Next-Generation Pavement 

Performance Measures

FHWA Publication No.: FHWA-HRT-23-076

FHWA Contact: Nadarajah Sivaneswaran (Siva) 

(ORCID: 0000-0003-0287-664X), HRDI-20, Office of Infrastructure, 

(202) 493-3147, nadarajah.sivaneswaran@dot.gov

This TechBrief is part 1 of a two-part series (part 2 is 

Next-Generation Transportation Asset Management Methodology

(FHWA-HRT-23-075)), and summarizes the Federal Highway 

Administration (FHWA) report Development of Next Generation 

Pavement Performance Measures and Asset Management 

Methodologies To Support MAP-21 Performance Management 

Objectives (FHWA-HRT-23-102).

INTRODUCTION

Since 2012, Federal legislation has promoted the use of performance-based 

decisions for managing the Nation’s highway system. The Moving 

Ahead for Progress in the 21st Century (MAP-21) Act identified seven 

national goals and established requirements for pavement and bridge 

performance measures on the National Highway System (P.L. 112-141; 

23 U.S. Code (U.S.C.) §115; 23 Code of Federal Regulations (CFR) 

Part 490). MAP-21 also established requirements for the development 

and implementation of risk-based Transportation Asset Management 

Plans (TAMPs) by State departments of transportation (DOTs) (23 CFR 

Part 515). These requirements promote the use of data-driven investment 

decisions to preserve the public’s investment in the highway system and 

maintain the highway infrastructure assets in a state of good repair (SOGR).

Although the condition-based pavement performance metrics defined in 

23 CFR 490.309 (e.g., cracking, rutting, faulting) meet the immediate needs 

under the legislation, FHWA initiated research to explore “next-generation” 

pavement performance measures (NGPPMs) that are more proactive in 

driving investment decisions that lead to enhanced long-term performance. 

FHWA also investigated the feasibility of a methodology to help agencies 

manage their highway infrastructure as a system rather than a network of 

individual asset classes.

The research was initiated in 2015 as a two-phased effort titled Identification 

of Effective Next-Generation Pavement Performance Measures and Asset 

Management Methodologies to Support MAP-21 Performance Management 

Requirements (FHWA n.d.). Phase Ӏ resulted in the identification of eight 

promising pavement performance measures (not currently required under any 

Federal legislation) capable of being used as leading indicators for long-term 

investment strategizing and decisionmaking (Bryce et al. 2016; Sadasivam 

and Mallela 2017). Two promising transportation asset management 

methodologies (TAMMs) were also identified through the research. In 

Phase II, after further development and analysis, seven of the promising 

performance measures and one of the proposed TAMMs were pilot 

TECHBRIEF

Research, Development, 

and Technology

Turner-Fairbank Highway 

Research Center 

6300 Georgetown Pike 

McLean, VA 22101-2296

https://highways.dot.gov/research
2

implemented at the State level to validate their use. The 

study also sought to validate the performance measures 

at the Federal level (Ram et al. 2023).

This TechBrief summarizes key findings from the 

research efforts to validate the NGPPMs and provides 

insights on how they can be used to support agencies’ 

decisionmaking processes. A separate TechBrief 

documents the validation of the proposed TAMM as 

an approach to support cross-asset decisionmaking 

(Thompson et al. 2023).

NGPPMs

Long-term investment decisions benefit from the 

use of performance measures that indicate expected 

pavement performance and investment needs. 

Performance measures that enable the implementation 

of a comprehensive asset management program may be 

grouped into the following three broad categories, as 

illustrated in figure 1.

• Condition measures that are specific to an asset class 

and agency (e.g., pavement roughness, rutting, and 

cracking, and an agency’s rating scale of pavements in 

good/fair/poor conditions).

• Lifecycle measures that provide information 

on the lifecycle cost (LCC) of maintaining a 

pavement network.

• Financial measures that describe the financial 

sustainability of an agency’s pavement 

management program.

Current and projected conditions are two common 

indicators of pavement performance. Condition is 

generally considered a measure that is physically 

observable through a standard rating protocol 

(e.g., cracking, rutting, roughness). These types of 

condition measures are currently used in pavement 

management systems (PMSs) and are predicted into 

future years using established deterioration models. 

Such models, combined with treatment application costs, 

enable agencies to estimate the cost effectiveness of 

applying treatments at different points in time. Applying 

this process to an entire network provides agencies with 

the information needed to evaluate different investment 

strategies. Most agencies already have procedures to 

rate asset conditions, and condition-based measures 

are well understood. However, these measures tend to 

lag behind investment decisions because they follow 

investments that have already been made. This study 

sought to look for additional leading measures that could 

be used proactively to drive investment decisionmaking 

in concert with existing condition measures.

Figure 1. Illustration. Performance measures to 

enable a comprehensive pavement management 

program implementation.

Source: FHWA.

The study focused on lifecycle and financial 

performance measures. Lifecycle performance measures 

characterize and monetize the long-term investment 

strategies (i.e., construction, maintenance, and 

rehabilitation treatments) associated with providing 

a desired level of service (LOS) for a highway asset. 

These measures proactively encourage activities that 

reduce the long-term cost of system preservation. Thus, 

the focus is on evaluating strategy cost effectiveness and 

achieving the highest overall system performance at the 

lowest practicable LCC. Table 1 provides an overview of 

the lifecycle performance measures selected by FHWA 

for evaluation during the second phase of the project.

Financial performance measures indicate if an adequate 

level of investment is being made to offset the rate of 

asset-value depreciation or to satisfy treatment needs 

(not just to meet condition targets but to sustain those 

targets into the future). Four different financial measures 

were evaluated in this study, each of them providing 

unique information on the financial sustainability of 

an agency’s investment approach. Table 2 provides an 

overview of the financial performance measures selected 

by FHWA for evaluation during this phase of the study.

Each performance measure can help transportation 

agencies answer questions that are important to drive 

cost-effective investments, consider long-term pavement 

performance, and measure the overall effectiveness of 

the pavement management program.
3

Table 1. Lifecycle performance measure overview.

Measure Description Source

Remaining service 

interval (RSI)

• Identifies a structured sequence of the type and timing 

of various intensities of repair and replacement actions 

required to provide the desired LOS to users over the 

asset lifecycle at minimum practicable costs.

• Provides a framework to incorporate a whole-life perspective 

in determining future repair and replacement actions (i.e., 

considering both current condition and past actions in determining 

future actions, performance risks, and investment needs).

Rada et al. (2016);

Bryce et al. (2016)

Annualized unit 

cost ratio (AUCR)

• Establishes a ratio of the annualized cost of all planned expenditures 

over the pavement lifecycle to the annualized cost of expenditures 

under the optimized strategy.

• Compares planned investments to the optimized lifecycle strategy.

Sadasivam and 

Mallela (2017)

Cost accrual 

ratio (CAR)

• Represents the ratio of net present value (NPV) of all programmed 

costs over a chosen period against the NPV of the agency’s optimized 

lifecycle plan (LCP).

• Compares the actual investments made against the optimized lifecycle 

strategy that requires the minimum practicable LCC.

Sadasivam and 

Mallela (2017)

Table 2. Financial performance measure overview.

Measure Description Source

Asset sustainability 

index (ASI)

• Represents the ratio of the amount of the budget allocated to the amount 

needed to address all current management system treatment selections. 

• Helps decisionmakers use ASI to determine the adequacy of 

investments to address needs identified by the management system.

• Helps agencies determine a strategic investment plan using trends in the ASI.

Proctor, Varma, 

and Varnedoe (2012)

Asset sustainability 

ratio (ASR)

• Represents the ratio of asset maintenance, preservation, and 

replacement expenditure to asset depreciation for a given period.

• Focuses on the process that is expected to drive pavement condition.

• Helps decisionmakers determine if sufficient investments have been 

made in the current period.

Howard, Dixon, 

and Comrie (2011) 

Asset consumption 

ratio (ACR)

• Represents the ratio of depreciated asset replacement 

cost to the current replacement value.

• Establishes a metric that highlights the average proportion of as-new/as-built 

condition left.

• Helps agencies use ACR trends to determine if adequate resources are being 

invested to maintain current life expectancy.

Howard, Dixon, 

and Comrie (2011) 

Stewardship 

liability ratio (SLR)

• Represents the ratio of unfunded treatment needs to replacement cost 

of the network.

• Helps agencies compare treatment strategies. Applies to multiple assets. The 

application capability exists to conduct tradeoff analysis and compare the 

impact of different funding levels across asset classes.

Zimmerman et al. 

(2016)
4

VALIDATION STUDIES

The NGPPMs were tested and validated at three 

selected State highway agencies—Idaho Transportation 

Department, South Dakota DOT, and Texas DOT 

(Ram et al. Forthcoming). In addition, the researchers 

attempted to validate the measures at the Federal 

level. The key findings from the validation efforts are 

summarized in the next section.

Key Findings From the State 

Validation Study

Table 3 summarizes the key takeaways and validation 

experiences associated with the lifecycle and financial 

performance measures evaluated in this study.

Table 3. Strengths and implementation challenges for each NGPPM evaluated.

Type Approach/

Measure Summary Testing and Validation Experiences

Lifecycle 

measures

RSI • Reduces the reliance on decision trees 

for treatment selection.

• Identifies the lowest LCC strategy 

that meets established objectives 

and performance requirements 

under constrained and unconstrained 

budget situations.

• Difficult to develop optimized models if the 

pavement management practices at an agency 

are not currently considering LCCs.

• Analysis runs are time- and resource-intensive.

• Output dataset requires more storage space.

Lifecycle 

measures

AUCR • Provides a relatively simple measure that 

compares the annualized costs of current 

strategies to the optimized strategy and is 

intuitive to decisionmakers.

• Helps visualize the magnitude of 

the deviation from the optimized 

lifecycle strategy.

• Calculating the measure would require the 

identification of the lowest LCC strategy using 

either the RSI analysis or other methodologies.

• Agencies would have to conduct additional work 

to determine the level of acceptable deviation 

from the optimized plan and its implications.

Lifecycle 

measures

CAR • Helps in visualizing the deviation from 

the optimized LCP and the short- and 

long-term differences in spending when 

comparing multiple lifecycle strategies.

• Aids in evaluating the financial 

sustainability of different lifecycle strategies 

assessed by the agency.

• Calculating the measure depends on the 

identification of the lowest LCC strategy using 

either the RSI analysis or other methodologies.

• Agencies would have to conduct additional work 

to determine the level of acceptable deviation 

from the optimized plan and its implications.

Financial 

measures

ASI • Assists in monitoring investment 

levels in different treatment categories 

(maintenance, preservation, etc.) and how 

they impact asset performance.

• Applies across different asset classes.

• Supports the establishment of short- and 

long-term investment targets at both the 

agency and district levels that will help the 

agency meet or exceed the desired SOGR.

• The measure requires calculation of the “need,” 

which can be challenging to apply consistently.

Financial 

measures

ASR • Indicates if an agency is 

investing adequately to offset 

asset-value depreciation.

• Applies across different asset classes.

• Supports the establishment of targets that 

will help keep the asset-value depreciation 

rate in check.

• Calculating asset value depreciation can 

be challenging, and approaches can vary 

significantly across agencies.

• Measures can help agencies communicate a 

story that can be different from what the condition 

trends may show.
5

Lifecycle Measures 

From a whole-life perspective, the remaining service 

interval (RSI) framework is the most robust analysis 

approach that can help agencies establish long-term, 

cost-effective treatment strategies. However, the RSI 

approach is computationally intensive, and the analysis 

can be time consuming. Additionally, a learning curve 

is associated with implementing this approach within 

State DOTs. Computational resources will continue to 

improve over time, and the processing power of computers 

5 to 10 yr from now could significantly reduce the time 

required to conduct the RSI analysis.

The RSI approach will help agencies:

• Evaluate the impact of all feasible treatment types 

and timing combinations without limitations 

imposed by decision trees.

• Make treatment recommendations based on 

lifecycle costs.

• Consider the short- and long-term impacts 

of deviating from the lowest LCC strategy 

(optimized strategy).

• Support pavement LCP activities required 

by 23 CFR Part 515.

FHWA has published a TechNote on the RSI approach that 

provides a simplified overview of the fundamental concepts 

associated with RSI (Ram et al. 2021). This TechNote 

provides suggestions on how agencies can start considering 

the RSI approach to support pavement LCP activities.

The other lifecycle measures evaluated in this study 

(CAR and AUCR) can be used in conjunction with the RSI 

analysis to help visualize how different lifecycle strategies 

compare against the optimized strategy.

Financial Measures

Asset sustainability index (ASI), asset sustainability 

ratio (ASR), and stewardship liability ratio (SLR) are 

the three most useful financial measures. When used 

together, these leading measures can help answer the 

following questions:

• How much needs to be invested to offset 

asset-value depreciation?

• Are the right types of investments (in terms of 

the right mix of treatment types that help offset 

depreciation effectively) being made rather than just 

focusing on the right investment level?

• When should a significant shift in the investment 

approach be considered to ensure acceptable 

long-term performance? (Ram et al. Forthcoming).

In the absence of network-level pavement structural 

condition information, the ASR can also serve as a 

surrogate measure for structural health if the asset value 

calculations are modeled based on predicted structural 

distresses (e.g., rutting and fatigue cracking).

The ACR measure provides some useful information 

from a communication standpoint (e.g., what fraction 

of the as-built condition is left). However, the story 

is no different from what conventional condition 

measures, such as the international roughness index (IRI) 

and composite indices, would help communicate 

(Ram et al. Forthcoming). Thus, agencies may not find 

the ACR measure very appealing.

One benefit of all the measures evaluated in this study is 

that they are asset generic and can be expanded to other 

asset classes besides pavements. This feature is beneficial 

for comparing investment options across asset classes.

Table 3. Strengths and implementation challenges for each NGPPM evaluated. (continued)

Type Approach/

Measure Summary Testing and Validation Experiences

Financial 

measures

ACR • Indicates the proportion of as-new 

condition left in an asset class.

• Applies across different asset classes.

• Calculating asset value depreciation can 

be challenging, and approaches can vary 

significantly across agencies.

• The measure does not communicate anything 

different than existing condition-based measures.

Financial 

measures

SLR • Tracks time-series trends on the 

progression of backlog when compared 

to the replacement value of the 

pavement work.

• The SLR was previously known as the 

backlog reduction ratio.

• If an agency’s PMS does not report backlog, 

calculation of this measure can be difficult.

• The measure can help determine whether a 

significant shift in treatment strategies may be 

necessary to help keep the rate of change of 

backlog in check.
6

NGPPM IMPLEMENTATION 

Even without full implementation, State DOTs can 

start using the NGPPMs evaluated under this project 

with traditional condition-based measures to better 

understand their usefulness in the pavement management 

decisionmaking process. Some short- and long-term 

strategies for practical use are as follows:

• Short-term strategies (<5 yr).

○ Calculate the NGPPMs and compare the results 

to existing agency-based condition measures to 

see if the measures can help communicate a more 

informed story.

○ Use the measures to communicate differences 

between various treatment strategies and 

funding levels evaluated by the agency for a 

nontechnical audience.

○ Pilot the use of NGPPMs within a district/region 

or county to validate treatment decisions.

○ Conduct training for PMS practitioners on how the 

measures can be implemented today through the 

development of simple tools that can be used in 

conjunction with PMS results.

• Long-term strategies (5 to 10 yr).

○ Work with the PMS vendor to make necessary 

adjustments that enable the calculation of 

the measures within the PMS without other 

supplemental tools.

○ Use performance measures to validate pavement 

management decision trees. This process involves 

assessing whether the PMS is recommending the 

right type of treatment at the right time to offset 

asset-value depreciation over the long term.

○ Improve measure computation accuracy to make 

it more representative of actual values through 

refinements of PMS data and model inputs.

ENHANCEMENTS TO TOOLS 

AND PROCESSES

The following suggestions can help advance the 

implementation of the NGPPMs:

• Ability to calculate the “need” within the PMS.

The calculation of the annual “need” to achieve or 

maintain the desired SOGR at the network level 

for each analysis run conducted using the PMS 

will greatly simplify the process of calculating the 

financial measures.

• Ability to estimate asset value depreciation within 

the PMS. If the analysis module within the PMS 

has tools to model asset valuation and asset-value 

depreciation using one or more approaches, it will 

significantly simplify the process of calculating 

financial measures, such as ASR and ACR.

• Ability to calculate LCC within the PMS. Typical 

PMS software tools do not calculate LCC suitable for 

use with the RSI approach. Considering the remaining 

value of treatments beyond the analysis period or the 

cost to restore the pavement network to the desired 

SOGR at the end of the analysis period will help in 

comparing the long-term impacts of different lifecycle 

strategies evaluated by the agency.

• Ability to evaluate all feasible treatment strategies 

without relying exclusively on decision trees.

Many of the PMS tools generate multiple treatment 

strategies for each pavement segment in the network. 

However, the strategies developed rely heavily on 

the rules established using decision trees. Decision 

trees rely on predetermined thresholds for distresses, 

pavement condition, and/or other performance 

indicators (cracking, rutting, overall condition index, 

IRI, etc.). Using these somewhat subjective treatment 

trigger thresholds could potentially result in the true 

optimal solution being missed.

• Use of structural health data to model asset 

valuation. The assessment of the structural health 

of pavements using technologies like the traffic 

speed deflection device and using data from these 

technologies to develop asset valuation models that 

are tied to pavement structural health will improve 

the overall decisionmaking characteristics of 

the NGPPMs.

• Use of robust pavement performance models.

The ability to develop performance models that 

not only consider the type of treatment and other 

associated parameters (e.g., traffic level, climate, 

functional class) but also the pretreatment structural 

and functional conditions will allow agencies to 

differentiate between viable treatment strategies 

and timings.

• Staff dedicated to pavement management data 

analytics. Staffing has always been a challenging 

issue for State DOTs. Agencies are already dealing 

with huge volumes of data, and the amount of data is 

only expected to increase in the future. A dedicated 

group of agency staff primarily focused on converting 

all the data collected (current and historical) into 

useful information that can be leveraged in the 

decisionmaking will be helpful in the future.
7

IMPLEMENTATION BENEFITS

The implementation of the NGPPMs will provide 

agencies with an enhanced ability to assess and compare 

pavement management strategies and make decisions 

that are not only cost effective in the short term but also 

provide the best return on investments over the lifecycle. 

The main uses from the implementation of the NGPPMs 

are summarized as follows:

• Identify pavement treatment strategies that 

result in the lowest practicable LCC. The lifecycle 

approaches and measures evaluated and validated 

in this project (RSI, CAR, and AUCR) will help 

agencies assess the effectiveness of the pavement 

management strategies selected by comparing the 

planned expenditures and performance outcomes 

to the optimized strategy. This approach will help 

agencies make necessary adjustments to the strategies 

being implemented to ensure that the deviation from 

the optimized strategy is minimized.

• Convey a message that may not be possible using 

just condition-based performance measures.

The use of NGPPMs may help agencies communicate 

a story that may not be apparent from just conditionbased indicators. Financial measures, such as ASI, 

ASR, and SLR, will help agencies not only evaluate 

the effectiveness of their investment approaches in 

meeting and sustaining the desired SOGR but also 

measure the impact of the treatment strategies in 

maintaining asset value and keeping the backlog 

growth rate in check. In addition, NGPPMs provide 

leading measures that can be used to drive investment 

decisions. The time-series trends exhibited by these 

measures can help identify when a significant shift in 

the pavement management approach may be needed.

• Communicate performance outcomes using 

measures that resonate with decisionmakers.

The use of traditional performance measures, 

such as IRI, pavement distresses, and overall 

condition indexes, may not necessarily resonate 

with decisionmakers within agencies. The financial 

measures evaluated in this study communicate 

pavement network performance using simple, 

intuitive indicators that do not require specialized 

pavement management knowledge.

• Use measures for cross-asset tradeoff analysis.

Because all the performance measures evaluated 

in this study are asset generic, one of the main 

benefits is the potential to use these measures for 

cross-asset tradeoff analysis to evaluate the long-term 

impacts of different treatment strategies and funding 

allocation approaches.

REFERENCES

Bryce, J., G. Rada, S. Van Hecke, and 

J. Zissman. 2016. Identification of Effective 

Next-Generation Pavement Performance Measures 

and Asset Management Methodologies to Support 

MAP-21 Performance Management Requirements. 

Draft Final Report. Unpublished. Washington, DC: 

Federal Highway Administration.

Federal Highway Administration. n.d. “Identification 

of Effective Next-Generation Pavement Performance 

Measures and Asset Management Methodologies 

to Support MAP-21 Performance Management 

Requirements” (web page). https://highways.dot.gov/

research/projects/identification-effective-next-generationpavement-performance-measures-asset-management, 

last accessed March 30, 2023. 

Howard, J., J. Dixon, and J. Comrie. 2011. Australian 

Infrastructure Financial Management Guidelines. North 

Sydney, New South Wales: Institute of Public Works 

Engineering, Australia.

National Archives and Records Administration. 2023. Asset 

Management Plans. 23 CFR Part 515. Washington, DC. 

https://www.ecfr.gov/current/title-23/chapter-I/subchapter-F/

part-515, last accessed March 31, 2023. 

National Archives and Records Administration. 2023. 

National Performance Management Measures. 23 CFR 

Part 490. Washington, DC. https://www.ecfr.gov/current/

title-23/chapter-I/subchapter-E/part-490, last accessed 

March 24, 2023.

Proctor, G. D., S. Varma, and S. Varnedoe. 2012. Asset 

Sustainability Index: A Proposed Measure for Long-Term 

Performance. Report No. FHWA-HEP-12-046. 

Washington, DC: Federal Highway Administration. https://

www.planning.dot.gov/documents/ASI_report/ASI_July9_

FINAL_web.pdf, last accessed March 31, 2023. 

Rada, G. R., B. A. Visintine, J. Bryce, S. Thyagarajan, 

and G. E. Elkins. 2016. Application and Validation of 

Remaining Service Life Interval Framework for Pavements. 

Report No. FHWA-HRT-16-053. Washington, DC: Federal 

Highway Administration. https://www.fhwa.dot.gov/

publications/research/infrastructure/pavements/16053/

index.cfm, last accessed March 31, 2023. 

Ram, P. V., K. L. Smith, K. A. Zimmerman, and 

S. Thyagarajan. 2021. Remaining Service Interval: A White 

Paper. FHWA-HRT-21-006. Washington, DC: Federal 

Highway Administration. https://www.fhwa.dot.gov/

publications/research/infrastructure/pavements/21006/

index.cfm, last accessed March 31, 2023. 
8

Ram, P. V., P. Thompson, S. Pai, A. Borthakur, O. Smadi, 

K.L. Smith, K. A. Zimmerman, B. W. Allen, K. Mugabe, and 

B. Bektas. Forthcoming. Development of Next-Generation 

Pavement Performance Measures and Asset Management 

Methodologies To Support MAP-21 Performance 

Management Objectives. Report No. FHWA HRT-23-102. 

Washington, DC: Federal Highway Administration.

Sadasivam, S., and J. Mallela. 2017. Identification of 

Effective Next-Generation Pavement Performance 

Measures and Asset Management Methodologies to 

Support MAP-21 Performance Management Requirements. 

Volume 1: Pavement Performance Measures. Unpublished. 

Washington, DC: Federal Highway Administration.

Thompson, P., P. Ram, K. Smith, K. Zimmerman, 

and B. Allen. 2023. Next-Generation Transportation 

Asset Management Methodology. Publication No. 

FHWA-HRT-23-075. Washington, DC: Federal 

Highway Administration.

U.S. Government Printing Office. 2012. Moving Ahead 

for Progress in the 21st Century Act. Public Law 112-141. 

https://www.govinfo.gov/app/details/PLAW-112publ141, 

last accessed May 3, 2023. 

U.S. House of Representatives. 2022. Advance 

Construction. 23 U.S.C. §115. https://uscode.house.gov/

view.xhtml?req=granuleid:USC-prelim-title23-section115

&num=0&edition=prelim, last accessed March 24, 2023.

Zimmerman, K. A., B. W. Allen, P. V. Ram, 

G. M. Duncan, O. Smadi, K. L. Smith, K. R. Manda, 

and B. A. Bektas. 2016. Identification of Effective 

Next-Generation Pavement Performance Measures and 

Asset Management Methodologies to Support MAP-21 

Performance Management Requirements. Unpublished. 

Washington, DC: Federal Highway Administration. 

Researchers—This TechBrief was developed by Prashant Ram (ORCID: 0000-0003-4229-998X), Paul Thompson 

(ORCID: 0000-0002-6559-7966), Kelly Smith (ORCID: 0000-0003-0460-0460), Katie Zimmerman (ORCID: 0000-

0002-5730-5185), and Brad Allen (ORCID: 0000-0002-1350-1563) and prepared under FHWA’s Development of 

Next-Generation Pavement Performance Measures and Asset Management Methodologies to Support MAP-21 

Performance Management Objectives project (693JJ318C000001). Applied Pavement Technology, Inc. of 

Urbana, IL, served as the contractor to FHWA.

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

made to the FHWA divisions and Resource Center.

Availability—This TechBrief may be obtained at https://www.fhwa.dot.gov/pavement.

Key Words—Pavement management, performance measures, asset management, leading indicators, lifecycle 

measures, financial measures, remaining service interval, cross-asset tradeoff analysis.

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, manufacturers, or outside entities. Trademarks, names, or logos 

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

included for informational purposes only and are not intended to reflect a preference, approval, or endorsement of 

any one product or entity.

Non-Binding Contents—Except for the statutes and regulations cited, the contents of this document do not have 

the force and effect of law and are not meant to bind the public in any way. This document is intended only to provide 

clarity to the public regarding existing requirements under the law or agency policies. While this document contains 

nonbinding technical information, you must comply with the applicable statutes and regulations.

Quality Assurance Statement—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.

SEPTEMBER 2023 FHWA-HRT-23-076

HRDI-20/09-23(WEB)E

Recommended citation: Federal Highway Administration, 

Next-Generation Pavement Performance Measures 

(Washington, DC: 2023) https://doi.org/10.21949/1521381