TECHBRIEF

Research, Development, and 

Technology

Turner-Fairbank Highway 

Research Center

6300 Georgetown Pike

McLean, VA 22101-2296

www.fhwa.dot.gov/research

Validation of Pavement 

Performance Measures 

Using Long-Term Pavement

Performance Data

FHWA Publication No.: FHWA-HRT-17-105 

FHWA Contact: Larry Wiser, HRDI-30, (202) 393-3079, 

larry.wiser@dot.gov 

This document is a technical summary of the Federal 

Highway Administration report Validation of Pavement 

Performance Measures Using LTPP Data, Publication No. 

FHWA-HRT-17-089.

Background

The Moving Ahead for Progress in the 21st Century 

(MAP-21) Act requires performance measures to be 

established for the Interstate Highway System (IHS).(1)

Consequently, in January 2015, the Federal Highway 

Administration (FHWA) issued a Notice of Proposed Rulemaking 

(NPRM) to establish performance measures to assess the 

condition of the pavements on the National Highway System 

(NHS) and IHS.(2) And, in January 2017, FHWA issued the Final 

Rule to implement the performance management requirements 

of MAP-21 and the Fixing America’s Surface Transportation 

Act.(3) The performance measures established to assess 

pavement condition are the percentage of pavements on the IHS

in good condition, IHS in poor condition, NHS (excluding IHS) in 

good condition, and NHS (excluding IHS) in poor condition.(4)

The condition of the pavements is to be determined based 

on the following condition metrics contained in the Highway 

Performance Monitoring System (HPMS): the International 

Roughness Index (IRI), cracking percent, rutting, and faulting.(4)

An important first step toward implementation of the 

performance measures by highway agencies is to demonstrate 

the measures are valid and effective. Due to the wealth of data 

contained in its Pavement Performance Database, the LongTerm Pavement Performance (LTPP) program was poised to 

The Long-Term Pavement Performance 

(LTPP) Program is a large research 

project for the study of in-service 

pavements across North America. Its 

goal is to extend the life of highway 

pavements through various designs 

of new and rehabilitated pavement 

structures, using different materials and 

under different loads, environments, 

subgrade soil, and maintenance 

practices. LTPP was established under 

the Strategic Highway Research 

Program and is now managed by 

the Federal Highway Administration.
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assist in implementation of the performance 

measures. Accordingly, FHWA undertook a 

study to validate the pavement performance 

measures and to demonstrate their use within 

the pavement decisionmaking process.(5,6)

Accomplishing these objectives required 

translation of LTPP pavement condition data to 

condition metrics in accordance with the NPRM. 

For asphalt concrete (AC) percent cracking, the 

translation was done based on the NPRM and 

Final Rule. A database containing the translated 

data and other supporting information was 

created; it is available via LTPP InfoPaveTM 

(https://infopave.fhwa.dot.gov/). This database 

supported all analyses for the study in question, 

and highlights from those analyses are 

presented in this TechBrief.

Initial Validation

A summary aggregate statistical approach was 

used to get a grasp on general data trends to 

make sure the data were suitable for the planned 

analyses as well as for more indepth analyses. 

The initial analysis entailed a review of condition 

metrics over time. As part of the review, the 

numeric values from surveys within two 

consecutive construction events were plotted 

versus time, and a linear trend line was defined 

for each section. The trend was then classified as 

increasing (worsening), decreasing (improving), 

or no change, based on the slope. The results 

appeared logical, in that conditions generally 

worsen with time in the absence of maintenance 

and rehabilitation (M&R). An increasing slope 

was found for 70 percent or more of the cases. 

For those cases in which the slope did not 

increase, there was generally an explanation; for 

example, it is expected that rutting will increase 

with time, but the rate of change is generally less 

than the measurement error, and as a result, it 

is possible for pavements to show a decreasing 

slope. 

The next analysis looked at changes in overall 

condition over time, with condition established 

by grouping the individual condition metrics; 

each group required that surveys of the different 

condition metrics were taken within 1 year. 

Figure 1 presents the overall condition ratings 

for AC (NPRM) pavements, AC (Final Rule) 

pavements, jointed portland cement concrete 

(JPCC) pavements, and continuously reinforced 

concrete pavements (CRCPs). As shown, 

there are few groupings in poor condition 

(less than 3 percent), and, except for CRCP, 

the majority of groupings are in fair 

condition.

The overall condition trends over time were then 

reviewed to determine if they followed a logical 

(good to fair to poor) trend. The results showed 

that they performed as expected in 83 percent 

or more of the cases. A more indepth review of 

those cases not following the expected trend 

was conducted. For AC pavements, for example, 

the main cause was rutting values fluctuating 

between 0.15 and 0.2 inch (i.e., between good and 

fair), while for JPCC pavements, it was faulting 

values fluctuating between 0 and 0.05 inch 

(i.e., between good and fair). 

Next, a comparison was made of the condition 

metrics and overall condition time-series against 

documented M&R applications. Treatments were 

first grouped based on similarity; for example, 

skin patching and pothole patching were 

grouped under patching. Next, the trend of the 

individual metrics was classified as increasing, 

decreasing, or no change, and the change in 

the metric resulting from the M&R application 

was quantified in percentage and magnitude 

terms. For the IRI metric, for example, the results 

showed that treatments such as mill and overlay 

and overlay had the most significant effect (i.e., 

reduction) on IRI. Finally, the M&R effect on 

overall condition was investigated. Table 1, for 

example, shows how JPCC pavement overall 
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Figure 1. Overall condition rating. 

condition was affected by M&R, with the effect 

classified as improved, reduced, or no change. 

As shown, the overall condition is largely unaffected based on the large percentage of no change.

The results also showed that the largest “No 

Change” percentage is a result of a “before” 

condition of fair and, to a lesser degree, a 

“before” condition of good. Table 2 illustrates 

the M&R impact on performance measures 

after treatment. For AC pavements and CRCPs 

the percent good increases, while for JPCC 

pavements, the percent good slightly decreases 

(only grinding was shown to improve overall 

condition, and that was minimal in comparison 

to the amounts associated with other treatments).

The final analysis entailed the review of 

overall condition against thresholds. First, the 

composition of the overall condition rating to 

the metric conditions was compared; in other 

words, how was overall condition affected by 

individual condition metrics? As an example, 

table 3 shows the breakdown of metrics for 

AC pavements. As shown, the overall condition 

rating for fair is driven by rutting, with 75 percent 

having rutting in fair or poor condition. The poor 

rating is driven by cracking and rutting.

Next, a review was conducted to identify 

the number of metrics in a given condition 

that makes up the overall condition. It was 

determined that the overall condition rating of 

Treatment Type No Change (%) Improve (%) Reduce (%)

Crack seal 86 0 14

Grinding 72 17 11

Joint seal 88 5 7

Patch 90 0 10

Slab replacement 50 17 33

Table 1. JPCC overall pavement condition between construction events.

Source: FHWA.
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Pavement Before Good (%) Before Poor (%) After Good (%) After Poor (%)

AC (NPRM) 3.6 2.2 6.1 1.4

AC (Final Rule) 6.2 1.2 11.1 1.0

JPCC 4.2 2.5 4.0 3.0

CRCP 9.5 0 13.8 0.5

Condition 

Rating IRI IRI IRI Cracking Cracking Cracking Rutting Rutting Rutting

Condition 

Rating G F P G F P G F P

Good 100% 0% 0% 100% 0% 0% 100% 0% 0%

Fair 65% 32% 2% 56% 15% 29% 25% 68% 7%

Poor 19% 29% 52% 7% 1% 91% 8% 30% 62%

Table 2. Effect of treatments on performance measures.

Table 3. AC (Final Rule) percentage metrics.

fair was largely affected by one or no metric 

in fair condition; that is, two metrics are good 

and one metric is fair or poor, or one metric is 

each good, fair, and poor. Similarly, the overall 

condition rating of poor is largely a result of two, 

not all three, metrics being poor. These findings 

also support the overall condition rating not 

being affected as much by M&R, as it does not 

require many metrics to be classified as fair for 

the overall condition to be fair. 

Finally, the average time for the overall condition 

to change over time was investigated using LTPP 

AC and JPCC Seasonal Monitoring Program and 

selected LTPP CRCP test sections that had at least 

four survey groupings within a construction 

event. The results showed that overall condition 

is largely static as it remained constant for more 

than 60 percent of the test sections investigated. 

The average timespan over which the condition 

remained constant ranged by pavement type 

from 3.8 to 15.8 years. For those cases where 

the condition changed, the average time for 

the condition to change varied from 4.1 to 12.0 

years depending on pavement type. 

Additional Validations

In light of the findings from the initial 

validations, additional analyses were done 

to assess the performance measures. The 

first analysis considered alternate pavement 

condition threshold values to those proposed 

in the NPRM. These values correspond with 

those in the Final Rule. Both sets of threshold 

values are presented in table 4. The alternate 

values were first used to evaluate the condition 

for each metric. The condition metrics were 

then combined to establish overall condition, 

and the results were compared to those based 

on the NPRM values. Figure 2, for example, 

presents the JPCC overall condition comparison 

results. As shown, the alternate (Final Rule) 

G = good; F = fair; P = poor.
5

cracking threshold reduces percent poor for 

the overall condition by 0.1 percent (from 

2.4 to 2.3 percent). The largest effect on 

overall condition is a result of the alternate 

faulting threshold, which increases 

percent good by 7.4 percent (from 32.1 to 

39.5 percent). 

Next, a follow-up temporal analysis was 

conducted using LTPP test sections located 

on the IHS. Figure 3 depicts the timespans 

referenced in this analysis. A survey grouping 

was formed only if the individual metric 

measurements were taken within 1 year, 

which often created a time lag between the 

time of construction (designated by time 

equal to zero) and the time the first grouping 

was considered (designated as time “t”), 

on average 1.5 years for AC pavements and 

Condition Metric Performance Level NPRM Threshold Final Rule Threshold

IRI Good <95 <95

IRI Poor

>170: population < 

1,000,000

>220: population > 

1,000,000

>170

Percent cracking, AC Good <5% <5%

Percent cracking, AC Poor >10% >20%

Percent cracking, 

JPCC Good <5% <5%

Percent cracking, 

JPCC Poor >10% >15%

Faulting Good <0.05 <0.10

Faulting Poor >0.15 >0.15 

Table 4. NPRM and Final Rule threshold values.

Figure 2. Overall condition comparison for JPCC.

Source: FHWA.
6

longer for other pavements. The overall 

timespan considered was from the time of the 

first to the last survey grouping (or “15 – t” 

years). The time to change was calculated as 

the time between the first survey grouping and 

the first grouping at a different condition (i.e., 

the fifth grouping in figure 3), which does not 

consider the time from construction to the first 

survey.

Table 5 summarizes the temporal analysis 

results. There were, for example, 130 AC test 

sections that remained fair for an average span 

of 5.5 years, while there were 135 AC test 

sections that changed from good to fair over 

an average span of 4.8 years. The percentage 

of test sections that remained constant 

over the entire analysis period ranged from 

63 percent for AC to 86 percent for CRCP. Again, 

Figure 3. Temporal groupings.

Pavement AC (Final 

Rule)

AC (Final 

Rule) JPCC JPCC CRCP CRCP

Condition Number of 

Sections

Average 

Time 

(years)

Number of 

Sections

Average 

Time 

(years)

Number of 

Sections

Average 

Time 

(years)

Good–constant 115 6.3 19 7.9 18 11.3

Fair–constant 130 5.5 103 7.7 12 11.5

Poor–constant 1 4.7 3 7.3 0 No data

Good to fair 135 4.8 45 6.7 5 8.5

Fair to poor 9 4.4 15 6.3 0 No data

Total sections 

and span of 

time

390 6.3 185 9.0 35 11.7

Table 5. Average time to change for sections on the IHS. 

Source: FHWA.
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Overall 

Condition Metrics Rutting Roughness Cracking Number of Groupings

Fair G-G-P 29% 4% 67% 652

Fair G-G-F 75% 14% 11% 2,413

Table 6. AC fair driver, single metric.

the implication is that condition ratings are 

stable; they do not change rapidly.

The final analysis investigated which metrics 

drive the overall condition and performance 

measures for both fair and poor condition 

using the alternate thresholds. Table 6, for 

example, presents the drivers for fair condition 

when only one of three metrics is causing 

the overall pavement condition to be fair. 

The G-G-P and the G-G-F groupings represent 

12 and 45 percent of the total number of 

groupings in fair condition, respectively. The 

table shows that cracking is the driving metric 

for the G-G-P grouping, while rutting is the 

driving metric for the G-G-F grouping, and 

roughness is rarely the driver of either the 

G-G-P or G-G-F groupings.

Guidelines to Affect Measures

The findings from the validation study enabled 

the development of guidelines for informing 

decisionmaking to affect the performance 

measures. The guidelines are provided as 

a stand-alone report, and their goal is to 

illustrate potential strategies to move the 

overall condition of pavements from poor to 

fair to good.(6) Toward achieving this goal, the 

guidelines enable agencies to address critical 

questions, such as what are the performance 

measure drivers and what are the effects of 

M&R treatments on condition metrics and 

overall condition? 

The approach in the guidelines begins with 

development of performance measure drivers 

for metric conditions and overall condition 

using agency data. The drivers are then 

combined with the effects of M&R treatments, 

and the findings are used to develop a list 

of potential M&R treatments that affect the 

condition. The final step is the integration of 

the results within the pavement management 

system (PMS). Challenges that agencies need to 

consider when implementing the performance 

measures are also provided at the end of the 

guidelines. They include different data sources 

(HPMS versus PMS), changes in optimization 

goals (how measures affect decisionmaking), 

and updating of models within PMS. Ultimately, 

agencies are required to meet the performance 

measures or face loss of flexibility for spending 

National Highway Performance Program funds 

until the minimum required condition levels 

are exceeded. 

Conclusions

The results of this validation study showed that 

the performance measures are comprehensive 

with respect to the state of the practice—they 

address functional and structural (albeit using 

cracking as a surrogate) performance. They are 

balanced as they comprise several individual 

types of metrics (IRI, cracking, rutting, 

and faulting), and they show the expected 

performance trend over time. A parallel study 

detailed in the FHWA Interstate Pavement 

Condition Sampling report addresses these 

and other criteria, including appropriateness 

or measures at the national level.(7) Although 

improvements are possible, the performance 

measures are considered appropriate and valid 

for their intended purposes.

G-G-P = good–good–poor; G-G-F = good–good–fair.
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References

1. Moving Ahead for Progress in the 21st 

Century Act. (2012). Public Law No. 112–141, 

126 Stat. 405. 

2. Federal Register. (2015). National 

Performance Management Measures; 

Assessing Pavement Condition for the 

National Highway Performance Program and 

Bridge Condition for the National Highway 

Performance Program, 80(2), 80 FR 325.

3. Fixing America’s Surface Transportation Act. 

(2015). Public Law No. 114–94, 129 Stat. 1312.

4. Federal Register (2017). National 

Performance Management Measures; 

Assessing Pavement Condition for the 

National Highway Performance Program 

and Bridge Condition for the National 

Highway Performance Program, 82(11), 23 

CFR, Part 490. 

5. Visintine, B.A., Simpson, A.L., and Rada, G.R. 

(2017). Validation of Pavement Performance 

Measures Using LTPP Data: Final Report, 

Report No. FHWA-HRT-17-089. Federal 

Highway Administration, Washington, DC. 

6. Visintine, B.A., Rada, G.R., and Simpson, 

A.L. (2017). Guidelines for Informing 

Decision-Making to Affect Pavement 

Performance Measures: Final Report, Report 

No. FHWA-HRT-17-090. Federal Highway 

Administration, Washington, DC. 

7. Rada, G.R., Simpson, A.L., Visintine, B.A., 

and Groeger, J.L. (2016). Interstate Pavement 

Condition Sampling, Report No. FHWAHIF-17-022. Federal Highway Administration, 

Washington, DC.

Researchers—Beth A. Visintine, Amy L. Simpson, and Gonzalo R. Rada.

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http://www.fhwa.dot.gov/research.

Key Words—MAP-21, FAST Act, Long-Term Pavement Performance, LTPP, pavement 

performance measures, pavement metrics, pavement condition, IRI, cracking, rutting, faulting.

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 

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APRIL 2018 FHWA-HRT-17-105

HRDI-30/04-18(200)E