3.639 curve Single curve rural 2 390 simple — 

KY Madison US-421 Both 5.943 6.101 curve Single curve rural 2 225 simple — 

KY Madison US-421 Both 6.125 6.282 curve S-curve rural 2 255 s-curve — 

KY Madison US-421 Both 6.303 6.382 curve Single curve rural 2 570 simple — 

KY Scott US-25 Both 11.03 11.104 curve Single curve rural 2 725 simple — 

KY Scott US-25 Both 11.14 11.199 curve Single curve rural 2 540 simple — 

KY Bracken KY-1159 Both 1.771 1.971 curve S-curve rural 2 530 s-curve — 

KY Bracken KY-1159 Both 2.197 2.31 curve Single curve rural 2 215 simple — 

KY Greenup KY-1458 Both 1.31 1.369 curve Single curve rural 2 170 simple — 

KY Hardin KY-9001 EB — — ramp

EB (KY 9001) to I

Ramp from WK Pkwy 

-65 

NB urban 1 240 simple — 

KY Fayette I-75 NB — — ramp

Ramp from I-75 NB to 

US 27 urban 1 155 compound — 

KY Mercer US-68 Both 5.112 5.312 curve S-curve rural 2 315 s-curve — 

KY Pike US-460 Both 23.08 23.12 curve Single curve rural 2 435 simple — 

SC Charleston

IUS 17526 @ 

NB — — ramp

Ramp from NB US 17 

to WB I-526—first half 

of ramp urban 1 140 compound — 

SC Charleston

IUS 17526 @ 

EB — — ramp

Ramp from SB US 17 

to EB I-526—end of 

ramp urban 1 140 simple — 

SC Cherokee

I-85 @ 

US 29 NB — — ramp

Offramp from NB I-85 

to US 29—only first 

part of ramp treated urban 1 140 simple — 

SC Greenville US 25 Both 52.19 53.22 curve Multiple curves rural 4 1430 (2)

multiple 

curves — 

SC Horry

SC 31 @ 

SC 9 EB — — ramp

Ramp from NB/EB 

SC 31 to NB SC 9—

first part of ramp urban 2 765 simple — 

SC Horry

SC 31 @ 

SC 9 EB — — ramp

Ramp from NB SC 9 to 

NB/WB SC 31—first 

part of ramp urban 1 255 simple — 

SC Horry

SC 31 @ 

SC 544 WB — — ramp

31 to SB SC 544

Ramp from WB/SB SC 

—first 

part of ramp urban 1 255 simple — 
State County Route Dir

Milepost 

Begin

Milepost 

End

Site 

Type Location Description

Area 

Type

Number 

Lanes

Radius of 

Curvature

Type of 

Curve

Friction Data 

(DFT 20 kph)

Pre-HFS

SC Horry

SC 31 @ 

US 501 WB

—— ramp

Ramp from WB/SB SC 

31 to SB US 501—first 

half of ramp urban1 295 simple—

SC Horry

SC 31 @ 

US 501 EB

—— ramp

Ramp from NB/EB SC 

31 to NB US 501—first 

part of ramp urban1 315 simple—

SC Richland

I-126 @ 

US 21 NB

—— ramp

Ramp from NB US 21 

(Huger St.) to WB I-126 urban

2 315 simple—

SC Richland

I-126 @ 

US 21 SB

—— ramp

Offramp from EB I-126 

to SB US 21 (Huger 

St.) urban2 700 simple—

TN Houston SR49 Both 14.9 15.15 curve Rural road curve rural

2 975 simple—

TN Carter SR37 Both 11.6 11.75 curve Rural road curve rural

3 405 simple—

TN Rhea SR30 Both 6.84 7.14 curve Rural road curve rural

3 475 simple—

TN Cumberland SR68 Both 8.1 8.33 curve Rural road curve rural

2 440 simple—

WI MilwaukeeI-94 EB 346.57 346.57 ramp

Ramp from EB I-94 to 

NB I-43 urban2 505 compound 0.314 

CA Humboldt US 101 NB 88.2 88.3 ramp

Ramp from NB US 101 

to EB CA 299—first 

part of ramp urban1 290 simple—

CA Nevada CA 20 Both 30.4 30.6 curve Single curve rural

2 300 simple—

CA Sacramento US 50 WB 16.805 n/a ramp

Ramp from Folsom 

Blvd to WB US 50—

beginning of ramp urban3 205 simple—

CA Sacramento US 50 WB 16.805 n/a ramp

Ramp from Folsom 

Blvd to WB US 50—

end of ramp urban1 870 simple—

CA Sacramento US 50 WB R0.595 n/a ramp

Ramp from Stockton 

Blvd to WB US 50—

end of ramp urban1 150 simple—

CA Sacramento US 50 WB R2.680 n/a ramp

Ramp from NB 65th St. 

to WB US 50—end of 

ramp urban1 150 simple—

CA Santa Cruz CA17 SB 9.37 9.65 curve

Curve on SB Highway 

17 rural2 470 simple 0.469 

TX WilliamsonI-35 NB

—— ramp

Ramp for U-turn lane 

on NB I-35 frontage 

road at FM 2338 urban1 60 simple 0.456

TX Williamson US 183 NB

—— curve

Curve at intersection of 

NB US 183 and 

US 183A rural2 115 simple 0.52

133 
State County Route Dir

Milepost 

Begin

Milepost 

End Type

Site 

Location Description Type

Area Number 

Lanes Curvature

Radius of 

Curve

Type of (DFT 20 kph)

Friction Data 

Pre-HFS 

IA Polk I-80 EB — — ramp

Ramp from EB I-80 to 

SB I-235 urban 1 510 simple — 

IA Polk I-80 EB — — ramp

Ramp from EB I-80 to 

NB I-35 urban 1 770 simple — 

IA Polk I-80 WB — — ramp

Ramp from WB I-80 to 

SB I-235 urban 1 770 simple — 

IA Linn I-380 Both 19.7 19.7 curve

Elevated roadway on I380 just south of Cedar 

River urban 6 1125 simple 0.53 

LA Orleans I-610 WB 2.7 3.17 curve elevated roadway

Curve on bridge/

urban 3 1300 simple — 

Sites listed in California, Texas, and Iowa were not included in the analysis.

— = for Milepost columns, indicates information not available. For Friction Data columns, indicates data not available. 

WB = Westbound

EB = Eastbound

HFS = High friction surface

DFT = Drift friction tester

Dir = Direction
Table 45. Treatment site by State, part 2. 

State County Route

Underlying 

Pavement 

Type

Lane 

Width 

(Avg)

Left 

Shoulder 

Width 

(Avg)

Right 

Shoulder 

Width 

(Avg)

Posted 

Speed 

Limit

Month-Year 

of 

Treatment

Length of 

Treatment 

(ft)

Single/ 

Double 

HFST

HFST 

Binder Ty

KS Johnson I-35 pcc 11 10 4 35 9-Aug 780 Single Epoxy Bind

KS Leavenworth K5 asphalt 10.5 n/a 0 20 9-Aug 255 Single Epoxy Bind

KS Sedgwick K96 pcc 15 4 8 30 9-Aug 850 Single Epoxy Bind

KS Wabaunsee K99 asphalt 11 n/a 0 50 9-Aug 450 Single Epoxy Bind

CO Boulder US36 asphalt 12 n/a 1 35 9-Sep 925 Single Epoxy Bind

CO Boulder CO119 asphalt 12 n/a 2 40 9-Sep 600 Single Epoxy Bind

MT Missoula US93 chip seal 12 4 8 55 9-Sep 1375 Single Epoxy Bind

MT Silver Bow I-15 chip seal 13 6 6 25 9-Sep 1600 Single Epoxy Bind

MI Oakland I-75 pcc 14 4 7 25 10-Sep 865 Single Epoxy Bind

MI Oakland I-75 asphalt 12 n/a 8 35 10-Sep 275 Single Epoxy Bind

MI Genesee I-69 pcc 14.5 3 6 25 10-Sep 1124 Single Epoxy Bind

MI Kent I-96 pcc 14.5 6 8 25 10-Sep 730 Single Epoxy Bind

MI Wayne M39 asphalt 13 2 2 15 ?/2007 120 Single Epoxy Bind

MI Macomb I-94 asphalt 11 15 12 45 ?/2007 150 Single Epoxy Bind

MI Delta M35 asphalt 12 n/a 12 40 9-Sep 500 Single Epoxy Bind

KY Jefferson KY-1142 asphalt 10 n/a 2 35 10-Oct 343 Single Epoxy Bind

KY Jefferson KY-22 asphalt 10 n/a 3 35 9-Jul 422 Single Epoxy Bind

KY Jefferson KY-22 asphalt 11 n/a 3 35 10-Oct 412 Single Epoxy Bind

KY Oldham KY-22 asphalt 10 n/a 3 45 9-Aug 449 Single Epoxy Bind

KY Fayette KY-922 asphalt 9 n/a 4 55 10-Nov 322 Single Epoxy Bind

KY Garrard KY-1295 asphalt 9 n/a 2 55 10-Nov 634 Single Epoxy Bind

KY Garrard KY-1295 asphalt 9 n/a 2 55 10-Nov 681 Single Epoxy Bind

KY Garrard KY-1295 asphalt 9 n/a 2 55 10-Nov 919 Single Epoxy Bind

KY Garrard KY-1295 asphalt 9 n/a 2 55 10-Nov 1584 Single Epoxy Bind

KY Jessamine KY-29 asphalt 10 n/a 4 55 10-Nov 412 Single Epoxy Bind

KY Madison KY-21 asphalt 10 n/a 2 55 10-Nov 317 Single Epoxy Bind

KY Madison KY-21 asphalt 10 n/a 2 55 10-Nov 269 Single Epoxy Bind

KY Madison KY-21 asphalt 10 n/a 2 45 11-Apr 407 Single Epoxy Bind

KY Madison KY-21 asphalt 10 n/a 2 55 11-Apr 634 Single Epoxy Bind

KY Madison KY-21 asphalt 10 n/a 2 55 11-Apr 533 Single Epoxy Bind

KY Madison KY-21 asphalt 10 n/a 2 55 11-Apr 607 Single Epoxy Bind

KY Madison KY-21 asphalt 10 n/a 2 55 11-Apr 876 Single Epoxy Bind

KY Madison US-421 asphalt 10 n/a 3 55 11-Apr 417 Single Epoxy Bind

135 
State County Route

Underlying 

Pavement 

Type (Avg)

Width 

Lane 

Left 

Shoulder 

(Avg)

Width 

Right 

Shoulder 

(Avg)

Width 

Limit

Speed 

Posted Month-Year 

of 

Treatment

Treatment 

Length of 

(ft) HFST

Double 

Single/ 

HFST 

Binder Ty

KY Madison US-421 asphalt 10 n/a 3 55 11-Apr 834 Single Epoxy Bind

KY Madison US-421 asphalt 10 n/a 3 55 11-Apr 829 Single Epoxy Bind

KY Madison US-421 asphalt 10 n/a 3 55 11-Apr 417 Single Epoxy Bind

KY Scott US-25 asphalt 10 n/a 3 55 10-Nov 391 Single Epoxy Bind

KY Scott US-25 asphalt 10 n/a 3 55 10-Nov 312 Single Epoxy Bind

KY Bracken KY-1159 asphalt 10 n/a 2 55 11-Apr 1056 Single Epoxy Bind

KY Bracken KY-1159 asphalt 10 n/a 2 55 11-Apr 597 Single Epoxy Bind

KY Greenup KY-1458 asphalt 11 n/a 2 35 11-Apr 312 Single Epoxy Bind

KY Hardin KY-9001 asphalt 14 6 6 20 10-Nov 990 Single Epoxy Bind

KY Fayette I-75 asphalt 15 4 6 25 10-Nov 790 Single Epoxy Bind

KY Mercer US-68 asphalt 10 n/a 2 55 11-Nov 1056 Single Epoxy Bind

KY Pike US-460 asphalt 10 n/a 2 55 11-Nov 211 Single Epoxy Bind

SC Charleston

IUS 17526 @ 

asphalt 18 4 1 35 10-Nov 450 Single Epoxy Bind

SC Charleston

IUS 17526 @ 

asphalt 17 4 2 35 10-Nov 345 Single Epoxy Bind

SC Cherokee

I-85 @ 

US 29 asphalt 16 3 10 20 11-Jun 250 Single Epoxy Bind

SC Greenville US 25 asphalt 11 3 10 45 8-Oct 5438 Single Epoxy Bind

SC Horry

SC 31 @ 

SC 9 asphalt 11 6 10 40 10-Apr 515 Single Epoxy Bind

SC Horry

SC 31 @ 

SC 9 asphalt 16 3 3 30 10-Apr 925 Single Epoxy Bind

SC Horry

SC 31 @ 

SC 544 asphalt 17 5 5 25 10-Apr 620 Single Epoxy Bind

SC Horry US 501

SC 31 @ 

asphalt 16 8 6 30 10-Apr 670 Single Epoxy Bind

SC Horry US 501

SC 31 @ 

asphalt 14 6 6 25 10-Apr 560 Single Epoxy Bind

SC Richland

IUS 21 126 @ 

asphalt 12 0 0 35 11-Jun 650 Single Epoxy Bind

SC Richland

IUS 21126 @ 

asphalt 14 0 0 35 11-Jun 715 Single Epoxy Bind

TN Houston SR49 asphalt 12 n/a 10 55 10-May 1100 Single Epoxy Bind

TN Carter SR37 asphalt 11 n/a 6 55 9-Apr 800 Single Epoxy Bind

TN Rhea SR30 asphalt 12 n/a 4 55 9-Apr 1600 Single Epoxy Bind

TN Cumberland SR68 asphalt 11 1 1 55 9-Apr 1200 Single Epoxy Bind

WI Milwaukee I-94 asphalt 12 4 14 40 11-Sep 1130 Single Epoxy Bind

CA Humboldt US 101 asphalt 11 4 6 35 12-Oct 800 Single Epoxy Bind

CA Nevada CA 20 asphalt 11.5 6 6 30 11-Jul 700 Single Epoxy Bind

CA Sacramento US 50 asphalt 14 2 6 25 11-Jul 645 Single Epoxy Bind
State County Route

Underlying 

Pavement 

Type

Lane 

Width 

(Avg)

Left 

Shoulder 

Width 

(Avg)

Right 

Shoulder 

Width 

(Avg)

Posted 

Speed 

Limit

Month-Year 

of 

Treatment

Length of 

Treatment 

(ft)

Single/ 

Double 

HFST

HFST 

Binder Ty

CA Sacramento US 50 asphalt 12 3 1 25 11-Jul 400 Single Epoxy Bind

CA Sacramento US 50 asphalt 13 4 6 35 11-Jul 330 Single Epoxy Bind

CA Sacramento US 50 asphalt 13 6 8 35 11-Jul 415 Single Epoxy Bind

CA Santa Cruz CA17 asphalt 12 5 11 40 12-Jul 930 Double

Polyester 

Resin 

TX Williamson I-35 asphalt 17.5 5 7 n/a 10-Jun 240 Single Epoxy Bind

TX Williamson US 183 asphalt 11 n/a 4 20 10-Jun 355 Single Epoxy Bind

IA Polk I-80 asphalt 14 4 6 40 12-Jun 890 Single Epoxy Bind

IA Polk I-80 asphalt 14 6 4 45 12-Jun 1270 Single Epoxy Bind

IA Polk I-80 asphalt 14 6 4 45 12-Jun 1000 Single Epoxy Bind

IA Linn I-380 pcc 12 5 9 55 12-May 2240 Single Epoxy Bind

LA Orleans I-610 pcc 12 10 12 60 10-Mar 1265 Single Epoxy Bind

Sites listed in California, Texas, and Iowa were not included in the analysis.

Avg = Average

HFST = High friction surface treatment

Agg = aggregate

137 

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