Acceptance Testing of 

High-Speed Transverse 

Profile Measuring Systems 

Purchased for the Long-Term 

Pavement Performance 

Program

FHWA Publication No.: FHWA-HRT-20-023

FHWA Contact: Larry Wiser (ORCID: 0000-0002-6916-

1369), HRDI-30, (202) 493-3079, larry.wiser@dot.gov

INTRODUCTION

The Federal Highway Administration purchased four Ames Engineering 

transverse profile measuring systems (ATPMSs) late in 2017. This 

technical brief (TechBrief) describes the procedures used to perform 

acceptance testing of ATPMSs. The test sections used to collect data, 

collection of reference data, transverse profile data collection by the 

AHSVs, procedures for analysis of the transverse profile data, and 

results from the analysis are described in this TechBrief. 

An ATPMS has been installed on each of the four Ames Engineering 

high-speed survey vehicles (AHSVs) that currently collect longitudinal 

profile and macrotexture data for the Long-Term Pavement Performance 

program. An ATPMS consists of a single camera mounted on the rear 

of the vehicle at an approximate height of 84 inches from the ground’s 

surface. Figure 1 shows the AHSVs with an ATPMS installed on the 

back of each vehicle. Figure 2 shows a close-up view of an ATPMS.

The ATPMSs collect transverse profile elevation data over a distance of 

approximately 13 ft. A single transverse profile consists of 2,048 data 

points with 0.076-inch spacing between the data points. The transverse 

profiles are obtained at a longitudinal interval of 0.98 inches based 

on the signal obtained from the distance measuring instrument in the 

AHSV. An ATPMS is capable of collecting transverse profile data at 

this interval up to a speed of 70 mph. According to the manufacturer, 

the specified vertical resolution of the ATPMS is 0.0256 inch, while the 

vertical range is ±4 inches.(1) ATPMSs are not equipped with an inertialmeasurement system for measuring a vehicle’s roll, which is needed 

to obtain the cross slope of the pavement. After the transverse profile 

elevation data are collected, the data are normalized to the end points 

of the measurements by a computer program since the cross slope of 

the pavement cannot be determined from the collected data. 

Intensity images obtained by an ATPMS are used to detect the outer 

and inner lane edges from the striping on the pavement, and the range 

data from an ATPMS show the transverse profile. Figure 3 shows an 

example of an intensity image obtained from an ATPMS. The solid 

white stripe at the bottom is the stripe on the edge of the right lane 

Research, Development, and 

Technology 

Turner-Fairbank Highway Research 

Center 

6300 Georgetown Pike 

McLean, VA 22101-2296

https://highways.dot.gov/research

TECHBRIEF
2

demarcating the shoulder, and the intermittent lane 

marking at the top is the striping on the left side of the 

right lane.

EQUIPMENT CRITERIA SPECIFIED IN 

AASHTO R 88

Table 1 shows the requirements for the following items 

specified in AASHTO R 88, “Standard Practice for 

Collecting the Transverse Pavement Profile”: maximum 

distance between collected transverse profiles, minimum 

width of transverse profile, maximum interval between 

data points in a transverse profile, vertical-measurement 

resolution, and maximum deviation of transverse profile 

from a path perpendicular to the lane centerline.

(2) ATPMS values for these items are also shown in

table 1, which shows that ATPMSs satisfy the equipment

requirements specified in AASHTO R 88.(2)

TEST SECTIONS AND REFERENCE DATA 

COLLECTION

Six test sections, each 50 ft long, were established 

for evaluating the ATPMSs. The test sections were 

established on a portion of southbound I-35 near 

Ames, IA, that was abandoned because of a highway 

realignment. Reference transverse profile data were 

collected on each test section at a location 1 ft in front 

of the reflective tape placed at the start of each test 

section to initiate data collection with the AHSVs. The 

reference data were collected using a device that had 

a laser mounted on a beam, which was developed by 

the ATPMS manufacturer. Figure 4 is a photograph of 

this reference device, which consists of an aluminum 

beam that is placed on the pavement and holds a single 

point–laser height sensor and a distance encoder. The 

laser height sensor is pushed forward manually, and 

the encoder records the traveled distance. This device 

Figure 3. Photograph. Example intensity image.

Figure 2. Photograph. Close-up view of an ATPMS.

Source: FHWA.

Figure 1. Photograph. ATPMSs mounted on the backs 

of AHSVs.

Source: FHWA.

Source: FHWA.
3

records transverse profile data at 0.077-inch intervals. 

Three measurements were taken with the reference 

device at each test location. 

Transverse profile data collected within the lane starting 

4 inches from the inside edge of the lane stripe at the 

right edge of the lane and ending 4 inches before 

the stripe at the left edge that demarcated the travel 

lanes were used for analysis. The collected data were 

normalized to remove the cross slope. Figure 5 shows 

an example of data collected by the reference device 

at a test section. Data for three runs are shown in this 

plot. In the plot, a distance of 0 ft represents the left 

edge of the lane (i.e., 4 inches from the stripe). As this 

plot shows, the data collected by the reference device 

demonstrate good repeatability.

DATA COLLECTED BY AHSVS

Each AHSV performed six runs at each test section at 

a speed of 50 mph to collect transverse profile data. 

Transverse profile data, which were collected at the 

same locations as the reference data at each test 

section, were used to evaluate the repeatability and 

accuracy of the transverse profile data collected by the 

AHSVs. The following procedure was used to obtain the 

data at these locations:

1. Average transverse profiles were computed over 

a 1-ft distance centered at the location where 

reference data were collected. As transverse profile 

data were collected at 0.98-inch intervals, typically 

13 transverse profiles were averaged.

2. A 2-inch moving average was applied to the 

averaged transverse profile elevations.

3. The data at the right and left edges were trimmed 

to obtain elevation data between 4 inches from the 

inside edge of the lane stripe at the right edge of the 

lane and 4 inches before the edge of the lane stripe 

that demarcated the travel lanes. 

4. Slope in the transverse profile was removed by 

normalizing the data to the transverse profile 

end points. This step was done by adjusting each 

elevation value to a datum through the two end 

points, whose elevations were set to 0 inches. 

Figure 6 shows the transverse profile data for six runs 

collected by an AHSV on a test section at the location 

where reference measurements were taken. In the plot, 

a distance of 0 ft represents the left edge of the lane 

(i.e., 4 inches from the stripe).

CROSS-CORRELATION METHOD

The cross-correlation method can be used to rate the 

agreement between two profiles.(3) In AASHTO R 56, 

“Standard Practice for Certification of Inertial Profiling 

Systems,” the repeatability and accuracy of longitudinal 

profile data collected by an inertial profiler are assessed 

using the cross-correlation method that is applied to 

profiles filtered via the International Roughness Index 

algorithm.(4) ProVAL, a free software available on the 

Web, can be used to perform this analysis.(5)

The repeatability and accuracy of transverse profiles 

collected by the AHSVs were evaluated using the 

cross-correlation method. ProVAL was used to perform 

Table 1. Requirements in AASHTO R 88 and associated values for ATPMSs.

Parameter AASHTO R 88 Requirement(2) ATPMS Value

Maximum distance between collected transverse profiles 1.5 ft for project level and 10 ft for network level 0.98 inch

Minimum width of transverse profile 13 ft 13 ft

Maximum interval between data points in a transverse profile 0.4 inch 0.076 inch

Vertical measurement resolution ≤0.04 inch 0.0256 inch

Maximum deviation of transverse profile from path perpendicular 

to lane centerline 15 degrees 0 degrees

Figure 4. Photograph. Reference device that collected 

reference transverse profile data.

Source: FHWA.
4

this analysis. Files in ERD format that contained the 

transverse profile data were loaded into ProVAL for the 

analyses.(6) No additional filters were applied to the 

transverse profile data through ProVAL when performing 

the cross-correlation analysis. Therefore, all analyses 

were performed on the actual transverse profile data.

ProVAL computes the cross-correlation between 

two data sets by fixing one profile and horizontally 

shifting the other profile to obtain the maximum crosscorrelation for the two data sets. A cross-correlation 

value of 100 percent indicates the two profiles are 

identical, while a value of 0 percent indicates there is no 

relationship between the two profiles. 

A constant vertical offset between two profiles does 

not affect the computed cross-correlation value. 

Figure 7 shows transverse profile plots for two data 

sets. The second data set was obtained by adding a 

constant vertical offset of 0.05 inch to the first data 

set. The cross-correlation between these two data sets 

computed by ProVAL was 100 percent as the two data 

sets were identical except for a constant vertical offset 

between them.

Figure 8 shows transverse profile plots for two data sets, 

where the second data set was obtained by adding 

a varying vertical offset to the first profile. The vertical 

offset of the second profile is 0.04 inch from the first 

profile at the left edge, and the vertical offset decreases 

at a constant rate such that the offset is 0 inches at 

the right edge of the profile. The cross-correlation 

between these two data sets computed by ProVAL was 

95 percent. Although the two profiles had the same 

shape, the varying vertical offset between the two 

profiles caused the cross-correlation to be reduced by 

5 percent. ProVAL does not have the ability to rotate 

one profile while fixing the other profile to obtain the 

maximum cross-correlation between two profiles. In 

this example, if the second profile were rotated with 

respect to the first profile to obtain the maximum crossFigure 5. Graph. Transverse profile data collected by 

the reference device.

Source: FHWA.

Figure 6. Graph. Transverse profile measured for six 

repeat runs by an AHSV at a test location.

Source: FHWA.

Figure 7. Graph. Two identical profiles with 

one profile having a constant vertical offset 

from the other.

Source: FHWA.

Figure 8. Graph. Two identical profiles, where 

one profile has a varying vertical offset from other.

Source: FHWA.
5

correlation, the cross-correlation between the two 

profiles would have been 100 percent.

Therefore, when performing cross-correlation between 

two profiles using ProVAL, a profile that has a similar 

shape but is rotated with respect to the other profile, will 

decrease the cross-correlation between the two profiles.

REPEATABILITY OF TRANSVERSE PROFILE 

DATA COLLECTED BY AHSVS

The repeatability of transverse profile data collected by 

the AHSVs was evaluated through the cross-correlation 

technique by using ProVAL. The Profiler Certification 

module in ProVAL was used with no filters applied to 

the data being analyzed. ProVAL computes crosscorrelation values for all possible pairs of data, and 

then, the computed values are averaged to obtain an 

average cross-correlation value. As 6 repeat runs were 

performed on each test section by each AHSV, crosscorrelation values were computed for 15 pairs of data 

and averaged to obtain the average cross-correlation 

value for the AHSV at a test section. The average 

cross-correlation values for the transverse profile data 

collected by each AHSV at each test section are shown 

in table 2.

For AHSV1, at test section northbound (NB)-1, the 

highest cross-correlation between two runs occurred 

for run 2 and run 3, with a value of 95 percent. For 

the same device at this test section, the lowest crosscorrelation between two runs occurred for run 1 and 

run 4, with a value of 70 percent. The transverse profile 

plots for the two runs with the highest cross-correlation 

(i.e., 2 and 3) are shown in figure 9, while the transverse 

profile plots for the two runs with the lowest crosscorrelation (i.e., 1 and 4) are shown in figure 10. A 

visual review of figure 10 showed the cross-correlation 

between the profiles could be improved by rotating one 

profile with respect to the other; however, ProVAL does 

not have the ability to perform this function.

ACCURACY OF TRANSVERSE PROFILE DATA 

COLLECTED BY THE AHSVS

As described in the section Test Sections and Reference 

Data Collection, three data sets were collected with the 

reference device at each test section. Table 3 shows 

the cross-correlation values between the reference 

data sets at all test sections. As table 3 shows, the 

reference data collected at each test section were 

extremely repeatable. As the reference data showed 

good repeatability, the data collected during the first 

data-collection run were used as reference data in the 

analysis at all test sections.

The accuracy of transverse profile data collected by 

the AHSVs was also evaluated through the crosscorrelation technique. Using ProVAL, the reference data 

were compared to the data collected by the AHSVs. 

The Profiler Certification module in ProVAL was used to 

compute the accuracy cross-correlation with no filters 

applied to the data. The cross-correlation between the 

reference data and the data collected by each AHSV 

was computed at each test section. During computation, 

the data from the reference device were fixed, and the 

data from the AHSV were shifted horizontally to obtain 

the maximum cross-correlation for the two data sets. 

As six repeat runs were obtained by each AHSV at a 

test section, cross-correlation values were computed 

between the data from each run and the reference data, 

and the computed values were averaged to obtain the 

average accuracy cross-correlation for each AHSV 

at that test section. Table 4 shows the accuracy crosscorrelation values that were computed at test section 

NB-1 for the data collected by the four AHSVs for 

all runs.

The computed average accuracy cross-correlation 

values for the AHSVs at all test sections are shown in 

table 5. A visual review of the data in table 5 shows 

that none of the AHSVs is collecting data that are vastly 

different from the data collected by another AHSV. 

Table 2. Average repeatability cross-correlation values.

Test Section AHSV1 (%) AHSV2 (%) AHSV3 (%) AHSV4 (%)

NB-1 85 94 93 96

NB-2 95 97 92 92

NB-3 96 96 91 89

SB-1 92 97 94 94

SB-2 98 99 97 98

SB-3 92 97 91 90

Average 93 97 93 93

NB = northbound; SB = southbound.
6

The overall average accuracy cross-correlation value, 

computed by averaging the accuracy cross-correlation 

values computed at the six test sections, for the AHSVs 

ranged from 79 to 88 percent.

When examining the accuracy cross-correlation values 

for all AHSV runs at all test sections, the worst crosscorrelation value was noted for run 1 of AHSV2 at 

section NB-1, where the accuracy cross-correlation 

was 62 percent. Figure 11 shows AHSV2’s run 1 and 

the reference device’s transverse profile at NB-1 after 

ProVAL shifted the profile for AHSV2 horizontally to 

obtain the maximum cross-correlation, which was 

62 percent. A review of the two plots in figure 11 

shows the vertical offset between the two profiles is not 

constant; the offset decreases from left to right. A review 

of the plots indicated the cross-correlation between the 

two profiles can be improved by rotating the profile 

from AHSV2 downward from the left end so it will better 

match the profile of the reference device.

As described in the section Cross-Correlation Method, 

a constant vertical offset between two profiles does 

not affect the cross-correlation value, but a varying 

Figure 9. Graph. Two runs with the highest crosscorrelation for AHSV1 at test section NB-1 (run 2 and 

run 3, cross-correlation = 95 percent).

Source: FHWA.

Figure 10. Graph. Two runs with the lowest crosscorrelation for AHSV1 at test section NB-1 (run 1 and 

run 4, cross-correlation = 70 percent).

Source: FHWA.

Table 3. Cross-correlation values between runs for reference data collected at the test sections.

Compared Runs NB-1 (%) NB-2 (%) NB-3 (%) SB-1 (%) SB-2 (%) SB-3 (%)

1 and 2 99.3 98.8 99.8 99.4 100.0 99.4

1 and 3 99.5 98.7 99.6 100.0 99.2 99.4

2 and 3 98.8 99.7 99.8 99.4 99.2 100.0

Average 99.2 99.1 99.7 99.6 99.5 99.6

SB = southbound.

Figure 11. Graph. Transverse profiles from 

run 1 of AHSV2 and the reference device at section 

NB-1.

Source: FHWA.

Figure 12. Graph. Reference-device data and rotated 

profile of AHSV2’s run 1 at section NB-1.

Source: FHWA.
7

vertical offset between two profiles will. The profile 

from AHSV2 was rotated manually to match with the 

reference profile, and figure 12 shows a plot of the 

rotated profile as well as the profile from the reference 

device. The cross-correlation between the two profiles 

shown in figure 12 is 88 percent. Hence, by rotating 

the profile from AHSV2, it was possible to increase the 

accuracy cross-correlation of the two profiles from 62 to 

88 percent.

A review of plots that had low accuracy crosscorrelation values indicated the accuracy of crosscorrelation values for some profiles could be increased 

by rotating the profile collected by the AHSV with 

respect to the profile collected by the reference 

device. Therefore, some of the average accuracy 

cross-correlation values presented in table 5 could be 

improved by using a technique that will rotate the profile 

from the AHSV after horizontally offsetting the profiles to 

obtain the maximum accuracy cross-correlation value. 

However, developing a computer program that can 

do this type of analysis was beyond the scope of this 

project. Evaluation of transverse profile data collected 

by the reference device and AHSVs showed that, 

overall, the AHSVs were able to capture the transverse 

profile measured by the reference device.

COMPARISON OF RUT DEPTHS COMPUTED 

FROM REFERENCE DATA AND DATA 

COLLECTED BY AHSVS

The wireline method option in the Ames Engineering 

profiler system software was used to compute the rut 

depths from the data collected for the first pass of the 

reference device at each test section.(1) In the wireline 

method, an imaginary wire is fixed at the right edge of 

the lane and stretched across the lane, connecting the 

high points on the pavement surface ending at the left 

edge of the lane. The maximum distance between this 

wire and the pavement surface in each half of the lane 

is computed and treated as the rut depth in each lane 

half. Rut depths were also computed from the transverse 

profile data collected by the AHSVs at the same 

locations where reference data were collected for each 

run at each test section and then averaged to obtain an 

average rut depth for each test section. Table 6 shows 

Table 4. Accuracy cross-correlation values at test section NB-1 for the four AHSVs.

 Run AHSV1 (%) AHSV2 (%) AHSV3 (%) AHSV4 (%)

1 71 62 90 79

2 85 73 89 84

3 83 68 88 80

4 94 71 86 77

5 71 68 85 78

6 76 70 87 80

Average 80 69 87 80

Minimum 71 62 85 77

Maximum 94 73 90 84

Table 5. Average accuracy cross-correlation values for the AHSVs.

Test Section AHSV1 (%) AHSV2 (%) AHSV3 (%) AHSV4 (%)

NB-1 80 69 87 80

NB-2 82 90 68 71

NB-3 81 94 83 83

SB-1 92 94 75 88

SB-2 92 98 84 91

SB-3 83 82 77 91

Average 85 88 79 84

SB = southbound.
8

the computed average rut depths for the inside and 

outside lane halves for the test sections.

Since there were six test sections, four AHSVs, and 

two locations for rut depths (i.e., inside and outside 

lane halves), there are 48 cases in which reference 

and AHSV-computed rut depths can be compared. 

Figure 13 shows the cumulative frequency distribution 

of the absolute difference in rut depth computed from 

the reference-device data and corresponding data 

collected by an AHSV. The following results are seen in 

figure 13:

• Rut depths from the AHSVs were within 0.01 inch 

of the reference rut depths for 46 percent of cases.

• Rut depths from the AHSVs were within 0.01 

to 0.03 inch of the reference rut depths for 

27 percent of cases.

• Rut depths from the AHSVs were within 0.03 

to 0.05 inch of the reference rut depths for 

13 percent of cases.

• Rut depths from the AHSVs were within 0.05 

to 0.11 inch of the reference rut depths for 

14 percent of cases.

Table 6. Rut depths from the reference device and AHSVs at the location where reference data were obtained.

Test Section Reference 

(Inches)

AHSV1

(Inches)

AHSV2

(Inches)

AHSV3

(Inches)

AHSV4

(Inches)

NB-1, inside lane half 0.25 0.18 0.14 0.20 0.21

NB-2, inside lane half 0.18 0.10 0.07 0.09 0.13

NB-3, inside lane half 0.22 0.17 0.13 0.17 0.16

NB-1, outside lane half 0.09 0.10 0.09 0.14 0.11

NB-2, outside lane half 0.08 0.11 0.10 0.08 0.11

NB-3, outside lane half 0.09 0.12 0.10 0.08 0.08

SB-1, inside lane half 0.19 0.19 0.18 0.19 0.18

SB-2, inside lane half 0.21 0.21 0.20 0.23 0.20

SB-3, inside lane half 0.12 0.10 0.15 0.13 0.12

SB-1, outside lane half 0.12 0.09 0.09 0.13 0.11

SB-2, outside lane half 0.15 0.17 0.15 0.17 0.16

SB-3, outside lane half 0.08 0.07 0.09 0.07 0.10

SB = southbound.

Table 7. Average rut depths for the entire test section.

Section Lane Half AHSV1 Rut 

Depth (Inches)

AHSV2 Rut 

Depth (Inches)

AHSV3 Rut 

Depth (Inches)

AHSV4 Rut 

Depth (Inches)

Range For Four 

AHSVs (Inches)

NB-1 Inside 0.18 0.16 0.21 0.21 0.16–0.21

NB-2 Inside 0.12 0.09 0.14 0.14 0.09–0.14

NB-3 Inside 0.18 0.14 0.20 0.17 0.14–0.20

SB-1 Inside 0.23 0.22 0.24 0.20 0.20–0.24

SB-2 Inside 0.19 0.19 0.22 0.18 0.18–0.22

SB-3 Inside 0.11 0.18 0.13 0.13 0.11–0.18

NB-1 Outside 0.11 0.09 0.09 0.10 0.09–0.11

NB-2 Outside 0.12 0.10 0.07 0.11 0.07–0.12

NB-3 Outside 0.13 0.10 0.07 0.06 0.06–0.13

SB-1 Outside 0.12 0.10 0.13 0.12 0.10–0.13

SB-2 Outside 0.12 0.11 0.13 0.13 0.11–0.13

SB-3 Outside 0.10 0.12 0.10 0.11 0.10–0.12

SB = southbound.
9

RUT DEPTH FOR ENTIRE SECTION

The data collected by the AHSVs were used to compute 

rut depths at 1-ft intervals at all six test sections, which 

were each 50 ft long. The rut depths were computed 

using the wireline method in Ames Engineering profiler 

system software. Thereafter, an average rut depth was 

calculated at the test section for each lane half for 

each run by averaging the rut depths computed at 1-ft 

intervals. The average rut depth for the six runs were 

then averaged, and the computed values are shown 

in table 7. The rut depths computed from the data 

collected by all four AHSVs were within 0.05 inch of 

each other for 9 of the 12 cases shown in table 7. For 

the other three cases, the rut depths were within 0.06 to 

0.07 inch.

CONCLUSIONS

The transverse profiles collected by all four AHSVs 

showed good repeatability and, when compared to 

reference profiles, good accuracy.

It was demonstrated that the cross-correlation method 

could be used to evaluate the repeatability and the 

accuracy of data collected by transverse profile 

measuring systems. 

In this analysis, ProVAL, which is used to evaluate 

longitudinal profile data, was used to evaluate the 

repeatability and accuracy of transverse profile data. 

In the accuracy evaluation, the data collected by 

a reference device were used to evaluate the data 

collected by the AHSVs. The transverse profile data 

collected by the AHSVs were analyzed using the Profiler 

Certification module in ProVAL with no filters applied 

to the data. When comparing two transverse profiles 

in this module, one profile is fixed, and the other profile 

is shifted horizontally to obtain the maximum crosscorrelation between the two profiles. However, these 

maximum cross-correlation values could be improved 

by using a technique that rotates one transverse profile 

with respect to the other after horizontally offsetting the 

profiles. Therefore, the cross-correlation value computed 

by ProVAL will underestimate the actual crosscorrelation between two transverse profiles if a profile 

being evaluated has a rotational shift.

REFERENCES

1. Ames Engineering Profiler Software. (2018). User’s 

Manual, Version 6.1, Ames Engineering, Ames, IA.

2. American Association of State Highway and 

Transportation Officials. (2018). Standard R 

88, “Standard Practice for Collecting Transverse 

Profile.” Standard Specifications for Transportation 

Materials and Methods of Sampling and Testing 

and AASHTO Provisional Standards, AASHTO, 

Washington, DC.

3. Karamihas, S.M. (2004). “Development of CrossCorrelation for Objective Comparison of Profiles.” 

International Journal of Vehicle Design, 36(2/3), 

pp 173–193, Inderscience, Geneva, Switzerland. 

4. American Association of State Highway and 

Transportation Officials. (2018). Standard R 56, 

“Standard Practice for Certification of Inertial 

Profiling Systems.” Standard Specifications for 

Transportation Materials and Methods of Sampling 

and Testing and AASHTO Provisional Standards, 

AASHTO, Washington, DC.

Figure 13. Graph. Cumulative frequency distribution of absolute difference in rut depths from reference device and 

AHSVs.

Source: FHWA.
10

 JANUARY 2020 FHWA-HRT-20-023

HRDI-30/01-20(WEB)E

Researchers—This study was conducted by R.W. Perera (ORCID: 0000-0003-4708-2595, SME, Plymouth, 

MI) and G.E. Elkins (ORCID: 0000-0002-2509-3341, Wood Environment and Infrastructure, Reno, NV) under 

contract number DTFH61-15-D-00004.

Distribution—This TechBrief is being distributed according to a standard distribution. 

Availability—This TechBrief may be obtained online at https://highways.dot.gov/research. 

Key Words—Transverse profile, LTPP, long-term pavement performance, rut depth.

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

(USDOT) 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 TechBrief only because they are considered essential to the 

objective of the document.

Quality Assurance Statement—The Federal Highway Administration (FHWA) provides high-quality 

information to serve the Government, industry, and public in a manner that promotes public understanding. 

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

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

continuous quality improvement.

5. ProVAL Version 3.61. (2017). “View and Analyze 

Pavement Profiles.” (website) Austin, TX. Available 

online: http://www.roadprofile.com. Last accessed 

March 15, 2017.

6. Sayers, M.W. and Karamihas, S.M. (1997). ERD 

File format for Storage and Analysis of Road Profiles, 

Report No. UMTRI-97-51, University of Michigan 

Transportation Research Institute, Ann Arbor, MI.