Publication No. FHWA-HIF-13-004

May 2013

All-Weather Pavement 

Marking for Work Zones: 

Field Evaluation in North 

Carolina and Ohio

Temporary Wet-Weather Pavement Markings for Work 

Zones, Phase II Final Report
Notice

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

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

contained in this document.

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

appear in this report only because they are considered essential to the objective of the document.

Quality Assurance Statement

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

industry, and the public in a manner that promotes public understanding. Standards and policies are used 

to ensure and maximize the quality, objectivity, utility, and integrity of its information. FHWA 

periodically reviews quality issues and adjusts its programs and processes to ensure continuous quality 

improvement.
Technical Report Documentation Page

1. Report No.

FHWA-HIF-13-004

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

4. Title and Subtitle

Temporary Wet-Weather Pavement Markings for Work 

Zones, Phase II: All-Weather Pavement Marking for Work 

Zones, Field Evaluation in North Carolina and Ohio

5. Report Date 

May 2013

6. Performing Organization Code

7. Author(s) 

C.M. Cunningham, J.E. Hummer, J. Chang, D. Katz, C.L. 

Vaughan, D.J. Findley, B.J. Schroeder, and D. McAvoy

8. Performing Organization Report No.

9. Performing Organization Name and Address

Institute for Transportation Research and Education

North Carolina State University

Centennial Campus Box 8601

Raleigh, NC 27695

10. Work Unit No. (TRAIS)

11. Contract or Grant No.

DTFH61-08-G-00001

12. Sponsoring Agency Name and Address

Federal Highway Administration

Highways for LIFE Program – HIHL-1

1200 New Jersey Avenue, SE

Washington, D.C. 20590

13. Type of Report and Period Covered

Final Report, November 2007-April 2013

14. Sponsoring Agency Code

15. Supplementary Notes

16. Abstract

To address the problem of seemingly invisible pavement markings under nighttime, rainy conditions, 3M 

developed “All-Weather Paint” (AWP), which uses highly retroreflective elements in combination with latexbased pavement marking installed by highway agencies. Whereas standard pavement markings using waterborne 

pavement marking and glass beads become harder to see in the rain, the AWP performed well during closedcircuit field tests.

Researchers at North Carolina State University and Ohio University teamed up to conduct tests in active 

highway work zones. The team defined four measures of effectiveness (MOE) in an attempt to quantify safety 

performance when comparing the AWP to standard pavement marking materials under real-world driving 

conditions: retroreflectivity, vehicle travel speed, rate of lane encroachments, and linear lane displacement. Data 

collection procedures for each MOE are systematically outlined throughout the report.

From the results, the study concluded the following: (1) Retroreflectivity values were confirmed to be higher for 

AWP when compared to standard pavement markings. However, the AWP retroreflectivity values were 

inconsistent, likely because of the variation of application methods by pavement marking contractors. (2) Speed 

was used as a surrogate MOE to evaluate safety performance. It was not clear if an increase or decrease in speed 

has a positive effect on safety. Results showed that speed generally increased as drivers exited work zone lane 

shifts for all marking types; however, no consistent finding was noted between the two marking systems in 

similar curves. (3) The findings for lane encroachments varied throughout the sites. While the first site studied 

indicated that more lane encroachments occurred at standard pavement marking crossovers, a more robust study 

at a second site found the results to be statistically insignificant. (4) When assessing lateral lane placement, 

researchers found statistically significant but varied results. More often than not, motorists maintain safer lane 

placements when traveling along the AWP delineated lanes.

This report documents Phase II of this project. The Phase I report is available on the FHWA website at: 

http://www.fhwa.dot.gov/hfl/partnerships/3m/phase1/index.cfm.

17. Key Words

Retroreflectivity, pavement markings, work 

zone safety

18. Distribution Statement

No restrictions. This document is available to the public 

through the National Technical Information Service, 

Springfield, VA 22161.

19. Security Classification (of this report)

Unclassified

20. Security Classification (of this page)

Unclassified

21. No. of Pages

63

22. Price

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

TABLE OF CONTENTS

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

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

Rain and Night Conditions Nationwide ...................................................................................... 3

Adverse Weather Crashes............................................................................................................ 3

Safety in Work Zones................................................................................................................... 4

Pavement Markings...................................................................................................................... 5

Measures of Effectiveness............................................................................................................. 8

Retroreflectivity and Safety ......................................................................................................... 9

SITE SELECTION..................................................................................................................... 11

Site Selection Criteria................................................................................................................. 11

Site Descriptions.......................................................................................................................... 12

Discussion of Potential for Data Collection .............................................................................. 23

RETROREFLECTIVITY.......................................................................................................... 25

Data Collection............................................................................................................................ 25

Analysis Methodology................................................................................................................. 26

Results.......................................................................................................................................... 27

Conclusions and Recommendations.......................................................................................... 29

SPEEDS ....................................................................................................................................... 31

Data Collection Methods............................................................................................................ 31

Analysis Methodology................................................................................................................. 34

Results.......................................................................................................................................... 36

Conclusions and Recommendations.......................................................................................... 41

LANE ENCROACHMENTS..................................................................................................... 43

Data Collection............................................................................................................................ 43

Statistical Analysis of Lane Encroachments............................................................................. 43

Conclusion and Recommendations ........................................................................................... 45

LATERAL LANE PLACEMENT............................................................................................. 47

Data Collection............................................................................................................................ 47

Analysis Methodology................................................................................................................. 48

Results.......................................................................................................................................... 48

Conclusions.................................................................................................................................. 51

OVERALL CONCLUSIONS .................................................................................................... 53

REFERENCES............................................................................................................................ 55
iv

LIST OF FIGURES

Figure 1. Chart. 2008 mean total precipitation.(1) .........................................................................................3

Figure 2. Graph. Safety in work zones.(5)......................................................................................................5

Figure 3. Photos. Wet weather comparison of standard pavement marking (left) and the AWP (right) 

pavement markings.................................................................................................................................6

Figure 4. Photos. Comparison between standard pavement marking (left) and the AWP marking with 

microcrystalline ceramic beads (right). ..................................................................................................7

Figure 5. Graph. Minimum pavement marking retroreflectivity requirements as a function of speed.(19)....9

Figure 6. Photo. I-85 crossover locations. ..................................................................................................12

Figure 7. Photos. I-85 southern crossover during daytime and nighttime. .................................................13

Figure 8. Photo. Aerial view of northern crossover. Southbound traffic shifts to northbound side divided 

by a jersey barrier.................................................................................................................................13

Figure 9. Photo. Aerial view of southern crossover. Southbound traffic shifts back to normal operation. 14

Figure 10. Photo. US-15/501 crossover locations.......................................................................................15

Figure 11. Photo. US-15/501 southern crossover – standard pavement marking and the AWP study site.16

Figure 12. Photo. Nighttime camera views of US-15/501 northern crossover. ..........................................16

Figure 13. Photo. US-421 crossover locations............................................................................................17

Figure 14. Photos. Four curves at the US-421 test site. ..............................................................................18

Figure 15. Photo. US-32/33/50 work zones................................................................................................19

Figure 16. Photos. The AWP (left) and standard pavement marking (right) work zones on eastbound US32/33/50 during the day........................................................................................................................20

Figure 17. Photos. The AWP (left) and standard pavement marking (right) work zones on eastbound US32/33/50 at night...................................................................................................................................20

Figure 18. Photo. I-90 crossover locations. ................................................................................................21

Figure 19. Photo. AWP treatment location on I-90. ...................................................................................21

Figure 20. Photo. Standard pavement marking treatment location on I-90. ...............................................22

Figure 21. Photos. View of the AWP (left) and standard pavement marking (right) double-lane crossover.

..............................................................................................................................................................22

Figure 22. Diagram. Retroreflectance measurement fields.........................................................................25

Figure 23. Photo. Internal view of sensor. ..................................................................................................33

Figure 24. Photo. Sensor on mounting base................................................................................................33

Figure 25. Graphs. US-32/33/50 speed results by location in work zone...................................................40

Figure 26. Graphs. US-32/33/50 lane placement results.............................................................................50

Figure 27. Graph. I-90 lane placement results............................................................................................51
v

LIST OF TABLES

Table 1. Nighttime visibility of painted and thermoplastic centerline and edge line markings under lowbeam illumination.(16)..............................................................................................................................8

Table 2. Field test site descriptions.............................................................................................................12

Table 3. Average monthly rainfall totals (inches).(22) .................................................................................23

Table 4. Average available time for nighttime data collection (hh:mm).(22) ...............................................23

Table 5. Retroreflectivity sample size obtained at each of the four sites studied. ......................................26

Table 6. List of retroreflectivity samples at each site. ................................................................................27

Table 7. Summary of statistics for retroreflectivity readings at all test sites. .............................................28

Table 8. Summary of statistics for speeds...................................................................................................37

Table 9. US-32/33/50 speed ANOVA results.............................................................................................39

Table 10. I-90 speed analysis ANOVA results...........................................................................................41

Table 11. Chi-square results for US-15/501 lane encroachments...............................................................44

Table 12. US-421 lane encroachment statistics. .........................................................................................45

Table 13. Summary of statistics for lateral lane placement. .......................................................................49

Table 14. US-32/33/50 lane placement ANOVA results............................................................................49

Table 15. I-90 lane placement ANOVA results..........................................................................................51

LIST OF ABBREVIATIONS

AADT Average Annual Daily Traffic

ANOVA Analysis of Variance

AWP All-Weather Paint

CCTV Closed-Circuit Television

DOT Department of Transportation

FHWA Federal Highway Administration

LIDAR Light Detection and Ranging

MOE Measure of Effectiveness

NOAA National Oceanic and Atmospheric Administration

RI Refractive Index

RPM Raised Pavement Marker

TIP Transportation Improvement Project
vi

EXECUTIVE SUMMARY

Traffic crashes cause tens of thousands of deaths every year; thus, highway safety is at the forefront of the 

decision making process of transportation improvement projects. Traffic-related fatalities and severe 

crashes top the list and are immediate areas of concern to transportation policy and decision makers. 

Aside from mistakes made by drivers, the conditions of the road and environment often factor in the 

motorist decision process. Factors including, but not limited to, weather, lighting, and surface 

deterioration are examples often considered in the condition of the national highway system. Pavement 

markings are the focus of this research effort—specifically, improving their retroreflectivity under 

nighttime rainy conditions, which often hinder motorists’ ability to drive safely, especially in work zones,

where quick decisions are often a life-or-death situation. Decreased visibility of lane delineation and lack 

of situational awareness make navigation through complex work zones potentially unsafe for drivers.

To address the problem of poor visibility of pavement markings under nighttime, rainy conditions, 3M 

developed “All-Weather Paint” (AWP). AWP utilizes highly retroreflective elements in combination with 

latex-based pavement marking installed by highway agencies. Whereas standard pavement markings 

become harder to see in the rain, the AWP performed well during closed-circuit field tests.

Researchers at North Carolina State University and Ohio University teamed up to conduct tests in active 

highway work zones. Five test sites were selected in North Carolina and Ohio. The team defined four 

measures of effectiveness (MOE) in an attempt to quantify safety performance when comparing the AWP 

to standard pavement marking under real-world driving conditions: pavement marking retroreflectivity, 

vehicle travel speed, rate of lane encroachments, and linear lane displacement. Data collection procedures 

for each MOE are systematically outlined throughout the report. Basic statistical analyses were 

performed, and the methodologies are stated herein.

From the results, the study concluded the following: 

• Retroreflectivity values were confirmed to be higher for AWP when compared to standard 

pavement markings. However, the AWP retroreflectivity values were inconsistent, which was 

likely due to the variation of application methods by pavement marking contractors. 

• Speed was used as a surrogate MOE to evaluate safety performance. It was not clear if an 

increase or decrease in speed has a positive effect on safety. Results showed that speed generally 

increased as drivers exited work zone lane shifts for all pavement marking types; however, no 

consistent finding was noted between the two marking types in similar curves. 

• The findings for lane encroachments varied throughout the sites. While the first site studied 

indicated that more lane encroachments occurred at standard pavement marking crossovers, the 

more robust study of the second site found results to be statistically insignificant. 

• Finally, when assessing lateral lane placement, researchers found statistically significant but 

varied results. More often than not, motorists maintain safer lane placements when traveling 

along the AWP delineated lanes.

In summary, this study shows that AWP provides a low-cost, all-weather marking to improve lane 

visibility and to enhance safety.
vii
1

INTRODUCTION

Pavement markings are a vital tool for safely navigating our nation’s roads. Pavement markings come in 

many variations, depending on the application. Many of the pavement markings used today are especially 

difficult to see in the rain, particularly during nighttime hours. One commonly used pavement marking

(herein referred to as “standard pavement marking”) uses a latex paint coupled with 1.5 refractive index 

glass beads that retroreflect light back to the driver. More times than not, this application method is used 

in temporary installations such as work zones. However, because the marking is hard to see during 

nighttime rainy conditions, there is concern that drivers may have lane-keeping issues when navigating 

through active work zones. This poses a hazard to drivers and increases the risk of crashes. Therefore, 

most States currently supplement the pavement marking application with raised pavement markers to 

provide a visual cue to the driver on the location of the lane line.

Funded under the Federal Highway Administration (FHWA) “Highways for LIFE” technology 

partnerships program, this research project aims to test a variation of the latex-based pavement marking

that uses specially designed optical elements for temporary applications such as work zones. Developed 

by 3M, the “All-Weather Pavement marking” (AWP) consists of a standard pavement marking

supplemented with additional optical elements that retroreflect light in rain conditions, greatly increasing 

visibility during rain events.

Phase I of this study involved testing in a controlled environment. First, 24 pavement marking samples 

were installed on a test deck in New Orleans, LA, and time-series data regarding their retained 

retroreflective properties were collected. Three prototype all-weathering markings were identified to carry 

into the next task, which involved the installation of the pavement markings at the Texas Transportation 

Institute rain range. In these studies, the AWP and conventional markings demonstrated substantial 

differences in retroreflectivity; however, these studies were not conducted under real-world driving 

conditions, and they could not address any issues with contractors applying the markings in a dynamic 

environment where application time must be considered to keep traffic moving.

To build on the findings of Phase I, the Phase II effort aims to study actual drivers as they navigate 

through work zones by comparing standard pavement marking to the AWP under daytime, nighttime, and 

rainy conditions. First, the research team conducted a literature review to learn from previous research in 

the area of pavement marking studies. Following this effort, they selected field test sites based on a 

variety of factors, including a minimum of two lanes per direction in the transition zone, no raised 

pavement markers (RPMs), a minimum speed limit of 45 miles per hour, and no disruption from nearby 

traffic signals. In total, five sites were chosen, three in North Carolina and two in Ohio. Next, standard 

pavement marking and the AWP were applied in each of the chosen work zones. The analysis considered 

four measures of effectiveness (MOE): retroreflectivity, speeds, lane encroachments, and lateral lane 

placement. Retroreflectivity was utilized to determine differences in pavement marking application across 

sites and between pavement marking types at the same site. Speed was used to supplement findings from 

the latter two MOEs since higher or lower speeds could not be correlated to better or worse driving 

conditions. Last, lane encroachment and lateral lane placement were the primary MOEs used to determine 

if safety had improved using the new AWP.
2
3

LITERATURE REVIEW

To gain more insight into the part that pavement markings play in roadway safety, the researchers 

conducted a literature review at the start of Phase II of the study. This chapter details previous findings 

regarding adverse weather crashes, work zone safety, and pavement marking effectiveness. This 

information provided valuable direction for the conduct of Phase II.

RAIN AND NIGHT CONDITIONS NATIONWIDE

Rainfall frequency across the United States varies greatly, and it creates more opportunities for difficult 

driving conditions for certain areas. Higher rainfall means an increased exposure to wet driving surfaces.

In 2008, Pisano, Goodwin, and Rossetti gathered adverse weather crash data and related it to weather 

conditions across the country.(1) Their report included a nationwide rainfall map, shown in figure 1. From 

a rainfall frequency perspective, it is clear that drivers in the northwestern and southeastern United States 

are at a higher risk due to higher rainfall.

1 inch = 25.4 mm

Figure 1. Chart. 2008 mean total precipitation.

(1)

Nighttime driving conditions also vary over parts of the U.S. In the northern parts of the continental U.S., 

nighttime can last over 15 hours, and drivers are forced to operate their vehicles in dark conditions, 

including during peak travel periods. This is the case in such cities as Seattle, Washington; Minneapolis, 

Minnesota; and Rochester, New York. Even in the southernmost urban areas such as Miami, Florida, and 

Corpus Christi, Texas, wintertime nights are around 13.5 hours.

(

2)

ADVERSE WEATHER CRASHES

Driving in rainy, snowy, sleety, and/or windy conditions carries additional risk above that of driving in 

dry conditions. Both a driver’s perception of the road ahead and the wheel’s ability to grip the road are

reduced, making it more difficult to maneuver safely on highways. Between 1995 and 2005, 24 percent of 

all traffic fatalities were due to adverse weather, about 7,400 each year. The number injured annually in 

adverse weather crashes is a couple of magnitudes larger, totaling 673,000.

(1)

When further examining the 
4

statistics, 47 percent of these crashes occurred during rainfall, and 75 percent occurred on wet pavement.

The remainder are caused by snowy and slushy roads, fog, and high winds.

The Midwest has the highest rate of average weather-related crashes because of high amounts of snowfall.

In fact, 40 percent of all nationwide weather-related crashes occur in this area, which only accounts for 22

percent of the U.S. population. The South, however, is not far behind, with 32 percent of total weatherrelated crashes and 36 percent of the total U.S. population. The ratio of rainfall-related crashes in the 

South is the highest in the nation—over 64 percent of all weather-related crashes, which likely 

corresponds to the high levels of rainfall.

Compared to the growth in vehicle miles traveled, adverse weather-related crashes decreased during the 

study period. Regardless, the average number of crashes is over 1.5 million annually. The risk of driving 

in adverse weather will remain high due to low visibility during precipitation and poor tire traction on wet 

pavement, which consequently reduces vehicle control and maneuverability.

Qiu and Nixon conducted a study to compile the research done on the effects of adverse weather on crash 

rates, injury crash rates, and fatal crash rates. Through meta-analysis, they compared daily cash rates 

during adverse weather with those during non-adverse weather. At a confidence interval of 95 percent, the 

study concluded that rain could increase the crash rate by 71 percent and the injury rate by 49 percent.(

3)

Although the percentage of crash rates related to snow events has decreased over time, the study found 

that this was not the case for crash rates related to rain events. The increased risk of crashes, injuries, and 

fatalities during rainy and/or wet conditions demonstrates the need for more informative, noticeable 

pavement markings.

Hummer et al. analyzed data from the Highway Safety Information System to document the 

characteristics of crashes in North Carolina.(4)

They found that, from 2003 to 2005, approximately onethird of crashes occurred in reduced lighting conditions (dusk, dawn, or dark). Additionally, 23 percent of 

collisions happened during adverse surface conditions (19 percent when wet and 4 percent with ice, snow, 

or slush).

SAFETY IN WORK ZONES

The United States has a transportation system that is reaching its capacity limits. The infrastructure is 

aging and needs to be replaced. Therefore, to maintain mobility for drivers, roadway maintenance and 

construction must occur concurrently with traffic operations. Safety in work zones is essential, especially 

as the number of highway rehabilitation and construction projects continues to increase. Since 1983, the 

first year of data available, the National Work Zone Safety Information Clearinghouse reports a general 

rise in work zone fatalities as a percentage of the total number of highway fatalities (figure 2).(

5)

Over the 10-year period from 1997 to 2006, work zone fatalities in crashes increased by 45 percent.(

6)

Although there was a marked drop in 2007 to levels not seen since the 1990s, work zone fatalities are 

increasing as a percentage of total fatalities. In the 1980s, work zone fatalities accounted for 1.5 percent

of all highway fatalities, but this increased to 1.8 percent in the 1990s, and then to 2.4 percent since 2000, 

peaking at near 2.8 percent in 2002.(5) This equates to 717 fatalities in 1996 and 1,095 in 2003, or an 

increase of 35 percent.(

7)
5

Figure 2. Graph. Safety in work zones.

(5)

Work zone safety has become a larger issue over the years. Even as total annual fatalities have slowly 

dropped, the percentage of work zone fatalities (as a share of total fatalities) has increased. The increase 

in work zone fatalities is likely a result of increased amounts of maintenance activity on the nation’s 

highways. Between 1997 and 2004, an increased share of construction costs was spent on existing 

facilities, growing from 47.6 to 51.8 percent of capital funds.

(

6)The FHWA also estimates that at any 

given time, 20 percent of some portion of each roadway in the National Highway System is under 

construction.

(

8)Additionally, the crash rate of any particular stretch of highway has been shown to 

increase by almost 30 percent during a construction period.

(

9) Although the percentages vary, another 

study also found that work zones greatly increase crash risks.(10)

Lastly, according to the FHWA, 

approximately half of all fatal work zone crashes occurred at night.(

6)This stresses the importance of 

visible pavement markings to help drivers navigate through work zones. 

PAVEMENT MARKINGS

Standard Pavement Marking

The most common method of marking pavement is to spray pavement marking on the roadway surface 

and then drop glass beads with an index of refraction of 1.5 to make the markings more visible at night.

The pavement marking, typically made of waterborne pavement marking, thermoplastics, epoxy, or 

solvent pavement marking, acts as a binder for the glass beads. Eighty-nine percent of State transportation 

agencies use waterborne pavement markings, which are the least expensive among these options.(11)

The retroreflective performance of the glass beads under given conditions depends on their refractive 

index (RI). Generally speaking, under dry conditions beads with an RI of 1.9 provide the highest 

retroreflectivity. Under wet conditions the RI needs to be greater, around 2.4, for high retroreflectivity 

because a film of water covering the beads changes the optical conditions that provide retroreflectivity.

An issue with conventional pavement marking systems is their poor performance in wet conditions. While 

dry, headlight illumination on pavement markings with 1.5 index beads typically provides sufficient 

guidance information to the driver. When covered by a layer of water, such as during rainfall, their 
6

retroreflective properties diminish. To a driver, it appears that the pavement markings have disappeared.

The reason for this drop in performance is that the water reduces the amount of light that is retroreflected 

from the markings to the oncoming driver, due to a loss of effectiveness of the optical system. It becomes 

very difficult to see the pavement markings, and drivers have increased difficulty navigating a safe route.

To improve visibility of pavement markings in wet weather, 3M developed the AWP, which maintains 

retroreflective properties while covered with a film of water. The key to this technology is specially 

developed elements that provide retroreflection in both dry and wet conditions. After the AWP is sprayed 

on the road surface, 3M bonded core elements are dropped, followed by a second drop of conventional 

glass beads. Figure 3 shows a comparison of standard pavement marking and the AWP during a typical 

rain event in Henderson, North Carolina. The AWP, when applied correctly, is visibly more 

retroreflective.

Figure 3. Photos. Wet weather comparison of standard pavement marking (left) and the AWP (right) 

pavement markings.

The AWP Marking 

Each 3M element is composed of a structural core surrounded by microcrystalline ceramic beads that 

have a range of higher refractive indices.(12)

The refractive indices of the small attached beads are 1.9 and 

2.4, compared to the typical 1.5 used in most standard pavement markings. The beads with a refractive 

index of 1.9 perform well in dry conditions, while the beads with the higher RI of 2.4 perform well in wet 

conditions, thereby improving overall visibility for the driver in a variety of weather conditions. Figure 4

displays a side-by-side comparison between standard pavement marking and the AWP marking.

The AWP binds the larger glass beads and the 3M elements to the pavement. According to 3M’s website, 

“3M All Weather Paint includes a ‘high-build’ resin to enable thicker application for lines that last up to 

twice as long as conventional traffic pavement marking,” and is manufactured with a high-build polymer 

emulsion.(13)

The pavement marking is ideal in locations where other existing waterborne equipment is 

available.(12) It is applied at 25 mil (.635 mm) wet thickness, providing a very thick, wear-resistant layer 

of pavement marking.
7

Figure 4. Photos. Comparison between standard pavement marking (left) and the AWP marking with 

microcrystalline ceramic beads (right).

To optimize the construction, 3M tested various mixtures of beads and elements, drop rates, and 

pavement marking thicknesses. Due to the variations possible, there is an opportunity for customization 

and optimization. The mixtures for this test were selected from 24 candidate samples of pavement 

markings tested on a New Orleans, Louisiana, test track in November 2007.

(14)

These candidates were 

measured for their retroreflectivity under dry and wet conditions over a period of 6 weeks. The 

degradation of the different mixes varied—elements and glass beads became loosened from the binding 

pavement marking and scattered away due to repeated traffic passes—and only those that performed well 

were recommended for future tests.

Later, the Texas Transportation Institute, using the field research facility at the Texas A&M University 

Riverside Campus in Bryan, Texas, performed a human factors study to determine drivers’ ability to see 

the AWP.

(14)

The driving course, outfitted with a rain range able to simulate rainy conditions, was sprayed 

with the pavement marking mixes that had performed well on the New Orleans test track. Two test 

segments were set up, once under the rain range and once elsewhere on the course for a dry weather 

comparison. Researchers first subjected drivers to the dry weather nighttime drive-through. Participants 

would announce when they were first able to see the end of the pavement marking, and the researcher 

would note the distance the vehicle was at that time. Next, the drivers were instructed to drive through the 

rain range where the wet conditions were simulated and similarly announced when they could view the 

end of each pavement marking. From these tests, one pavement marking mix was chosen to be 

implemented in the field due to its better visibility. The specifications provided are: 

3M medium-sized high refractive index dual-optics drop-on elements at a drop rate of

8g/lineal ft in combination with MODOT Type P (or AASHTO M247 Type I).1.5 index glass 

beads at a drop rate of 12g/lineal ft applied in a double-drop onto a high-build waterborne 

pavement marking applied at a 20 mil [.508 mm] wet film thickness.

Safety Effect

Lane markings influence driver behavior continuously, and they are one of the most common ways 

drivers receive information about the roadway alignment ahead. Their presence helps influence the 

driver’s position on the road, specifically within a travel lane. The position of a vehicle in a lane has a 

high correlation with safety, with correlation coefficients typically between 0.7 and 0.8, showing that the 

existence of pavement markings to guide the driver is extremely important. To ensure drivers have 
8

adequate guidance at night, the pavement marking’s retroreflective properties must provide sufficient 

visual detection distance. Rumar and Marsh concluded that drivers need between 3 and 5 seconds of 

“preview time” to guide their vehicle.(15)

They describe that, “in night driving, the single-vehicle crash

risk increases proportionately with the decrease of average geometric sight distance” and that “this is an 

indication of the importance of longer preview times.” Pavement markings that do not have high 

retroreflective properties put drivers at risk at night.

Pavement markings are also influential on a driver’s speed, but the precise nature of that influence is 

unclear. Improved lane markings have reportedly increased speeds, but speeds have also dropped in some 

locations.

(15) Speed increases are likely to happen because drivers have an increased amount of 

information being provided to them about the roadway ahead. This increases driver confidence to 

navigate the road, allowing an increase in speed. However, it is still to be determined if this increase in 

speed creates a safety risk.

Rumar and Marsh focused their study on visibility to evaluate pavement markings, as opposed to the 

crash rate, which they rejected as being inconclusive. They also hesitated to use speed, lateral placement, 

and the number of overtakings as measures of effectiveness.

(15)

Detection distance was identified as the 

most important aspect to the researchers. Emphasizing their case, the authors discussed several studies on 

the visibility of lane markings and retroreflectivity. Their paper presents examples of where lane 

markings give great benefit, such as at night, when a driver’s point of fixation moves down and to the 

right, to avoid the bright lights of oncoming vehicles. Here, edge lines become critical for the driver to 

navigate the roadway safely.

Emphasizing visibility at critical times, Jacobs et al. discussed pavement markings in rainy conditions.(16)

The study looked at several pavement marking products that differed in performance properties during 

wet conditions. Drivers were asked to drive a section of road where the pavement markings changed from 

traditional to all-weather performance. The drivers were to indicate when they noticed the change in the 

pavement marking products. The distance from where drivers indicated that they noticed the change to 

where the pavement marking actually changed was the primary measure of effectiveness and was 

obtained during dry and wet conditions. Table 1 displays the comparison table from that study; however, 

it should be noted that the samples taken in wet and dry conditions were not comparable and must be read 

independently. The authors strongly articulated that, in these situations, lane markings are most necessary, 

but they are prone to fail more often at these times. Such concern is what the AWP pavement markings 

are intended to address, especially in temporary application such as work zones.

Table 1. Nighttime visibility of painted and thermoplastic centerline and edge line markings under lowbeam illumination.(16)

Condition of markings New Old

Condition of road Dry Wet Dry Wet

No oncoming glare

164-285 ft

(50-87 m)

112-125 ft

(34-38 m)

98-213 ft

(30-65 m)

89-108 ft

(27-33 m)

Oncoming low-beam glare

164-253 ft

(50-77 m)

131-164 ft

(40-50 m)

98-131 ft

(30-40 m)

98-131 ft

(30-40 m)

MEASURES OF EFFECTIVENESS

Carlson et al. chose two conflict MOEs to study the effect of pavement markings in rural high-speed 

maintenance work zones.

(17)

They considered 14 different conflict types and found only 2 applicable to 

work zones. The first was the slow vehicle conflict, which involves a faster vehicle overtaking a slower 

vehicle. The second was the lane change conflict, where a vehicle needs to brake to switch travel lanes 
9

because of a lack of gaps in the other lane, or a vehicle changing lanes and forcing vehicles behind it to 

brake or swerve when there was not a large enough gap.

In Phase I of this study, the researchers identified three additional MOEs.

(18)

The Phase I report stresses 

the importance of these MOEs and describes the following as necessary for evaluation of the new 

pavement markings: mean of the lateral placement of the vehicle in the travel lane, variance of the lateral 

placement of the vehicle in the travel lane, and number of times the vehicle touches the edge markings.

The first two MOEs are correlated with crash frequency. There is a long tradition in the field that vouches 

that edge markings can be related to crash frequency as well. Increases in the inadvertent contact of the 

edge markings is a result of drivers varying their position in a lane away from the center, and a reduction 

would indicate increased safety benefit.

RETROREFLECTIVITY AND SAFETY

The retroreflectivity of pavement markings is defined by the coefficient of retroreflected luminance, 

usually expressed in units of millicandela per meter squared per lux (mcd/m2

/lux). Higher values indicate 

that the marking will appear brighter when illuminated by a set of headlights. Thus, a higher value of 

retroreflectivity should provide a longer detection distance and enable drivers to make earlier navigation 

decisions. As speed increases and drivers have less time to make judgments, it is critical that the 

pavement markings are still detected at distances that adequately inform users of the road geometry 

ahead. In Schieber’s study on aging drivers, he showed how retroreflectivity needs increase at a higher 

rate than speed, as seen in figure 5.

(19)

Schieber also described how higher retroreflectivity is critical for 

aging drivers whose vision has decreased.

 1 mph = 1.6 kph

Figure 5. Graph. Minimum pavement marking retroreflectivity requirements as a function of speed.

(19)

Opposing arguments for high retroreflective pavement markings exist. Bahar et al. found that increased 

retroreflectivity in longitudinal pavement markings does not have any safety benefit.(11)

Their claim is that 

the most important aspect of pavement markings is that they be present and visible for drivers; the level of 

brightness of the pavement markings is of less importance to safety. The argument is that drivers adapt to 

higher retroreflective markings and increase speeds due to increased confidence of the road ahead, 

thereby negating the safety benefit. Higher speeds place the driver at risk for more frequent and higher 

severity crashes.
10

Summarizing past literature, Bahar et al. described how the relationship between retroreflectivity and 

nighttime crashes has not been sufficiently proved. Likewise, Lee, Maleck, and Taylor were unable to 

identify any relationship between nighttime crashes and the retroreflectivity of pavement markings(20)

Another study, by Abboud and Bowman, compared long-term crash rates to the average crash rate of 

crashes influenced by line visibility.

(21)

Although this study recommended a minimum value of 

retroreflectivity, Bahar et al. disputed the data analysis methods used. Bahar et al. studied the safety effect 

of pavement markings themselves and found that no pattern of improved safety exists as retroreflectivity 

is increased. Ultimately, they concluded that any safety effect is too small to detect, and the brightness of 

lane markers shows no increase in safety. They also reported that driver adaptation minimizes road safety 

improvements even in adverse weather conditions.
11

SITE SELECTION 

SITE SELECTION CRITERIA 

All aspects of this study took place in work zones. Work zones require drivers to make decisions that are 

not typical to normal driving operations, putting their cognitive processes under additional stress. For this 

project, crossovers were the chosen work zone maneuver for study, as opposed to lane drops or other 

maneuvers. Crossovers occur when traffic is diverted away from the existing roadway onto a temporary 

roadway section and then reintroduced to the existing roadway (this maneuver is typical when a bridge is 

being reconstructed and drivers are diverted onto a temporary bridge). Crossovers typically have less 

signage and information conveyed to the user, increasing the driver’s need for pavement markings or 

other devices installed along the roadway for navigation. For example, at crossovers through the 

construction site, information is conveyed to the user through special means such as cones, barrels, and 

temporary signage. To remove the effect that other devices may have on drivers’ ability to navigate a 

crossover, 3M provided certain site requirements and desired characteristics of candidate work zones.

With only minor deviations, these requirements were communicated to members of the research team 

before the project began, and included:

General Site Requirements

• Work zone open for a minimum duration of 3 months.

• Four or more lanes on the highway (two lanes per direction of travel).

• No roadway lighting, if possible, especially construction lighting. If lighting is necessary, it 

should be approved and kept to a minimum.

Desired Site Characteristics

• Posted work zone speed limit of 45 miles per hour (72 kph) or higher.

• State, US, or interstate highways.

• Substantial lane shifts; for instance, no short crossovers to the shoulder (less than one lane width).

• No temporary RPMs. These typically are installed on all work zone applications in North 

Carolina and as necessary in Ohio.

• If jersey barriers are utilized at the site, no paddle reflectors installed on the top of the barriers.

• A good pavement marking contractor, as indicated by the State department of transportation 

(DOT).

• A history of multiple rainy days on record in the construction work zone season.

• No traffic signals in or near the crossover being studied.

In addition, in Phase I of the present study, the researchers concluded that a total sample of 250 vehicles 

was necessary to determine a 0.05 level of significance in differences in lane line encroachments.(18) At 

this value, it should be possible to detect differences between the experimental and standard pavement 

markings and their interactions with wet conditions.

Using these criteria, the research team worked closely with DOTs and nearby municipalities to find sites 

that would be suitable for study. Sites that were considered were all under construction during the 2009-

2011 construction seasons. Additional criteria the team used to choose sites included:

• Two crossovers (entering and exiting the construction zone) so that one of the crossovers is 

utilized as a comparison site with standard pavement markings.

• Moderate to high traffic volumes that would meet the sample size requirements.

• Manageable implementation timeframes.
12

• Proximity to research team base to get to a site quickly during potential rain events. Sites within a 

radius of approximately 1 to 1.5 hours were preferred; however, in special circumstances, sites 

within a 4-hour drive were considered feasible.

Site types of interest included bridge replacements, resurfacing and rehabilitation of roadway surfaces, 

and widening. The effect of the economy on road construction and the requirements necessary (especially 

number of lanes and time to site) posed a major challenge to members of the research team. Table 2 

shows a list of sites. The Lanes column shows the number of lanes that remained open through the 

crossover in one direction of the highway; all highways had four lanes originally. 

Table 2. Field test site descriptions.

SITE DESCRIPTIONS 

I-85, Henderson, North Carolina

This project was a rehabilitation of several miles of I-85, designated North Carolina DOT Transportation 

Improvement Project (TIP) #I-2810. Henderson is located in the Northern Piedmont. During the data 

collection months of May and June 2009, the average monthly rainfall was above 3.6 inches (9.5 cm) and 

had a minimum of 9 hours and 22 minutes between sunset and sunrise.

(22)

During the period of 

construction, southbound I-85 was repaved, including some bridge rehabilitation. Median crossovers were 

utilized to bring traffic from southbound I-85 to the inside lane of northbound I-85. The north and south 

crossovers are approximately 6 miles (9.7 km) apart, shown in figure 6. Figure 7 shows daytime and 

nighttime views of the right curve into the treatment crossover on the south end of the work zone.

Figure 6. Photo. I-85 crossover locations.

Roadway Location Type Lanes Speed Limit

I-85 Henderson, NC Interstate 1 55 mph (89 kph)

US-15/501 Chapel Hill, NC US Route 2 45 mph (72 kph)

US-421 Winston-Salem, NC US Route 2 55 mph (89 kph)

US-32/33/50 Athens, OH US/SR Routes 1 55 mph (89 kph)

I-90 Ashtabula, OH Interstate 2 55 mph (89 kph)

Northern Crossover

Southern 

Crossover
13

Figure 7. Photos. I-85 southern crossover during daytime and nighttime.

During the construction phase, traffic were reduced to one travel lane in each direction prior to entering 

the study area, using the original northbound travel lanes in a two-lane, two-way traffic pattern. At the 

time of the initial site selection process, the research team was not aware of the desire to study multiple 

lanes shifting into and out of the work zone. Because a sizeable effort was made to install data collection 

equipment, the site was kept for further data collection and analysis to note any differences between this 

site and other sites with multiple lane crossovers studied in this project. In addition, retroreflectivity 

studies were not affected by the need for more than one lane of traffic, so those findings are included in 

the summary findings. Figures 8 and 9 show aerial views of the southbound traffic shift at the northern 

and southern crossovers.

Figure 8. Photo. Aerial view of northern crossover. Southbound traffic shifts to northbound side divided 

by a jersey barrier.
14

Figure 9. Photo. Aerial view of southern crossover. Southbound traffic shifts back to normal operation.

Standard and the AWP markings were installed at the crossovers located along southbound I-85. The 

northern crossover was marked by the standard pavement marking specified in the construction plans and 

included RPMs and traffic barrels. The southern crossover was marked by the AWP, traffic barrels on the 

passenger side, concrete barriers with reflectors on the driver side, and RPMs. Based on recommendations 

in the literature, a minimum 5-second preview time was utilized with the AWP on the southern crossover, 

corresponding to a 500-foot (152.4 m) application of the AWP along the tangent just prior to first radius 

of the crossover.

(17)

The speed limit was 65 miles per hour (105 kph), but a $250 speeding penalty was in 

effect and was affixed to speed limit signs throughout the site. Speeds were under enforcement along a 

tangent section in the northbound direction during many of the initial site visits the research team made 

early during the construction process, especially during daytime operations. No data were collected while 

enforcement was taking place.

Both the comparison and treatment sites had S-curve geometry. After some discussion with North 

Carolina DOT inspectors and the pavement marking contractor, it was decided that the last curve exiting 

the work zone on the southern site (treatment) could not be tied into the regular lanes with the AWP 

because there was not enough time to switch out the elements and properly calibrate the pavement 

marking thickness and element drop rate, all while slowly rolling traffic through the work zone. This 

meant the final curve (left turn of the treatment site) could not be studied. Thus, the right turns for the 

northern and southern crossovers would be the focus of study for this particular site for comparison of 

speeds and lane encroachments.

Another issue that the project team noted at the northern end of the test site was the atypical manner in 

which I-85 was reduced to one lane (see figure 7). At a single point, drivers are forced to choose between 

veering left onto I-85 southbound or staying straight to exit onto US-158. Drivers appeared confused by 

this arrangement; many cars were observed making last-second lane changes less than 200 feet (61 m) 

from the delineating barrels. Other drivers were seen coming to a complete stop on the highway to decide 

which way to go. Furthermore, some drivers would make the wrong choice, quickly pull off onto the 

shoulder, and either reverse down the highway or drive through the barrels separating the exit and I-85.

Because the layout of the entry into the crossover was problematic, data were not collected during periods 

of obvious confusion. In addition, the first turn (left turn) into the comparison site crossover was not 

studied so that any other possible “confusion effect” was eliminated.

Last, it should be noted that the length of this site (approximately 6 miles [9.7 km]) could pose problems 

in the analysis. Rain duration and intensity could be very different at one crossover than at the other. To 

help alleviate any potential concerns, the research team only analyzed data 10 minutes after the obvious 

beginning of a rain event at each site and stopped 10 minutes before rain ceased.
15

In light of the various issues with this site and assumptions that had to be made, research team members 

recommend that any speed and lane encroachment effects should be evaluated carefully. This site should 

only be considered as potentially supplementing any findings from the other test sites.

US-15/501, Chapel Hill, North Carolina

This particular project was a bridge rehabilitation project approximately 0.9 miles (1.4 km) in length 

located on US-15/501 just north of Chapel Hill. Figure 10 shows an aerial photo of the entire construction 

site. The site is located near the Durham and Orange County borders, with US-15/501 intersecting I-40 

approximately 1 mile (1.6 km) beyond the southern crossover point. 

Figure 10. Photo. US-15/501 crossover locations.

After the completion of the first of two bridges, southbound traffic was transitioned over to the 

northbound travel way to a temporary bridge built adjacent to the existing bridge. Two temporary lanes of 

traffic were constructed and separated from northbound traffic with a barrier. After crossing the 

temporary bridge, traffic was redirected back into the original southbound lanes. This second crossover 

was the focus of this study because of the nearby traffic signal at the northern crossover, especially the 

queues that spilled back into the crossover from the exit. Figure 11 is a photograph of the southern 

crossover. The research team installed a mast with an omnidirectional camera to observe this southern 

crossover point during both analysis periods (see figure 12) as both pavement markings were installed at 

this location in a staggered fashion. During the data collection months of May and July 2010, the average 

monthly rainfall was above 4.1 inches (10.4 cm), and there was a minimum of 13 hours and 38 minutes 

between sunset and sunrise.

(22)
16

Figure 11. Photo. US-15/501 southern crossover – standard pavement marking and the AWP study site.

Figure 12. Photo. Nighttime camera views of US-15/501 northern crossover.

Both crossovers were two-lane movements. The comparison crossover consisted of standard pavement 

marking and a median barrier adjacent to the left lane. The treatment crossover was similar in that the 

AWP and median barrier were the only devices providing information to drivers regarding the 

configuration of the travel lanes. The speed limit was 55 miles per hour (89 kph). Both the comparison 

and the treatment sites had S-curve geometry similar to that of the Henderson site. However, the curves 

were slightly less abrupt than at the Henderson site, with larger radii and longer curve lengths.
17

US-421, Winston-Salem, North Carolina

The Winston-Salem site was similar to the Chapel Hill site in that it was also a bridge rehabilitation 

project. This site is located on US-421 approximately 2 miles (3.2 km) from I-40, Exit 188, between 

South Peace Haven Road and Lewisville Clemmons Road. An aerial photo of the site is shown in figure 

13. 

Figure 13. Photo. US-421 crossover locations.

Temporary bridges were installed in each direction while new bridges were built in the existing locations.

After the completion of these temporary bridges, traffic lanes were shifted onto them, with approximately 

500 feet (152.4 m) of space between the bridge and each crossover. The research team monitored the 

eastern and western crossover points for both directions, as opposed to only one crossover point, which 

was the case at the US-15/501 site.

The lanes traveling eastbound were marked with standard pavement markings, designating it the 

comparison side. Consequently, the two westbound lanes were marked with the AWP, thus being the 

treatment side. Both sides had a barrier between the left travel lanes and the construction area that 

transitioned from barrels to concrete barrier and back to barrels. Additionally, during the course of the 

research, RPMs were installed on the comparison side, unbeknownst to the research team. Soon after 

finding out about the installation of the RPMs, a member of the team went to the site and, with the help of 

the North Carolina DOT, uninstalled them. This was done to obtain a fair and accurate comparison 

assessment of the visibility of the standard pavement markings and that of the AWP pavement markings.

The site was monitored using standard closed circuit television (CCTV) cameras, which were attached to 

two separate masts that were erected in the median a few hundred feet past the eastern and western 

crossover points looking directly down the lane lines. Figure 14 displays screen shots from the videos of 

the four curves. The use of two masts allowed the researchers to collect data for all four crossover points 

simultaneously. Because rain duration and intensity were the same on each of the crossovers, the 

simultaneous data collection was optimal for the data analysis. During the data collection months (May 

through October 2011), the average monthly rainfall was 3.5 inches (8.9 cm), and there were a minimum 

of 12 hours and 58 minutes between sunset and sunrise.

(22)
18

Figure 14. Photos. Four curves at the US-421 test site.

US-32/33/50, Athens, Ohio

The US-32/33/50 project involved the reconstruction of the existing highway over a distance of 

approximately 2 miles. The Athens study site was located on US-32/33/50 between the interchanges with 

State Route 682 (SR 682) and Township Highway 60 (Blackburn Rd.). When US-33 and US-32/50 split 

near the end of the work zone, the study area continued on US-32/50 until just before Township Highway 

60. The study area is shown in figure 15.
19

Figure 15. Photo. US-32/33/50 work zones.

According to the Ohio DOT, the annual average daily traffic (AADT) in 2009 for the section of US32/33/50 was 17,960 vehicles per day, with trucks representing 8 percent of the total traffic. For the 

section of US-32/50, the AADT was 13,050 vehicles per day, with trucks representing 7 percent of the 

total traffic.(23)

During the data collection months from April through June 2010, the average monthly 

rainfall was above 3.4 inches (8.6 cm), and there were a minimum of 12 hours and 39 minutes between 

sunset and sunrise.

(22)

The AWP was used for the work zone located along the eastbound lanes of US32/33/50, while standard pavement marking was used for the work zone located along the westbound 

lanes. For each type of pavement marking, an area before, during, and after the lane shift was studied for 

the work zones. Figure 16 shows the work zones during the daytime hours, and figure 17 shows the work 

zones during the nighttime hours.
20

Figure 16. Photos. The AWP (left) and standard pavement marking (right) work zones on eastbound US32/33/50 during the day.

Figure 17. Photos. The AWP (left) and standard pavement marking (right) work zones on eastbound US32/33/50 at night.

While a site with a lane drop was not desirable, the site was selected due to proximity to the research 

office and capabilities of obtaining data during all weather conditions: daytime dry, nighttime dry, 

daytime wet, and nighttime wet. During construction, traffic was shifted over one lane to accommodate 

construction of half of the roadway at a time. The construction began during the spring of 2010 and 

continued through the fall of 2010. Data were only collected when the traffic was shifted to the inside 

shoulder of each direction of travel. During the construction, all exit ramps were open to traffic.

Throughout each of the work zones the posted speed limit was 55 miles per hour (89 kph).

The data from this site were collected with video cameras mounted inside research vehicles that followed 

subject vehicles through the work zone. Data were collected for both directions of travel during the same 

time periods over several months. The amount of precipitation was noted during each rain event. As the 

data collection for the Athens site occurred over a several-week period, the average rainfall during the 

periods of data collection equaled 0.53 inches (1.3 cm) for the nighttime data collection and 0.54 inches 

(1.4 cm) for the daytime data collection.

I-90, Ashtabula, Ohio

The I-90 project involved the reconstruction of the existing concrete pavement over a distance of 

approximately 5.75 miles (9.3 km) between Paine Road in Lake County to the Ashtabula County Line.
21

Specifically, the Ashtabula study site was located on I-90 between the interchanges with State Route 11 

(SR-11) and State Routes 84 and 193 (SR-84 and SR-193), as shown in figure 18.

Figure 18. Photo. I-90 crossover locations.

According to the Ohio DOT, the AADT in 2009 for this section of I-90 was 22,910 vehicles per day, with 

trucks representing 30 percent of the traffic.(24)

During the data collection months of August and 

September 2011, the average monthly rainfall was above 3.8 inches (9.7 cm), and there were a minimum 

of 11 hours 48 minutes between sunset and sunrise.

(22) The AWP was used for the double-lane crossover 

on the eastbound lanes of I-90, as shown in figure 19. Standard pavement marking was used for the 

double-lane crossover on the westbound lanes of I-90, as shown in figure 20. Views of each of the 

crossovers from within a vehicle can be seen in figure 21. 

Figure 19. Photo. AWP treatment location on I-90.

Eastern Crossover

Western Crossover
22

Figure 20. Photo. Standard pavement marking treatment location on I-90.

Figure 21. Photos. View of the AWP (left) and standard pavement marking (right) double-lane crossover.

During the construction period, the two eastbound travel lanes were shifted to the two westbound travel 

lanes through a two-lane crossover. After crossing the construction site, the eastbound travel lanes were 

shifted back to their original travel path. The construction of the eastbound travel lanes began in late 

November of 2010. At the onset of the project, neither the AWP nor standard pavement marking were 

installed along the two-lane crossovers due to concerns with temperatures at the site, which were 

substantially below the desired 50-degree Fahrenheit (10 degrees Celsius) temperature for installation of 

the pavement markings. In the interim, a fast-dry temporary pavement marking was utilized for the 

pavement marking with raised pavement markers throughout the work zone until the temperatures in the 

spring (April) were adequate to install the AWP and standard pavement markings. 

Both crossovers utilized similar geometric features, such as a reverse curve with a radius of 3,125 feet

(952.5 m). The total length of the western crossover was 1,095 feet (333.8 m), and the total length of the 

eastern crossover was 1,085 feet (331 m). Throughout the work zone, the posted speed limit was 55 miles 

per hour (89 kph) for both directions of travel.

Data at the Ashtabula site were collected with sensors along the entire length of each crossover. Due to 

the number of sensors required for each crossover, the data were not collected simultaneously on both 

crossovers. Therefore, the amount of precipitation was noted for each period to assure similar 

precipitation events were compared in the analysis. The cumulative precipitation occurring during the 2-

hour data collection for the AWP was 0.34 inches (.86 cm), while the precipitation for the standard 
23

pavement marking was 0.42 inches (1.1 cm). As the rainfall amounts were similar during the nighttime 

data collection periods, rainfall was not considered a confounding variable.

DISCUSSION OF POTENTIAL FOR DATA COLLECTION

The data collection for this project ultimately depended on the potential for rain events during nighttime 

conditions. The research team looked into the potential for data collection at each of the five sites by 

summarizing the average rainfall and nighttime hours during each month of the year, as shown in tables 3 

and 4. Average rainfall totals are provided as the average monthly rainfall from 2003 to 2008. The 

available time for data collection during nighttime hours was calculated as the available time between 

ensuing darkness (30 minutes after sunset) and midnight, after which it was assumed that data collection 

probably was not feasible due to low traffic volumes and extended team travel time.

Table 3. Average monthly rainfall totals (inches).

(22)

1 inch = 25.4 mm

Table 4. Average available time for nighttime data collection (hh:mm).

(22)

The rainfall totals were interesting. Surprisingly, the average rainfall for each of the sites is fairly constant 

over time, with little variation between the seasons of the year. However, the available data collection 

time improves during winter months, as the winter solstice takes place in mid-December. The earlier 

sunset also affords the potential for higher traffic volumes during data collection, since the researchers 

would be collecting a portion of the peak hour traffic in some cases. Therefore, sites studied during winter 

months have the higher likelihood of collecting larger samples of data because the nighttime data 

collection time period is longer and includes potentially higher total traffic volumes by including a 

portion of the peak period travel.

Location Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec

Henderson, NC 3.6 3.5 3.8 3.0 3.9 3.8 4.3 4.7 3.7 3.4 3.3 3.3

Chapel Hill, NC 3.7 3.9 4.3 3.3 4.5 4.4 4.1 4.4 3.3 3.5 3.5 3.5

Winston-Salem, NC 3.3 3.3 3.7 2.8 4.0 3.8 4.5 3.9 3.5 3.5 3.0 3.4

Ashtabula, OH 2.7 2.2 2.8 3.6 4.0 4.5 4.7 3.8 4.3 4.0 3.7 3.5

Athens, OH 2.6 2.7 3.3 3.4 4.4 3.7 4.4 3.3 2.8 2.7 3.3 3.0

Location Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec

Henderson, NC 6:08 5:35 5:09 4:42 4:17 3:57 3:59 4:26 5:09 5:52 6:25 6:30

Chapel Hill, NC 6:04 5:32 5:06 4:40 4:15 3:56 3:57 4:24 5:07 5:49 6:21 6:26

Winston-Salem, NC 6:00 5:28 5:02 4:35 4:10 3:50 3:52 4:19 5:02 5:45 6:17 6:22

Ashtabula, OH 6:10 5:55 4:49 3:45 2:54 2:53 2:57 3:29 3:58 4:30 5:55 6:52

Athens, OH 5:58 5:46 4:44 3:44 2:55 2:36 2:39 3:09 3:33 5:00 5:23 6:20
24
25

RETROREFLECTIVITY 

Retroreflectivity is a measure of the ability of a sign, pavement marking, or other countermeasure to 

reflect light back toward the source (i.e., the driver in the car). The research team took retroreflectivity 

measurements of standard pavement marking and AWP during the same day the markings were applied.

The team formally documented the field installations of each pavement marking type from four different 

contractors in two different States, each given basic training and/or guidance from 3M on the installation 

of the new AWP prior to installation. No guidance was given on how to install the standard pavement 

marking, allowing the research team to document real-world field installations currently used in practice 

based on the specifications given by the respective DOTs.

DATA COLLECTION

Retroreflectometer

The retroreflectometer used in this study is in accordance with the standard test method for measurement 

of retroreflective pavement marking materials: it has an illumination angle of 1.24 degrees and an 

observation angle of 2.29 degrees, simulating an observation distance of 98.4 feet (30 m). The 

illumination and observation fields are shown in figure 22. Note how the illumination field, when 

measuring retroreflectivity of a wet continuous pavement, is not immediately below the device, as 

opposed to normal operation. This shift occurs when the wet night rails are attached, allowing the 

machine to measure pavement continually wetted by a water sprayer directly in front of it. All tests were 

to be performed with the wet night rails attached, per 3M instruction.

1 mm = .04 inch

Figure 22. Diagram. Retroreflectance measurement fields.

Retroreflectivity values were collected approximately 30 minutes after the pavement markings were laid 

on the road surface. Before collecting retroreflectivity data, the section of pavement marking was swept to 

remove any loose optical elements and glass beads that did not bind to the pavement marking. This 

allowed for a more accurate retroreflective measurement because, over time, these elements would 

dislodge and scatter. One by one, three retroreflectivity tests were conducted on the test segment: the 

standard dry test (ASTM E1710-05), the wet continuous test (ASTM E2177-01), and the wet recovery 

test (ASTM E2176-01).
26

For the wetting of the pavement during the wet continuous test, the research team used a 4-gallon (15-

liter) rechargeable sprayer. As suggested by 3M, the research team added a few drops of regular 

dishwashing soap to the water in the sprayer’s reservoir, about 1 tablespoon (14.8 ml) for 3 gallons (11.4 

liters) of water, which is enough to give the solution a very light tint. Per the ASTM test specifications, 

the water was sprayed on the pavement marking at a rate of 9 inches per hour, and retroreflectivity 

readings were measured until the displayed values were constant with each recording. The water spraying 

then ceased, and the wet recovery retroreflectivity value was measured 45 seconds after.

Retroreflectivity Test and Samples

Following the aforementioned procedures, retroreflectivity tests were conducted at four of the five test 

sites: I-85 (Henderson, NC), US-15/501 (Chapel Hill, NC), US-421 (Winston-Salem, NC), and I-90 

(Ashtabula, OH). Retroreflectivity measurements were taken at the US-32/33/50 site (Athens, OH); 

however, immediately after the markings were applied for both sections (the AWP and standard pavement

marking), the contractor sprayed water and applied straw to the median, which affected the 

measurements. Therefore, these data were not included in the analyses. 

Sample sizes were dependent on the team’s ability to collect data in various events. For instance, the 

Henderson site was a large facility that had a rolling road block. The first two rolling road blocks allowed 

each of the markings to dry. Then several more rolling road blocks were conducted based on the 

willingness of the contractor at the site. In all, a minimum of 10 retroreflectivity readings were conducted 

for each pavement marking type under the 3 simulated weather conditions (dry, wet continuous, and wet 

recovery). In addition, both yellow and white pavement markings were sampled. Table 5 shows the 

individual sample for each simulated weather condition for each pavement marking color and type. For 

example, in Henderson, 11 white treatment pavement marking readings was taken under dry conditions, 

followed by 11 separate wet and wet recovery readings.

Table 5. Retroreflectivity sample size obtained at each of the four sites studied.

Site Treatment (AWP) Comparison (Standard)

White Yellow White Yellow

I-85 Henderson, NC 11 11 11 11

US-15/501 Chapel Hill, NC 10 10 10 10

US-421 Winston-Salem, NC 22 26 20 16

I-90 Ashtabula, OH 9 6 11 6

Neither the I-85 nor the US-15/501 data sets needed any reduction in the measurements taken. As for the 

US-421 test site, three retroreflectometer readings were taken on the temporary bridge installments on 

both directions of the highway for both white and yellow striping under each simulated weather condition. 

The bridge deck striping required additional thermo-treatment prior to pavement marking installation. The 

inclusion of thermoplastic greatly enhanced the retroreflectivity and would bias the results; therefore, 

these measurements taken on the bridge decks were removed from the samples to be analyzed. 

ANALYSIS METHODOLOGY

At each test site, there were a variety of different retroreflectivity measurements. The three factors that 

distinguished results included (1) pavement marking treatment (the AWP or standard pavement marking), 

(2) pavement marking color (white or yellow), and (3) simulated weather conditions (dry, wet continuous, 

or wet recovery). Therefore, each site had 12 distinct retroreflectivity measurement sets. The 

measurement sets, defined by the combinations of factors, are listed in table 6.
27

Table 6. List of retroreflectivity samples at each site.

Retroreflectivity Samples

# Pavement Marking Treatment Pavement Marking Color Simulated Weather Condition

1 AWP White Dry

2 AWP White Wet Continuous

3 AWP White Wet Recovery

4 AWP Yellow Dry

5 AWP Yellow Wet Continuous

6 AWP Yellow Wet Recovery

7 Standard White Dry

8 Standard White Wet Continuous

9 Standard White Wet Recovery

10 Standard Yellow Dry

11 Standard Yellow Wet Continuous

12 Standard Yellow Wet Recovery

For each sample, the average and standard deviation were calculated. A standard two-sample t-test was 

conducted assuming unequal variances. Samples treated with the AWP pavement marking were compared 

to standard pavement marking treatment samples of the same striping color under the same simulated 

weather condition, and at the same locations for each weather condition. Finally, for each comparison, the 

percent difference between retroreflectivity averages was calculated.

RESULTS 

There were marked differences observed between retroreflectivity readings at each of the three sites even 

when treatments, striping colors, pavement type, and weather conditions were the same. Therefore, sites 

were rendered independent of one another, and the samples were analyzed separately. 

For each site, the averages and standard deviations were calculated for the two types of pavement 

marking systems installed for both white and yellow striping under the three simulated weather 

conditions. The mean difference was calculated by subtracting each standard pavement marking average 

(baseline) from the AWP average. The percent difference was found by dividing the mean difference by 

the baseline standard pavement marking average. An unpaired two sample t-test was used to compare all 

the AWP samples with standard samples. The significance level was set to a 95 percent confidence 

interval. The null hypothesis assumed that the two pavement marking samples had similar retroreflectivity 

measurements. All four sites are summarized in table 7.
28

Table 7. Summary of statistics for retroreflectivity readings at all test sites.

Retroreflectivity White Yellow

 (mcd/m2/lx)

Marking Dry Wet 

Continuous

Wet 

Recovery Dry Wet 

Continuous

Wet 

Recovery

I-85 (Henderson, NC)

Standard Avg. 344.6 220.0 315.7 155.5 101.4 137.7

Std. Dev. 24.6 12.8 21.9 17.3 15.3 17.7

AWP Avg. 740.7 464.0 679.8 561.4 386.0 461.5

Std. Dev. 65.4 202.2 87.8 70.2 77.5 35.6

Mean Difference +396.1 +244.0 +364.1 +405.9 +284.6 +323.8

Percent Difference +114.9% +110.9% +115.3% +261.0% +280.7% +235.1%

P(T<=t) two-tail < 0.001* 0.0042* < 0.001* < 0.001* < 0.001* < 0.001*

US-15/501 (Chapel 

Hill, NC)

Standard Avg. 166.2 10.2 9.3 122.6 8.8 37.9

Std. Dev. 40.7 1.7 9.8 20.9 1.0 9.2

AWP Avg. 507.2 107.6 294.5 418.5 44.6 223.0

Std. Dev. 175.4 39.2 123.9 133.0 16.8 76.3

Mean Difference +341.0 +97.4 +285.2 +295.9 +35.8 +185.1

Percent Difference +205.2% +954.9% +3066.7% +241.4% +406.8% +488.4%

P(T<=t) two-tail < 0.001* < 0.001* < 0.001* < 0.001* < 0.001* < 0.001*

US-421 (WinstonSalem, NC)

Standard Avg. 274.9 37.9 54.7 194.0 22.5 26.7

Std. Dev. 33.1 10.0 13.4 12.9 8.0 3.6

AWP Avg. 463.2 39.8 140.4 372.6 36.8 165.7

Std. Dev. 36.0 17.9 41.4 41.1 12.2 26.5

Mean Difference +188.3 +1.9 +85.7 +178.6 +14.3 +139.0

Percent Difference +68.5% +5.0% +156.7% +92.1% +63.6% +520.6%

P(T<=t) two-tail < 0.001* 0.69 < 0.001* < 0.001* < 0.001* < 0.001*

I-90 (Ashtabula, OH)

Standard Avg. 110.5 5.3 9.0 35.5 4.0 2.7

Std. Dev. 32.3 1.7 3.5 9.7 0.8 1.9

AWP Avg. 536.1 17.2 230.3 263.2 17.4 166.2

Std. Dev. 167 6.3 88.7 44.4 5.7 68.9

Mean Difference +425.6 +11.9 +221.3 +227.7 +13.4 +163.5

Percent Difference +79.4% +69.2% +96.1% +86.5% +77.0% +98.4%

P(T<=t) two-tail < 0.001* < 0.001* < 0.001* < 0.001* 0.002* 0.002*

* p-value is statistically significant (α = 0.05)

The p-values for the overwhelming majority of retroreflectivity tests were less than the significance level 

selected (α = 0.05); therefore, the null hypotheses that the two pavement markings had similar 

retroreflectivity measurements were rejected. The results of the t-tests imply that a statistically significant 

difference can be expected between the AWP and standard pavement markings’ retroreflectivity during 

all three weather scenarios and pavement marking colors at each individual site. Major improvements 

were noted across the board when the AWP was used; however, the most significant findings were in the 

increased retroreflectivity results for yellow pavement marking. In general, yellow pavement marking was 

less reflective than white pavement marking for standard marking and the AWP, so the notable increase 

in retroreflectivity could be very important for transportation agencies.
29

Research team members cannot readily explain the major differences in retroreflectivity between sites.

Two possible theories may explain the differences. First, although a 3M representative was on-hand for 

the calibration of machinery and during the actual application of the pavement markings, the research 

team suspects there was some human error during the application process due to the differences in 

machinery used and experience and comfort of the team in actually applying different pavement marking

mixtures. If this is true, pavement marking applications should be expected to have a lot of variability,

which suggests that minimum pavement marking retroreflectivity standards should be considered by 

public entities (indeed, many agencies use such standards). Second, the team suspects that there may have 

been differences in results based on the ability of the pavement marking to shed water in some instances, 

possibly due to differences in pavement type, pavement roughness, or maybe even grade or crown in 

various roadways studied. Unfortunately, this possibility was not accounted for in the analysis.

CONCLUSIONS AND RECOMMENDATIONS

As shown in table 7, based on the collection of retroreflectivity readings and the statistical analysis, it 

may be concluded that, when correctly installed, the AWP markings will provide a statistically significant 

increase in retroreflection when compared to standard pavement markings. In addition, the variability in 

the retroreflectivity readings for both pavement markings types being studied suggests that pavement 

marking contractors are not using consistent methods for applying their pavement markings. 

In this study, the retroreflectivity readings were taken only after the initial installment of pavement 

markings. As the temporary work zone markings are expected to last at least 1 year, it is suggested that 

further investigation be conducted in examining how the AWP markings degrade over varying periods of 

time and under various traffic conditions.

Validating that the AWP markings were indeed more retroreflective, the research team further sought to 

examine whether these pavement markings would increase lane visibility and thereby improve navigation 

through work zone detours under nighttime, rainy conditions. The measures of effectiveness, including 

speeds, lane encroachments, and lane deviations, are discussed in the following chapters.
30
31

SPEEDS 

AWP has been proven to be more retroreflective than the standard pavement marking. As a follow-up to 

that finding, the research team sought to discover how this new pavement marking affects driver 

behavior. The analysis of speed data was used as an indication of a motorist’s perceived risk while 

traveling through a work zone guided by the AWP or standard pavement marking through lane shifts and 

crossovers. Comparisons are made between entry and exit curves for both standard and AWP.

DATA COLLECTION METHODS

LIDAR Speed Gun

Whenever possible, speeds were collected using a Class I laser speed gun. The speed gun uses light 

detection and ranging (LIDAR) technology, which uses a pulse of light that calculated the distance to the 

nearest vehicle 200 times per second, allowing for extremely accurate readings when calibrated.

According to Laser Atlanta, the gun is capable of providing speeds within ± 1 mile per hour (± 1.16 kph).

The speed gun is capable of acquiring its intended target in as little as 0.3 seconds, provided the user can 

acquire the target quickly and follow it with a steady hand. Speeds were recorded as vehicles were 

approaching or departing the location of the speed gun user, with no more than ± 5 degrees of angle 

between the user and vehicle being recorded to reduce any potential accuracy issues.

Two independent observers collected speed data concurrently in the crossover sections at the treatment 

and comparison locations when at the entry or exit in the same direction of travel. This meant that the 

population of drivers in the comparison and treatment speed bins was very similar as they entered and 

exited the work zone. In addition, the rain intensity at both segments was approximately the same 

throughout the entire data collection period. Of course, longer work zone segments likely had different 

rain intensities throughout the speed data collection time period; however, the differences were assumed 

to be negligible since data at longer sites were collected after a suitable period of time elapsed once rain 

was observed at both sites. If a platoon of vehicles was observed, the lead vehicle of the platoon was 

recorded as a single measurement and the following vehicles were ignored. This allowed the team to only 

collect speed data on vehicles that were maneuvering through the crossover at a safe speed as determined 

by the driver of that particular vehicle. Following vehicles would have likely been affected by the lead

vehicle’s decision about what the safe traversing speed was for the curve and would not have been a true 

representation of what that driver’s speed may have been if not following a platoon.

Video Calculated Speeds

Speed data also were calculated from videos taken at the I-85, US-15/501, US-421, and US-32/33/50 test 

sites. The research team had three motivations for using videos to collect speed data. First, this method 

allowed speeds to be obtained during rain events at site locations farther from the research team’s offices 

(i.e., those requiring longer travel times), especially when the probability of a rain event was low. Second, 

speed samples could be collected for the exact same drivers through both crossovers at the same location 

every time, whereas the speed gun measurements included some human error in obtaining speeds at the 

same location in the transition every time. Third, video was required for lane encroachment analysis and 

was readily available for speed usage. Using the video taken to collect lane encroachments and volumes, 

speeds were collected from the same populations examined for lane encroachments rates.

The primary drawbacks to this method were two-fold. First, there is human error when using a stopwatch 

and known distance to collect speeds. To counter this, a time-stamp overlay was utilized to obtain 
32

accurate times within 1/30th of a frame. Second, the angle to the crossover where speeds were taken meant 

that some occlusion existed. Since occlusion was consistent at all known points, we assumed this to be 

consistent among all measurements.

Speeds were calculated in the following steps: 

1. Record for no less than 2 hours at each curve.

2. Take physical distance measurements at each curve.

3. Calibrate measurements with the speed gun.

4. Watch the videos in the lab and record the times it took lead vehicles to traverse the measured 

distances.

5. Calculate speeds using timing macros and functions in a computer-based database program.

Sensor Calculated Speeds

The research team used sensors to determine the speeds of vehicles traveling along the two-lane crossover 

on I-90. The sensors were located at 100-foot (30.5-m) intervals along the right and left travel lanes for 

the eastbound direction in which the AWP pavement markings were utilized. Along the westbound 

direction of travel, in which the standard pavement marking were utilized, the sensors were only located 

along the right travel lane due to the lack of an inside shoulder. The speeds were calculated by dividing 

the spacing of two sequential sensors by the time taken by a vehicle to traverse the distance between the 

sensors.

The sensors included a SHARP distance measuring sensor unit (Part No. GP2Y0A710K0F) and a 

MicroStrain SG-Link® Wireless Strain Node. The SHARP sensor was composed of a position-sensitive 

detector, infrared emitting diode, and a signal processing circuit, which provide a voltage output that 

corresponds to the detection distance (SHARP, 2006). The operating range for the SHARP sensors is 3.28 

to 18.05 feet (1 to 5.5 m). The SG-Link® Wireless Strain Node was used to process the voltage output of

the SHARP sensor and transmit it to a MicroStrain WSDA®-Base-mXRSTM Wireless Base Station 

connected to a laptop. The laptop connected with the Base Station had the MicroStrain Node 

Commander® software installed to conduct synchronous sampling for multiple SG-Link® Nodes. The 

SHARP sensor and the SG-Link® Node were installed in a plastic enclosure and were each connected to 

batteries for a power supply. The internal view of a sensor with the lid of the plastic enclosure removed is 

shown in figure 23. To securely place the sensors in the field, mounting bases were constructed using 

lumber, Velcro, and brackets. These mounting bases helped to elevate the sensors so they could measure 

the axle of the passing vehicle (opposed to the body of the vehicle) without being impeded by the crossslope of the roadway. Also, the bases provided a means of securing the sensors to the graded shoulder. 

The sensor placed in the mounting base can be seen in figure 24.
33

Figure 23. Photo. Internal view of sensor.

Figure 24. Photo. Sensor on mounting base.
34

Once the data were collected, the researchers needed to filter the data since there were continuous voltage 

readings for the entire data collection period. The team observed spikes in the voltage readings from a 

constant baseline level. This behavior is expected, as the output voltage increases as the detection distance 

decreases. Therefore, the baseline low voltage represents nothing present within the operating range, 3.28 

to 18.05 feet (1 to 5.5 m). When the voltage increased above 1 volt, the sensor detected an object within 

the operating range. The researchers determined that to calculate speeds using the data collected, 

determining the initial voltage peak for each vehicle was necessary. This initial voltage peak would 

correspond to the front axle of the vehicle. As a result, a macro was created to filter through the data to 

find the initial voltage peaks. Once all of the readings were properly filtered, the sensor data, which 

produced a time-stamp for each collected reading, was utilized to determine vehicle speed. Theoretically, 

as a vehicle progressed through the crossover it would pass in front of each of the sensors and produce 

readings on each. Thus, the elapsed time from sensor to sensor can be determined by lining up the 

readings and, subsequently, the speed of the vehicle was calculated.

ANALYSIS METHODOLOGY

This section outlines the differences in data collection at each of the sites, any particular problems with 

data collection, and sample sizes utilized during the data collection effort. Basic statistical analysis was 

conducted for the speed data collected at each test site after outliers were removed. First, averages and 

standard deviations were calculated from the filtered speed data. The samples’ averages and mean 

differences were then compared and calculated. Two different statistical tests were utilized. For North 

Carolina sites, the unpaired t-tests assuming unequal variances were used to test the significance levels in 

comparing speed samples. For Ohio sites, analysis of variance (ANOVA) was used. All sites were 

independent of one another and required separate analyses.

I-85 

The team sought to obtain speeds during nighttime rain events through the northern and southern 

crossovers at I-85 using a LIDAR speed gun. However, due to the variable nature of rain events and the 

lack of operators and equipment, the team was only able to record a single speed sample at each 

crossover. The two speed samples were taken from vehicles well within their respective crossovers.

Samples of only 15 reliable speeds were taken in the northern crossover (standard pavement marking), 

and 46 were taken in the southern crossover (the AWP), each sample in a platoon only including the lead 

vehicle. The samples were a bit small for any confidence in statistical analysis, but they are nonetheless 

available.

Upon further investigation in the office, camera angles at the northern crossover allowed speeds to be 

obtained at the entry and exit curves. (Collecting speeds at the southern crossover using video was not 

feasible because the relative camera angles presented too much occlusion from vehicles to consistently 

and accurately confirm distance.) Although not the primary goal, the team found it useful to examine the 

possibility of mean speed differences at the entry and exit of this single crossover. From the video, sample 

sizes of 208 and 211 were collected from the northern crossovers’ entry and exit curves, respectively. 

Outliers less or greater than 2 standard deviations were removed from the samples, reducing the sample 

sizes to 200 and 202, respectively.

Given the two speed samples, initially two separate statistical comparisons were conducted for data 

collected using both available samples. First, the mean difference was calculated for the northern and 

southern crossovers. As the sample sizes were small when comparing the AWP to standard pavement 

marking, the analysts decided against further analysis of this initial data set, since the findings could be 

incorrectly used with findings from other sites with similar analyses but much more data. Therefore, the 

only remaining speed analysis conducted at this particular site was from the northern crossover entry and 
35

exit curves. The two samples were compared using the unpaired two-sample t-test assuming unequal 

variances.

US-15/501 

To obtain speeds at the US-15/501 test site, the team used the alternative video extraction method. As 

mentioned earlier, for this site only one curve was feasible for analysis due to the signal at the northern 

crossover. The research team worked with the pavement marking contractor and 3M to arrange for the 

AWP to be installed on top of the standard pavement marking after a series of rain event data had been 

collected on the standard pavement marking. After the AWP installation, video footage of nighttime rain 

conditions was again recorded from the same camera installation locations for future data extraction. 

Although other samples of rainy nighttime data were available, only two samples were utilized: one 

sample during the time the lane shift was treated with standard pavement marking and the other after the 

AWP overlay. The primary reason for this decision was the team hypothesized that rain intensity would 

have a marked effect on drivers’ ability to safely traverse the work zone, so comparisons of greatly 

different rain events were not examined. 

During the data collection for standard pavement marking, the rain intensity was approximately 0.50 

inches (1.27 cm) per hour, while the approximate rain intensity for the AWP overlay was 0.40 inches (1.0 

cm) per hour. A sample of 218 speeds was collected when the site was treated with standard pavement 

marking, and 275 speeds were collected with the AWP. The sample size was sufficiently large; therefore, 

the analyst removed outliers beyond 2 standard deviations from both samples, thereby reducing the 

samples sizes to 212 speeds and 262 speeds for standard pavement marking and the AWP, respectively.

The two samples were compared using the unpaired two-sample t-test assuming unequal variances.

US-421 

As described earlier, the field setup for the US-421 site was ideal for data collection because of the two

bridges being reconstructed simultaneously. Therefore, both entry and exit crossovers along each 

direction of travel were analyzed independently, which is detailed previously in figure 14. This allowed 

the research team to utilize four synchronized videos of each crossover location. Speeds were taken for 

over 200 lead vehicles at each of the curves.

In all, 240 and 248 speeds were recorded at the AWP entry and exit curves, respectively, and 219 and 233 

speeds were recorded for the standard pavement marking entry and exit curves, respectively. Again, any 

speeds less than or greater than 2 standard deviations of their curve speed averages were deemed outliers 

and filtered from the data. After the data filtration, there were 227, 239, 205, and 220 speeds for the AWP 

and standard pavement marking curves. The t-tests conducted compared the AWP entry speeds versus 

standard pavement marking entry speeds, the AWP exit speeds versus standard pavement marking exits 

speed, the AWP entry versus the AWP exit, and standard pavement marking entry versus standard 

pavement marking exit during nighttime rain conditions.

US-32/33/50

To obtain speeds at the US-32/33/50 test site, the video extraction method was utilized. Based on the 

placement of the video cameras, the speed data could be extracted by subdividing the work zone into 

three separate sections: at the beginning of the lane shift, in the midpoint of the lane shift, and at the end 

of the lane shift. Due to the location of an exit ramp near the lane shift, any vehicle exiting the highway 

via the ramp was excluded from the positional speed analysis. Similar vehicles were utilized for the speed 
36

data extraction as well as the lateral lane placement data. Data were collected during daytime and 

nighttime rain conditions as well as during nighttime dry conditions through the months of May, June, 

and July 2010. The sample sizes for the data collection ranged from 54 vehicles to 121 vehicles due to the 

elimination of vehicles that were not lead vehicles in a platoon and those that did not continue through the 

entire lane shift.

The samples at the US-32/33/50 test site included speeds along the travel lanes for entering, within, and 

exiting the lane shifts for both the AWP pavement markings and the standard pavement markings. A oneway analysis of variance was conducted to compare the speeds using several hypotheses, listed below:

• Mean speeds were similar for entering, within, and exiting lane shifts for the AWP and standard 

pavement marking sites.

• Mean entering speeds were similar for the AWP as compared to the standard pavement marking.

• Mean speeds within the lane shift were similar for the AWP as compared to the standard 

pavement marking.

• Mean exiting speeds were similar for the AWP as compared to the standard pavement marking. 

I-90

Speeds at the I-90 test site were collected using sensors. Data were collected during daytime and 

nighttime dry conditions as well as during nighttime wet conditions through the months of August and 

September 2011. The sample sizes for the data collection ranged from 300 vehicles to 1,504 vehicles. The 

samples at the I-90 test site included speeds along the travel lanes for both the standard pavement marking

and the AWP crossovers. A one-way analysis of variance was conducted to compare the speeds assuming 

a null hypothesis that stated that the mean speeds were similar for the AWP and the standard pavement 

marking sites. The analyses were conducted for daytime dry conditions, nighttime rain conditions, and 

nighttime dry conditions.

RESULTS 

From the speed data obtained by LIDAR, video extraction, and sensors, the research team compared 

various samples for each of the five test sites. Mean speeds were particularly dependent on site geometry, 

conditions, and characteristics; therefore, samples would only be compared with other samples from the 

same site. The mean speeds, standard deviations, and mean differences were first calculated, followed by 

a statistical analysis using either t-tests or ANOVA. Table 8 displays a summary table of the speed data 

collected and analyzed for all five test sites.
37

Table 8. Summary of statistics for speeds.

* p-value is statistically significant (α = 0.05)

1 mph = 1.6 kph

I-85

Speeds were compared between the entry and exit curves delineated with standard pavement marking at 

the northern crossover. The work zone speed limit was posted at 55 miles per hour (89 kph). Actual 

speeds at the northern crossover ranged from 44.9 to 46.3 miles per hour (72.3 to 74.5 kph). From the 

analysis, a higher mean speed was found for the exit curve. Furthermore, the p-value was statistically 

Site Light Weather Paint Location Mean Speed (MPH) Standard 

Deviation

Mean 

Difference

P(T<=t) 

two-tail

Standard Entry Curve 44.9 5.2

Standard Exit Curve 46.3 5.2

Standard Entry Curve 38.7 6.2

AWP Entry Curve 42.2 6.4

Standard Entry Crossover 50.7 4.3

AWP Entry Crossover 51.5 4.6

Standard Exit Crossover 56.7 5.2

AWP Exit Crossover 55.3 8.3

Standard Entry Crossover 50.7 4.3

Standard Exit Crossover 56.7 5.2

AWP Entry Crossover 51.5 4.6

AWP Exit Crossover 55.3 8.3

Standard Entry Crossover 35.7 3.6

AWP Entry Crossover 37.3 6.4

Standard Within Workzone 36.4 5.2

AWP Within Workzone 37.9 5.1

Standard Exit Crossover 36.5 5.9

AWP Exit Crossover 38.1 5.8

Standard Entry Crossover 34.4 5.3

AWP Entry Crossover 38.9 4.7

Standard Within Workzone 37.2 4.8

AWP Within Workzone 37.8 5.5

Standard Exit Crossover 38.6 4.2

AWP Exit Crossover 36.3 5.0

Standard Entry Crossover 36.3 3.7

AWP Entry Crossover 39.1 3.5

Standard Within Workzone 37.8 4.1

AWP Within Workzone 38.1 3.5

Standard Exit Crossover 39.3 5.1

AWP Exit Crossover 37.6 3.5

Standard Exit Crossover 52.8 8.3

AWP Entry Crossover 56.8 11.0

Standard Exit Crossover 55.9 9.8

AWP Entry Crossover 54.6 8.7

Standard Exit Crossover 57.0 7.8

AWP Entry Crossover 52.1 10.1 Night Rain -5.0 < .001*

Rain

Clear

Clear

Clear

1.6 0.526

Rain

Rain

Rain

Rain

5.9 < 0.001*

0.039*

0.057

Night

0.8

-1.3

3.8 < 0.001*

1.4 0.009*

< 0.001*

I-85

US 15-501 Night

Night

3.5

Day

Night 

Night 

US-32/33/50

US-421

-1.3 0.312

4.0 0.714

1.5 0.476

4.5 < 0.001*

I-90

Day

Night 

1.6 0.590

0.982

0.057

< 0.001*

0.997

-1.8 0.102

0.3

2.9

-2.3

0.6
38

significant with 95 percent confidence. Speeds were likely lower than the posted speed limit because the 

site contained a single lane crossover with barriers on each side.

When comparing the speeds obtained from LIDAR speed guns, the northern crossover yielded a mean 

speed of 43.9 miles per hour (70.7 kph) based on a very limited sample size of 15, whereas the AWP 

(southern) crossover yielded a mean speed of 41.7 miles per hour (67.1 kph) using a sample size of 46. 

This resulted in a mean difference of 2.2 miles per hour (3.5 kph). From this limited analysis, drivers 

drove faster through the section with standard pavement marking than through the section with the AWP. 

US-15/501

From the statistical analysis of the two samples obtained at US-15/501, mean speeds of 38.7 and 42.2 

miles per hour (62.3 to 68 kph) were calculated for the standard pavement marking and the AWP entry 

curves, respectively. At a confidence interval of 95 percent, the mean speed of the AWP data was 

significantly greater (3.5 miles per hour, or 5.6 kph) than that of the standard pavement marking data. 

Both averages were noticeably lower than the posted speed limit of 45 miles per hour (72 kph).

US-421

The averages and standard deviations were calculated for the samples collected for each of the curves at 

the US-421 test site. The work zone speed limit was posted at 55 miles per hour (89 kph). Based on 

results from video data extraction, the mean speeds on each of the four crossovers ranged between 50.7 

and 56.7 miles per hour (81.6 and 91.2 kph). Basic findings from entry and exit curves include the 

following:

• For both standard pavement marking curves and the AWP curves, the average speeds at the entry 

curves were lower than those of the exit curves. 

• Average speeds on both entering curves were less than the posted speed limit.

• Average speeds on both exiting curves were just above the posted speed limit.

• Differences between the entering and exiting speeds were 3.0 to 6.0 miles per hour (4.8 to 9.7 

kph).

More important are the results when comparing standard pavement marking and the AWP and the entry 

versus exit for each pavement marking type. The findings from this comparison at this site are the 

following:

• The entry curve speeds in the sections with the standard pavement marking were compared to 

those with the AWP. The mean speed was slightly greater than 1 mile per hour (1.6 kph) faster

for the AWP. A p-value of 0.057 indicates that there was no statistically significant difference 

between speeds at the entry curves of both pavement markings, although the difference was 

practically significant (90+ percent confidence).

• In comparing the exit curves, the mean difference was just below 1.5 miles per hour (2.4 kph).

Surprisingly, the exit curve with standard pavement marking had a higher mean speed than that 

with the AWP. The p-value calculated shows this finding to be significant with 95 percent 

confidence. 

• Finally, when comparing entry and exit curves of the same pavement marking, there was a mean 

difference of at least 3.5 miles per hour (5.6 kph), again indicating drivers exit the work zone 

faster than when entering. Both t-tests give p-values much less than the significance level. 
39

The findings from the entry and exit curve comparison support the findings from the I-85 test site, in 

which higher speeds were observed in the exit curve. In addition, although not statistically significant, the 

findings between standard and the AWP pavement markings at similar crossovers were comparable to the 

results from US-15/501.

US-32/33/50

The averages and standard deviations were calculated for the samples collected for each of the crossovers 

at the US-32/33/50 test site. The posted work zone speed limit was set at 55 miles per hour (89 kph). 

Based on results from video data extraction, the mean speeds ranged between 34.4 to 39.3 miles per hour 

(55.2 to 63.2 kph) for the various conditions—much lower than the posted speed limit. Substantial 

deviations from the posted speed limit can likely be attributed to the barriers present along the work zone, 

the single lane, and even the high visitor population that was not familiar with the roadway geometry.

Statistical tests were used to determine if the mean speeds of the AWP crossovers were significantly 

different from those of the standard pavement marking crossovers. The one-way analysis of variance was 

utilized to compare the mean speeds. Due to heterogeneous variances, the Welch’s modification to the 

one-way analysis of variance was utilized, and the calculated F-value was based on an asymptotic 

distribution. In addition, due to the unequal variances and unequal sample sizes, the Games Howell post 

hoc test was utilized to examine specific differences within those samples tested in the one-way analysis 

of variance. Based on the one-way analysis of variance, it was determined that the mean speeds associated 

with the vehicles entering, within, and exiting the lane shift for the daytime rain, nighttime dry, and 

nighttime wet conditions were significantly different, as shown in table 9.

Table 9. US-32/33/50 speed ANOVA results.

Comparison Sum of Squares Degrees of 

Freedom

Mean 

Squares

F-Calc p-value

Daytime Rain Conditions

Between Groups

Within Groups

Total

328.57

14159.23

14487.79

5

183.62

65.713

27.44 2.843 0.17

Nighttime Dry Conditions

Between Groups

Within Groups

Total

555.89

7360.29

7916.17

5

200.07

111.18

15.93 7.171 0.000*

Nighttime Rain Conditions

Between Groups

Within Groups

Total

1044.29

9870.81

10915.10

5

411

208.86

24.02 8.696 0.000*

* p-value is statistically significant (α = 0.05)

For US-32/33/50, the ANOVA results indicated the following:

• The only statistically significant differences between the AWP and standard pavement marking

were during nighttime dry and rainy conditions.

• Although not statistically significant, it is noteworthy that the AWP and standard pavement 

marking seem to have a large effect on speeds exiting the crossovers at nighttime dry and rainy 

conditions (p-values of 0.102 and 0.057, respectively).
40

Another trend is found when examining the data in a graphical visualization. As figure 25 shows, when 

plotting the data by location in the work zone (entry, within, and exiting) versus speed, it is evident that 

speeds generally increased through the work zone in all conditions except nighttime with the AWP.

1 mph = 1.6 kph

Figure 25. Graphs. US-32/33/50 speed results by location in work zone.

These findings suggest that the speed differential from entry to exit for the AWP is much more 

pronounced than with standard pavement marking, with a decreasing trend from the entrance through the 

exit. This also suggests that drivers are likely more confident entering the work zone with the AWP, as 

evidenced by the fact that speeds are higher during these two conditions than any other.

I-90

The averages and standard deviations were calculated for the samples collected for each of the two-lane 

crossovers at the I-90 test site. The posted work zone speed limit was set at 55 miles per hour (89 kph).

Based on results from the sensor data extraction, the mean speeds ranged between 51.9 and 57.0 miles per 

hour (83.5 to 91.7 kph) for the various conditions. Average speeds on both two-lane crossovers were less 

than the posted speed limit for the AWP conditions, except for daytime, clear conditions. Average speeds 

during both the nighttime standard pavement marking conditions (clear and rain) exceeded the posted 

speed limit; however, the average speeds during the daytime clear conditions were less than the posted 

speed limit mainly due to the reduction of speeds through the work zone when workers were present, 

which continued through the end of the work zone.

Statistical tests were used to determine if the mean speed for the test site compared to the control site was 

statistically significant. Again, the one-way analysis of variance was utilized to compare the mean speeds.

Due to heterogeneous variances, the Welch’s modification to the one-way analysis of variance was 

utilized, and the calculated F-value was based on an asymptotic distribution. In addition, due to the 

unequal variances and unequal sample sizes, the Games Howell post hoc test was utilized to examine 

specific differences within those samples tested in the one-way analysis of variance. Based on the oneway analysis of variance, it was determined that the mean speeds associated with the vehicles traveling 

along the two-lane crossover for the three conditions were significantly different, as shown in table 10.
41

Table 10. I-90 speed analysis ANOVA results.

Comparison Sum of Squares Degrees of 

Freedom

Mean 

Squares

F-Calc p-value

All Conditions

Between Groups

Within Groups

Total

21596.97

522466.36

544063.33

5

1849.71

4319.39

87.93 51.32 0.000

* p-value is statistically significant (α = 0.05)

For I-90, the ANOVA results provided earlier in table 8 indicated the following:

• The only statistically significant differences between the AWP and standard pavement marking

were during nighttime rainy conditions.

• The mean speeds for those vehicles traveling along the two-lane crossover with the AWP were 

significantly lower than those vehicles traveling through the crossover with the standard 

pavement marking. However, unlike the US-32/33/50 site, the AWP was installed at an entry 

curve and the standard pavement marking was installed at an exit curve. Therefore, the difference 

in speeds at each crossover cannot be attributed solely to the pavement marking type, but could 

very likely be due to differences in entry versus exit curves. This is further validated by the 

findings at other sites with similar pavement markings at entry and exit curves, where major 

differences were noted between the two curves.

CONCLUSIONS AND RECOMMENDATIONS

Vehicle speed was used as a surrogate measure used to evaluate the safety performance of more reflective 

lane markings. From the analysis of the speed data, it is not clear what effect the more reflective 

pavement marking has on driver behavior, or if an increase or decrease in speeds is a positive effect.

Nonetheless, knowing the changes in speeds due to the new pavement marking should be instructive.

Speeds should be analyzed with some caution, for their variability and dependence on a number of 

factors. In the case of this study, major factors affecting speeds included (1) the geometry of highway 

crossover, particularly lane shifts, (2) the installation of highway detours and the nature of work zones, 

(3) the intensity of rain, and (4) the lighting conditions. The research team attempted to compensate for 

these factors by keeping them consistent (when possible) when comparing various samples.

At the I-85 test site, two issues arose. The geometry varied slightly, and more importantly, the intensity of 

the rain likely varied since the crossovers were so far apart (6 miles, or 9.7 km). This being the case, it 

was unreasonable to compare the AWP curves with the standard pavement marking curves. 

Consequently, the team was only able to compare entry with exit curves for the standard pavement 

marking installation. At a 95 percent confidence level, the researchers found a slight increase in speed at 

the exit curve. One could assume that drivers likely decelerate entering the crossover and accelerate 

slightly exiting the crossover.

Data collection at the US-15/501 site used the same curve for analysis, thus keeping the highway 

geometry and work zone layout consistent. This issue that remained was the rain intensity, since data 

were collected during different rain events. Standard pavement markings were initially installed and video 

was taken. When enough rainy nighttime footage was recorded, the standard markings were overlaid with 

the AWP pavement markings. Therefore, it was impossible to get identical rain events, even if the 

monthly rainfall was consistent during data collection periods. Every attempt was made to get similar rain 

intensities using rain data obtained from the National Oceanic and Atmospheric Administration (NOAA). 
42

From the footage used for analysis, the standard comparison sample was taken during a 2-hour period 

during which 1 inch (2.54 cm) of rain fell, whereas the AWP treatment sample saw just over 0.5 inches 

(1.27 cm) of rain for a 1 hour and 20 minute period, so the hourly rainfall intensities were very similar. 

Nonetheless, statistical analysis was used to compare the standard sample with the AWP sample. Speed 

was found to be higher on the AWP treatment sample. Even though average intensity was similar during 

both rain events, the researchers noted points of concern. First, the analysts observed consistent rainfall 

during the standard pavement marking data collection, while the rain intensity dramatically increased in 

the second half of the AWP data extraction. Second, possible changes in speed during phases of 

construction should be addressed. The standard comparison sample was collected just after the lane shift 

was constructed and opened as a detour. Even with familiar drivers, there can be an expected learning 

curve, thus decreasing maneuvering decision time and decreasing traveling speed until the mean speed 

could have returned to a steady state after the initial observation period.

The US-421 test site provided the most promising data from the North Carolina locations. Lane shift 

construction and opening was simultaneous as contractors worked to replace bridges on the four-lane 

divided highway. Both lane shifts had entry and exit crossovers side by side within a 0.5-mile (.8-km) 

stretch of highway. Therefore, the team was able to use synchronized video for data extraction and 

analysis. Some speed differences were noted when comparing the standard pavement marking curves with 

the AWP curves; however, the differences were small and conflicting (though statistically significant).

For instance, when comparing pavement marking types at the entry curves, speeds were higher on the 

sections with AWP. This finding was not validated at the entry curves, where the standard pavement 

marking correlated with slightly higher speeds. Last, on both the AWP and standard pavement marking

lane shifts, higher speeds were observed on exit curves than on entry curves.

The US-32/33/50 speed data analysis indicated that vehicles entering the lane shift utilizing the AWP 

during nighttime dry and rain conditions traveled at a higher rate of speed than on the lane shift utilizing 

the standard pavement marking. This is consistent with the theory that motorists will travel faster through 

a given length of highway due to the higher retroreflective pavement markings. On the other hand, the I90 test site speed data analysis indicated that vehicles traveling through the two-lane crossover utilizing 

the AWP during the nighttime rain conditions traveled at a lower rate of speed as compared to the 

crossover utlizing the standard pavement marking. However, this finding at I-90 should be looked into 

further because both pavement markings were applied on different curves (entry and exit).

Overall, based on the comparisons between the mean speeds at the US-15/501, US-421, US-32/33/50, and 

I-90 test sites, the team finds no conclusive evidence that drivers travel faster through work zone detours 

delineated with the AWP than those with the standard pavement marking. There was no consistent 

positive or negative speed change between the two pavement marking types; in fact, the drivers on the 

AWP sections exhibited both higher and lower speeds than those on the standard pavement marking

sections at each of the various crossovers studied.

In future studies, the research team recommends that speed data be collected by video extraction under 

the following circumstances:

1. Using synchronized video for highway curves similar in geometry and work zone layout.

2. Timing for speeds over a distance of at least 200 feet (61 m).

3. Calibrating measurements with supplemental speed gun recordings

4. Using larger samples sizes.

5. Removing outliers.

6. Developing macro-enabled databases for analysis.
43

LANE ENCROACHMENTS

AWP has been proven to be more retroreflective than standard pavement marking; however, the 

researchers found no conclusive evidence that speeds increased or decreased according to the type of 

pavement marking used. Taking a more direct approach, lane encroachments were used to evaluate the 

two pavement marking types. Lane encroachments are defined as vehicles that infringe upon the adjacent 

lane by crossing the center line striping of a multi-lane highway. 

The research team examined lane encroachments at the North Carolina test sites only, as they had 

temporary lane shift detours installed during bridge construction projects.

DATA COLLECTION

Lane encroachment data were obtained through manual video extraction for two test sites in North 

Carolina. Once temporary detours (i.e., lane shifts) had been fully constructed and delineated with work 

zone pavement markings, the team set up video cameras to record highway activities at entering and 

exiting curves into and out of lane shifts. Video was recorded to a DVR from a high elevation to give the 

observer a clear overhead view of the lanes and lane lines. The team focused primarily on capturing rainy 

nighttime conditions for at least 2-hour periods. When sufficient video was recorded, the team took the 

equipment back to the lab for investigation and data extraction.

From the video, traffic volumes and lane encroachments were collected either for a total count or at 5-

minute intervals. Traffic volumes were obtained simply by counting the number of vehicles passing a predesignated segment of highway, whereas lane encroachments required additional effort. During nighttime 

conditions, lane lines were difficult to make out in the darkness of the videos. Therefore, the observer 

physically traced pavement striping from daytime footage and used these traces as lane line references for 

the nighttime footage. Lane encroachments were counted when a vehicle’s tire crossed over a solid 

pavement stripe. Additionally, only the lead vehicle of a platoon was observed so as not to include 

vehicles simply following the lead vehicle’s taillights.

STATISTICAL ANALYSIS OF LANE ENCROACHMENTS

I-85 

This site was a single-lane crossover with barriers and RPMs; therefore, lane encroachment analysis was 

not conducted due to the quality of video and fact that this particular site was only a single lane (and thus 

had no second lane to encroach upon).

US-15/501 

Multiple videos were recorded at a work zone crossover in 2009. Videos of nighttime, rainy conditions 

were shot on May 28 and July 12 between 11:00PM and 1:00AM and 9:00PM and 12:15AM, 

respectively. Additionally, the rainfall intensity was approximately 0.5 inches (12.7 mm) per hour for 

both recordings. Total vehicle counts and the number of vehicles that encroached lane lines were tallied. 

Although other video of nighttime rainy events were available, the lack of rain intensity and duration 

limited their use.

During the May video recording, standard pavement markings were installed. There were a total of 824 

vehicles, of which 36 encroached. By the time of the July recording, the AWP markings had already been 
44

laid over the standard pavement markings. The AWP data had a total of 1,493 vehicles recorded, of which 

30 encroached. Table 11 displays the summary table of lane encroachments.

Table 11. Chi-square results for US-15/501 lane encroachments.

The percentages of vehicle encroachments were 4.6 percent and 2.1 percent for standard pavement 

marking and AWP markings, respectively. A Chi-square test indicated that the difference in lane 

encroachments for the AWP and standard pavement marking was statistically significant at the 99 percent

confidence level. Therefore, the AWP appears to have reduced the percentage of lane encroachments at 

this crossover. 

US-421 

As noted earlier, at the US-421 test site there were lane shifts in both directions of the divided highway, 

both having very similar geometry. The westbound direction was treated with standard pavement marking

while the eastbound direction was treated with the AWP. For each direction of travel, there were two 

crossovers (entry and exit). Each crossover contained entering and exiting curves. Therefore, at the US421 site, there were a total of eight curves in which lane encroachments could occur—two per crossover,

or four per direction. Lane encroachments were only taken for the lead vehicle of a platoon.

The team obtained 3.5 hours of video during rainy nighttime conditions concurrently at all of the curves. 

The actual vehicle counts were 2,582 for standard pavement marking (westbound direction) and 1,411 for 

the AWP (eastbound direction). There were 270 and 166 lead vehicle lane encroachments, respectively. 

Foreseeing the variability of traffic conditions, the observers recorded vehicle counts and lead vehicle 

encroachments at 5-minute intervals. Thus, 42 5-minute increments were obtained for nighttime, rainy 

conditions at each of the 4 crossovers.

Using the 5-minute samples of traffic volumes and lead vehicle encroachments, lane encroachment rates 

were calculated. The mean and standard deviations were computed from the samples of lane 

encroachment rates. Samples were compared, and their mean differences determined. Finally, t-tests were 

used to analyze the significance of various sample comparisons. The results of the t-tests were based on a 

95 percent confidence interval.

When analyzing the lane encroachment rates, comparisons were made between standard pavement 

marking and the AWP. The comparisons were as follows:

• Standard pavement marking entry versus the AWP at an entry crossover.

• Standard pavement marking exit versus the AWP at an exit crossover.

• AWP at an entry versus an exit crossover.

• Standard pavement marking at an entry versus an exit crossover.

• Combined lane encroachment rates of standard pavement marking entry and exit versus rates of 

the AWP at entry and exit crossovers.

Table 12 summarizes the findings of the statistical tests.
45

Table 12. US-421 lane encroachment statistics.

Statistical Analysis of Lane Encroachment Rates

Sample Comparison Mean Standard 

Deviation

Mean 

Difference

Two-tail pvalue

Standard Entry 4.1% 3.2% +1.2% 0.1935

AWP Entry 5.3% 4.9%

Standard Exit 6.4% 3.5% +0.1% 0.8819 AWP Exit 6.5% 4.8%

Standard Entry 4.1% 3.2% +2.3% 0.0028*

Standard Exit 6.4% 3.5%

AWP Entry 5.3% 4.9% +1.2% 0.2591 AWP Exit 6.5% 4.8%

Standard Entry + Exit 10.5% 5.4% +1.3% 0.3571

AWP Entry + Exit 11.8% 7.5%

* p-value is statistically significant (α = 0.05)

Table 12 shows that crossovers marked with standard pavement marking had slightly lower encroachment 

rates than similar crossovers marked with the AWP. However, the differences were small, and based on 

the results from the t-tests no lane encroachment rate comparisons, except between standard pavement 

marking entry versus standard pavement marking exit, had statistically different rates.

CONCLUSION AND RECOMMENDATIONS

In North Carolina, the research team used lane encroachments as the primary factor to evaluate the safety 

measure of installing the brighter AWP. It was hypothesized that bright lane markings would enhance the 

driver’s ability to clearly distinguish lanes on the roadway and make safer driving maneuvers. The results 

of the lane encroachment analysis provided inconclusive findings.

First, when studying the lane encroachments at the US-15/501 site, the results from the experimental test 

show that the installation of the AWP reduced the rate of lane encroachments by 50 percent. That is to 

say, the percentage of vehicles that encroached out of the total number of vehicles was half that of the 

percentage calculated for standard pavement markings. Upon first glance, this is impressive, yet it should 

be interpreted with caution. A variety of confounding issues may exist, especially regarding the variable 

nature of rain intensity. However, studying the rate of lane encroachments at the US-421 site, the team 

sought to compare differences between entry and exit curves installed with the AWP and standard 

pavement markings. T-tests showed that the small difference in lane encroachment rates were statistically 

insignificant. 

Another interesting finding is the lane encroachment rates between entry and exit curves using the same 

pavement markings, which indicate that drivers are likely making more lane encroachments at the exit 

curves. Recalling findings from the speed analysis, there were similar statistically significant differences 

between entry and exit curves. Mean speeds were higher in exiting curves than in entry curves. The type 

of crossover appears to be more important to traffic operations than other factors studied.
46
47

LATERAL LANE PLACEMENT

Lane placement describes the distance a driver deviates laterally within the travel lane. Driver behavior 

and vehicle placement of vehicles travelling through the work zone was recorded using two methods of 

data collection: a video camera mounted in a survey vehicle and distance measuring sensors. The 

methodology for each data collection method is described in detail in the following sections.

DATA COLLECTION

Video Calculated Distances for US-32/33/50

To collect data on the lane placement of vehicles through the work zone for the US-32/33/50 site, a digital 

video camera was mounted inside a survey vehicle and data were recorded through the advance warning 

area and through the shift area of the work zone while following a target vehicle in the traffic stream. Due 

to the remote location of the site and the lack of ambient lighting, video data collection from an elevated 

location was not feasible because the video could not delineate the pavement markings. Therefore, 

motorists were followed through the work zone and monitored through the use of a video camera on-site.

The motorists were not aware that they were being monitored and, thus, their driving behavior was 

unbiased.

The video data were then analyzed in the laboratory to obtain quantifiable lateral lane placement data for 

each observed vehicle. When analyzing the video data, the lateral placement was determined by locating 

the center of the lane and comparing that location to the location of the center of the vehicle’s license 

plate in a 6-inch positional alignment. When a vehicle was positioned left of the centerline, or away from 

the work zone, that was recorded as a positive distance, whereas a vehicle traveling right of the centerline, 

or closer to the work zone, was recorded as a negative distance. The data were extracted from the video 

for every second of travel through the shift and immediately before and after the shift. 

Distance Measuring Sensors for I-90

To obtain lateral placement distances of vehicles traveling through the I-90 crossovers, sensors were 

utilized to collect data from the shoulder of the roadway. These sensors were the same used for speed 

collection. 

For the I-90 test site, the lane placement and speed data were collected for the double-lane crossover 

sections of the work zones for each pavement marking type. For the crossover section with the AWP 

treatment, four sensors were placed along both of the travel lanes in the middle of the crossover. The 

sensors were placed along the left travel lane at 100-foot (30.5-m) spacing, with the first sensor placed at 

approximately the point of reverse curvature in the double-lane crossover. The last sensor on the right 

travel lane was placed just prior to the jersey barriers dividing the eastbound and westbound lanes. The 

remaining sensors were then placed at 100-foot (30.5-m) spacing back from the last sensor. For the 

standard pavement marking treatment, data could not be feasibly collected for the left travel lane, as there 

was no shoulder available alongside the lane. As a result, all eight sensors were placed along the right 

travel lane at 100-foot (30.5-m) spacing, with the first sensor located approximately 200 feet (61 m) into 

the double-lane crossover. Due to the potential in comparing lane placement and speed data for left and 

right lanes, a statistical analysis was conducted to determine differences, if any, between the two lanes for 

one direction of travel. The results from the ANOVA indicated that there was no statistical difference 

between lane placements or speeds between the left and the right lane. Therefore, comparisons can be 

made between the pavement marking sites.
48

For each of the work zones studied, the sensors were oriented perpendicular to the roadway and placed on 

the graded shoulder. The distance from the sensor to the edge line of the closest travel lane was measured 

and recorded to determine the lane placement of the vehicle. The sensors were placed within 6 feet (1.8 

m) of the edge line to remain within the operating range of the sensors. The primary focus was to be able 

to detect a vehicle within the travel lane adjacent to the shoulder where the sensors were located. Once all 

of the sensors were in place, the synchronized sampling was conducted using the MicroStrain Node 

Commander® software installed on a laptop computer. The synchronized sampling was conducted for the 

eight sensors at a rate of 128 Hz. The data were collected for approximately 1.5 to 2 hours, depending on 

the life of the laptop’s battery. 

ANALYSIS METHODOLOGY

Statistical analyses were conducted for the lateral lane placement data collected at the two Ohio sites.

Averages and standard deviations were calculated from the filtered lateral placement data. The data were 

compared using the one-way analysis of variance testing the significance at a 95 percent level of 

confidence.

RESULTS

The primary difference between the two test sites was that the distances of lane placements were relative 

to different reference points, and therefore incomparable. At US-32/33/50, the distances were taken for 

lane placement relative to the center of the travel lane, whereas at I-90 distances were measured from the 

shoulder perpendicular to the travel way. The mean speeds, standard deviations, and mean differences 

were first calculated, followed by a statistical analysis using either t-tests or ANOVA for sample 

comparison. Table 13 displays a summary table of the lane placement data collected and analyzed for the 

test sites, followed by a section summarizing the findings from each site studied.

US-32/33/50

The averages and standard deviations were calculated for the samples collected for each of the lane shifts 

at the US-32/33/50 test site. Based on results from video data extraction, the mean lateral lane placements 

ranged between 0.043 to 0.771 feet (1.3 to 23.5 cm) from the center of the lane. The positive nature of 

each of the means indicates that motorists were traveling to the right of the center of the lane, or veering 

away from the work zone.

Statistical tests were used to determine if the mean lateral lane placement for the test site as compared to 

the control site was statistically significant. The one-way analysis of variance was utilized to compare the 

mean speeds. Due to heterogeneous variances, the Welch’s modification to the one-way analysis of 

variance was utilized, and the calculated F-value was based on an asymptotic distribution. In addition, due 

to the unequal variances and unequal sample sizes, the Games Howell post hoc test was utilized to 

examine specific differences within those samples tested in the one-way analysis of variance. Based on 

the one-way analysis of variance, it was determined that the mean lateral lane placements associated with 

the vehicles entering, within, and exiting the lane shift for the daytime rain, nighttime dry, and nighttime 

wet conditions were significantly different, as shown in table 14.
49

Table 13. Summary of statistics for lateral lane placement.

* p-value is statistically significant (α = 0.05)

1 foot = 30.5 cm

Table 14. US-32/33/50 lane placement ANOVA results.

Comparison Sum of Squares Degrees of 

Freedom

Mean 

Squares

F-Calc p-value

Daytime Rain Conditions

Between Groups

Within Groups

Total

25.02

88.49

113.51

5

240.03

5.00

0.159 29.148 < 0.001*

Nighttime Dry Conditions

Between Groups

Within Groups

Total

15.12

109.61

124.73

5

257.84

3.025

0.184 20.854 < 0.001*

Nighttime Rain Conditions

Between Groups

Within Groups

Total

12.67

97.00

109.68

5

251.25

2.535

0.174 14.259 < 0.001*

* p-value is statistically significant (α = 0.05)

For US-32/33/50, the ANOVA results provided in tables 13 and 14 indicate that for all conditions:

• For daytime and nighttime rain conditions, the mean lateral lane placements within and exiting 

the lane shifts were statistically different between the standard pavement marking and the AWP, 

while means for entering the lane shift were statistically similar. While traveling through the 

Site Light Weather Paint Location Average Lane 

Placement (Feet)

Standard 

Deviation

Mean 

Difference

P(T<=t) 

two-tail

Standard Entry Crossover 0.61 0.46

AWP Entry Crossover 0.75 0.43

Standard Within Work Zone 0.28 0.31

AWP Within Work Zone 0.43 0.40

Standard Exit Crossover 0.55 0.35

AWP Exit Crossover 0.04 0.46

Standard Entry Crossover 0.57 0.46

AWP Entry Crossover 0.66 0.39

Standard Within Work Zone 0.29 0.39

AWP Within Work Zone 0.50 0.39

Standard Exit Crossover 0.57 0.41

AWP Exit Crossover 0.24 0.47

Standard Entry Crossover 0.52 0.54

AWP Entry Crossover 0.77 0.45

Standard Within Work Zone 0.30 0.31

AWP Within Work Zone 0.53 0.42

Standard Exit Crossover 0.67 0.40

AWP Exit Crossover 0.39 0.40

Standard Exit Crossover 3.25 4.42

AWP Entry Crossover 5.82 3.75

Standard Exit Crossover 9.28 2.13

AWP Entry Crossover 5.48 3.03

Standard Exit Crossover 6.57 3.09

AWP Entry Crossover 6.50 3.01 Night Rain -0.07 0.994

Rain

Clear

Clear

Clear -3.80 < 0.001*

2.57 < 0.001*

0.765

Night 

Night 

US 32/33/50 0.09

0.15 0.214

Rain 0.15 0.017*

I-90

Day

Night 

-0.50 < 0.001*

0.001*

< .001*

0.003*

< 0.001*

-0.28 0.001*

0.23

0.25

-0.33

0.21

Day
50

work zone, vehicles tended to maintain a position closer to the white markings when utilizing the 

AWP. When exiting the work zone, vehicles maintained a position closer to the center of the lane 

delineated with the AWP.

• For the nighttime dry conditions, the mean lateral lane placements for all locations along the lane 

shifts were statistically different between the standard pavement marking and the AWP sections. 

When entering the lane shift and through the lane shift, those vehicles traveling through the AWP 

work zone maintained a position closer to the white pavement markings as compared to the 

standard pavement marking work zone. When exiting the work zone, vehicles traveling through 

the AWP work zone were able to maintain a position closer to the center of the lane than those 

traveling through the standard pavement marking work zone.

The US-32/33/50 results are visually represented in figure 26.

1 foot = 30.5 cm

Figure 26. Graphs. US-32/33/50 lane placement results.

I-90

The averages and standard deviations were calculated for the samples collected for each of the two-lane 

crossovers at the I-90 test site. The sensor was placed 3 feet (.91 m) away from the outside edge of the 

lane line. Based on results from sensor data extraction, the mean lateral lane placements (i.e., the distance 

between the sensor and the vehicle’s closest tire) ranged between 3.25 and 9.28 feet (1 and 2.8 m). Given 

a lane width of 12 feet (3.7 m) and an assumed vehicle width of 7 feet (2.1 m), a vehicle positioned 

directly in the center of the lane would be 2.5 feet (.76 m) inside the edge lane. Therefore, a vehicle 

position (the edge of the vehicle) that was approximately 5.5 feet (3 + 2.5 feet), or 1.7 meters, from the 

sensor is assumed to be located within the center of the closest lane in the two-lane crossover. A vehicle 

detected 9.28 feet (2.8 m) from the sensor is assumed to be encroaching a total of 1.28 feet (0.39 m) in the 

second lane as follows: the near tire is 6.28 feet (1.91 m) from the near edge line (9.28 feet – 3 feet set 

back for the sensor), with the far tire positioned 13.28 feet (4.05 m) from the near edge line (6.28 feet + 7-

foot width). Based on a 12-foot (3.7-m) lane width, the far tire is located 1.28 feet (0.39 m) inside the 

adjacent lane.

Table 15 shows a summary of lane placements determined for I-90. Based on the one-way analysis of 

variance, it was determined that the mean lateral lane placements associated with the vehicles traveling 

through the two-lane crossover for the daytime dry, nighttime dry, and nighttime wet conditions were 

significantly different, as shown in table 15.
51

Table 15. I-90 lane placement ANOVA results.

Comparison Sum of Squares Degrees of 

Freedom

Mean 

Squares

F-Calc p-value

All Conditions

Between Groups

Within Groups

Total

22505.34

176294.20

198799.54

6

3855.34

3750.89

12.56 341.09 < 0.001*

* p-value is statistically significant (α = 0.05)

For I-90, the ANOVA results indicate the following:

• During daytime dry conditions, the mean lateral lane placements of vehicles on the standard 

pavement marking and the AWP sections were statistically different. The vehicles traveling 

through the AWP crossover generally maintained a position in the center of the travel lane more 

consistently than those traveling along the standard pavement marking crossover. The edges of 

vehicles traveling along the standard pavement marking crossover were generally located just 1

foot (.3 m) from the white lane line.

• For the nighttime dry conditions, the post hoc analysis also indicated that the vehicles traveling 

through the AWP crossover were able to maintain a position in the center of the lane more 

consistently than those traveling along the standard pavement marking crossover. The edges of 

vehicles traveling along the standard pavement marking crossover were generally located closer 

to the lane line dividing the two lanes, thereby creating a sideswipe crash potential.

• The post hoc conditions indicated that, during the nighttime rain conditions, the lateral lane 

placement of vehicles were statistically similar. 

The I-90 results in table 15 are visually represented in figure 27.

 1 foot = .3 m

Figure 27. Graph. I-90 lane placement results.

Note: The dashed line represents the distance from the sensor to the edge of the outer lane line. The lane placement 

measurements are the linear distance between the sensor and the vehicles closest tire.

CONCLUSIONS

The lateral lane placement of vehicles was quantified to assess the ability of the pavement markings in 

guiding motorists through the work zone. Vehicle placement within a work zone was considered a 
52

primary measure for safety due to the ability to discern a vehicle’s crash risk. The crash risk was 

identified by the location of a vehicle close to a lane line, indicating the potential for a sideswipe crash or 

intrusion into the work zone, increasing the potential for a fixed object crash or a pedestrian crash.

Along the US-32/33/50 site, motorists maintained their position either in the center of the lane or close to 

the white edge marking. Statistical differences were noted for the daytime and nighttime rain conditions 

within and exiting the lane shift and for all the locations for the nighttime dry condition. Generally, when 

exiting the lane shift, those vehicles traveling through the AWP site were able to maintain a position 

closer to the center of the lane than those traveling through the standard pavement marking site.

For the I-90 site, motorists also maintained their position in the center of the lane when traveling through 

the AWP site for the daytime dry and nighttime dry conditions. During the nighttime rain conditions, 

motorists maintained their position nearly in the center of the lane for both the AWP and the standard 

pavement marking sites. While traveling through the standard pavement marking site during the nighttime 

dry conditions, the mean lateral lane placement position of the vehicles indicated a higher potential for 

sideswipe crashes.

It was preferable for motorists to be able to locate their vehicle in the center of the lane to minimize the 

crash potential. However, based on the higher levels of retroreflectivity associated with the white 

pavement markings, it was not surprising that drivers would utilize the white edge line to appropriately 

position their vehicles. At the I-90 site, the examined lane for lateral placement utilized white pavement 

markings along both sides of the lane, which enabled drivers to maintain their lane appropriately more 

frequently.
53

OVERALL CONCLUSIONS 

This research effort sought to quantify the effects of a new pavement marking product developed by 3M 

for work zone deployment under nighttime rainy conditions. The AWP was previously tested under rain 

simulated conditions, and findings indicated that the marking was much more visible than its 

conventional counterpart, primarily due to supplemental optical elements that retroreflect light in rain 

conditions. This research study builds on these findings by employing studies at actual work zones. Four 

MOEs were utilized in studying five work zones in North Carolina and Ohio. These MOEs included 

retroreflectivity, speed, lane encroachment, and lateral lane placement.

Retroreflectivity measurements were taken to determine 1) differences in AWP applications across sites 

and 2) differences between AWP and standard pavement marking at each individual site. From the data, it 

can be concluded that AWP applications had statistically significant differences between the AWP and

standard pavement marking; however, there was not consistency among the pavement marking 

contractors in the application of either pavement marking type. This suggests that pavement marking 

contractors likely have different application methods with very little consistency in application rates and 

element drop rates. The research team suggests that future studies look at the retroreflectivity values over 

time to see the degradation rates of individual pavement markings under actual field conditions.

Speed was utilized to supplement findings regarding lateral lane placement. The research team used

speed as a supplemental finding because higher or lower speeds through work zones proved not to be a 

good indicator of improved safety. For instance, increased visibility may result in increased speed if there 

is more driver comfort. It is not known what affect this may have on driver safety. Nevertheless, analyses 

of speeds were instructive and interesting. Speeds should be interpreted with caution due to their 

variability and dependence on many factors, primarily the geometry of the crossovers being compared, 

rain intensity during different analysis periods, method of extraction, and overall light conditions. The 

research team took every possible measure to eliminate as much variability as possible when collecting 

data and making comparisons.

Speeds were analyzed at all five work zone sites. The overall findings indicated the following:

• Statistically significant differences in speeds at entry and exit portions of a single crossover. This 

indicates that drivers tend to decelerate into each crossover and speed up exiting the same 

crossover, regardless of the marking type used.

• Statistically significant differences in speeds at entry and exit curves (entering and exiting the 

entire work zone – two crossovers). The findings showed that drivers drive 3.8 to 5.9 miles per 

hour (6.1 to 9.5 kph) faster when exiting the work zone.

• Although inconclusive, it appears that speeds were more likely to be higher in similar entry or 

exit curves when AWP was used than when standard pavement marking was used. This was the 

case in three of four different sets of curves that were compared, and all were statistically 

significant except one. For all practical purposes, even this one sample was significant (p-value 

=0.057).

Lane encroachment and lateral lane placement were the primary MOEs used to determine if safety had 

improved using the new AWP, as these measures are easily correlated with driver’s lane-keeping ability. 

It was hypothesized that brighter lane markings would enhance the driver’s ability to distinguish lanes on 
54

the roadway and make safer driving maneuvers. A total of four sites were studied for lane-keeping, two 

each for lane encroachments and lateral lane placement.

Lane encroachments were defined as vehicles crossing the lane line in a crossover. Comparing both 

pavement marking types in similar curves provided contrasting findings. The first site studied indicated 

that a higher number of lane encroachments occurred at standard pavement marking curves; however, that 

study introduced several factors that could not be accounted for directly (such as rain intensity). The 

second study, which was more robust, found statistically insignificant findings that standard markings 

resulted in more lane encroachments than AWP.

An interesting finding did emerge at these two sites when comparing entry and exit curves for both 

marking types—the exit lane was found to have more lane encroachments. This correlates to earlier 

findings that speeds were higher at exit curves (3.5 miles per hour [5.6 kph] or greater). This finding 

indicates that more attention should likely be given to the exit curve of a work zone since higher lane 

encroachments and speeds likely lead to higher potential for a serious collision.

Lateral lane placement describes a vehicle’s proximity to a lane line, indicating the potential for a 

conflict or crash. Two sites were studied, one comparing an entry, within, and exit portions of two parallel 

work zones and the other comparing the entry and exit crossover for one direction of travel. At the first 

site, statistically significant differences were noted for daytime and nighttime rainy conditions within and 

exiting the lane shift; drivers maintained their lateral placement better within a work zone marked with 

standard markings but maintained their lateral placement better when exiting a work zone marked with 

AWP. During nighttime clear conditions, drivers maintained their lateral placement better throughout 

work zones marked with standard markings, while a statistically significant improvement in lateral lane 

placement was found with AWP when drivers were exiting the work zone.

At the second site, results also varied. During daytime clear conditions, drivers in the standard pavement 

marking sections maintained their lateral placement better. In contrast, during nighttime clear conditions, 

they maintained their lateral placement better in the sections marked with AWP. More importantly, 

nighttime rain conditions showed no statistically significant difference between the pavement markings.

In short, findings for lateral lane placement were similar to those regarding lane encroachments. Findings 

varied among sites and locations within the work zone; therefore, the findings were inconclusive.
55

REFERENCES

1. Pisano, P. A., Goodwin, L. C., and Rossetti, M. A. (2008). “U.S. Highway Crashes in Adverse Road 

Weather Conditions.” 24th Conference on International Interactive Information and Processing 

Systems for Meteorology, Oceanography, and Hydrology. New Orleans, LA, January 21-24.

2. Astronomical Applications Department of the U.S. Naval Observatory. (2008, February 27). Sun or 

Moon Rise/Set Table for One Year. Retrieved May 5, 2009, from U.S. Naval Observatory: 

http://aa.usno.navy.mil/data/docs/RS_OneYear.php.

3. Qiu, L., and Nixon, W. A. (2008). “Effects of Adverse Weather on Traffic Crashes: Systematic 

Review and Meta-Analysis.” Transportation Research Record No. 2055. Transportation Research 

Board of the National Academies, Washington, D.C., pp. 139-146.

4. Hummer, J. E., Rasdorf, W., Findley, D. J., Zegeer, C. V., and Sundstrom, C. A., (2010). “Curve 

Collisions: Road and Collision Characteristics and Countermeasures,” Journal of Transportation 

Safety and Security. Volume 2, Number 3, pp. 203-220. 

5. Roadway worksafetyzone.org. (2010, September 16). Work Zone Fatalities. Retrieved September 8, 

2011, from The National Work Zone Safety Information Clearinghouse: 

http://www.workzonesafety.org/crash_data/workzone_fatalities.

6. Federal Highway Administration. (2008, December 9). Ninth Annual National Work Zones 

Awareness Week 2008: Work Zones Safety and Mobility Fact Sheet. Retrieved May 4, 2009, from 

U.S. Department of Transportation: http://safety.fhwa.dot.gov/wz/wz_awareness/2008/factsht08.cfm.

7. Federal Highway Administration. (2009, May 1). Facts and Statistics – FHWA Work Zone. Retrieved 

May 4, 2009, from U.S. Department of Transportation: 

http://ops.fhwa.dot.gov/wz/resources/facts_stats.htm.

8. Federal Highway Administration. (2008, December 9). Ninth Annual National Work Zones 

Awareness Week 2008: Work Zones Safety and Mobility Fact Sheet. Retrieved November 30, 2009, 

from U.S. Department of Transportation: 

http://safety.fhwa.dot.gov/wz/wz_awareness/2008/factsht08.cfm.

9. Huebschman, C. R., Garcia, C., Bullock, D. M., and Abraham, D. M. (2003). Construction Work 

Zone Safety. Publication FHWA/IN/JTRP-2002/34. Joint Transportation Research Program, Indiana 

Department of Transportation and Purdue University, West Lafayette, IN. 

10. Ullman, G.L., Finley, M. D., Bryden, J. E., Raghavan, S., and Council, F. M. (2008). NCHRP Report 

627: Traffic Safety Evaluation of Nighttime and Daytime Work Zones. Transportation Research Board 

of the National Academies, Washington, D.C.

11. Bahar, G., Masliah, M., Erwin, T., Tan, E., and Hauer, E. (2006). NCHRP Web-Only Document 92: 

Pavement Marking Materials and Markers: Real-World Relationship Between Retroreflectivity and 

Safety Over Time. Transportation Research Board of the National Academies.

http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_webdoc_92.pdf.

12. 3M. (2007). All Weather Paint: Product Bulletin. Traffic Safety Systems Division. 

13. 3M. (2011). 3M All Weather Paint – Reflective Traffic Paint System for Wet and Dry Roads.

Retrieved December 6, 2011, from 3M:

http://solutions.3m.com/wps/portal/3M/en_US/Traffic_Safety/TSS/Offerings/Products/Pavement_Ma

rkings/?PC_7_RJH9U5230GE3E02LECFTDQOVJ4000000_nid=31B4G7GPWLbeMRKM0WZXC

Wgl.

14. Higgins, L., Miles, J., and Carlson, P. (2009). “Nighttime Visibility of Prototype Work Zone 

Markings under Dry, Wet-Recovery, and Rain Conditions.” Paper presented at 88th Annual Meeting 

of the Transportation Research Board, January 11-15, Washington, D.C.

15. Rumar, K., and Marsh II, D. K. (1998). “Lane Markings in Night Driving: A Review of Past Research 

and of the Present Situation.” Report No. UMTRI-98-50. The University of Michigan Transportation 

Research Institute.
56

16. Jacobs, G., Hedblom, T., Bradshaw, T., Hodson, N., and Austin, R. (1995). “Detectability of 

Pavement Markings under Stationary and Dynamic Conditions as a Function of Retroreflective 

Brightness. Transportation Research Record No. 1495. Transportation Research Board of the 

National Academies, Washington, D.C., pp. 68-76.

17. Carlson, P. J., Fontaine, M. D., and Hawkins, Jr., H. G. (2000). “Evaluation of Traffic Control 

Devices for Rural High-Speed Maintenance Work Zones.” Report No. FHWA/TX-00/1879-1. Texas 

Department of Transportation. Federal Highway Administration.

18. Federal Highway Administration. (2008). Final Report, Phase 1: Temporary Wet Weather Pavement 

Markings for Work Zones. Highways for LIFE Technology Partnerships Program. Available on the 

FHWA website at: http://www.fhwa.dot.gov/hfl/partnerships/3m/phase1/index.cfm.

19. Schieber, F. (2004). “Highway Research to Enhance Safety and Mobility of Older Road Users.” 

Transportation in an Aging Society: A Decade of Experience. Transportation Research Board 

Conference Proceedings 27. Technical Papers and Reports from a Conference, Bethesda, MD, 

November 7-9. Washington, D.C.

20. Lee, J., Maleck, T., and Taylor, W. (1999). “Pavement Marking Material Evaluation Study in 

Michigan.” ITE Journal. Volume 69, Number 7, pp. 44-51.

21. Abboud, N., and Bowman, B. L. (2002). “Establishing a Crash-Based Retroreflectivity Threshold.”

Washington, D.C., 81st Annual Meeting of the Transportation Research Board, January 13-17, 

Washington, D.C.

22. National Oceanic and Atmospheric Administration (NOAA) Satellite and Information Service.

Climatological Data Publications. Retrieved October 7, 2011, from National Environmental Satellite, 

Data, and Information Service (NESDIS). National Climatic Data Center: 

http://www7.ncdc.noaa.gov/IPS/cd/cd.html.

23. Traffic Survey Report – 2009 Athens County. Traffic Monitoring, Ohio Department of Transportation, 

Columbus, OH. 

24. Traffic Survey Report – 2009 Ashtabula County. Traffic Monitoring, Ohio Department of 

Transportation, Columbus, OH.