Next-Generation 

Transportation Asset 

Management Methodology

FHWA Publication No.: FHWA-HRT-23-075

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 2 of a two-part series (part 1 is 

Next-Generation Pavement Performance Measures

(FHWA-HRT-23-076)), 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 the national performance measures for 

pavements and bridges 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). 

Requirements were also established for the development and implementation 

of risk-based Transportation Asset Management Plans 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.

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 

transportation 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.a). Phase Ӏ of the project identified 

eight promising pavement performance measures (not currently required 

under any Federal legislation) highway agencies could use as leading 

indicators for long-term investment strategizing and decisionmaking, along 

TECHBRIEF

Research, Development, 

and Technology

Turner-Fairbank Highway 

Research Center 

6300 Georgetown Pike 

McLean, VA 22101-2296

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

with two promising transportation asset management 

methodologies (TAMMs). In Phase II, after further 

development and analysis, seven of the promising 

performance measures and one of the proposed TAMMs 

were pilot 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. Forthcoming).

This TechBrief summarizes key findings from the 

research efforts to validate the proposed TAMMs as 

an approach to support cross-asset decisionmaking. 

A separate TechBrief documents the validation and 

potential use of the NGPPMs (Ram et al. 2023).

TAMM DEVELOPMENT

Transportation agencies own, operate, and maintain a 

diverse infrastructure to support transportation services 

to the public. This infrastructure includes a variety of 

asset classes, such as pavements, bridges, tunnels, 

earthworks, drainage facilities, guardrails, traffic control 

devices, lighting, and buildings. These asset classes 

work together as an infrastructure network; however, 

each asset class has its own technologies and specialized 

maintenance requirements, and the performance of each 

asset class affects the public in its own way.

Because of the specialized technologies and professional 

disciplines required to construct and maintain the various 

classes of physical assets, transportation agencies 

have traditionally managed them separately. Over 

time, each asset class has evolved its own conceptual 

frameworks, research concerns, training requirements, 

technical jargon, and performance metrics. These issues 

make it challenging to develop practices and tools that 

leverage the great strengths of the separate technical 

disciplines while enabling the integrated management 

of the infrastructure network when viewed holistically 

(Maggiore et al. 2015).

PROPOSED TAMM

Agencies face decisions that relate current choices 

about work to be done (e.g., maintenance, preservation, 

rehabilitation, reconstruction, network expansion) to 

future expectations for network performance. Changes 

in the allocation of resources in the near term can be 

expected to lead (in a cause/effect fashion) to changes in 

performance later. If the agency allocates a greater share 

of funding to urban highways, for example, then urban 

network performance should be expected to improve 

relative to rural network performance. Similarly, 

focusing resources on safety in preference to other goals 

should result in greater safety improvement. 

To adopt a results-oriented approach to decisionmaking, 

agencies need modeling tools that estimate future 

performance based on near-term actions. These tools 

use quantitative data about current asset inventory and 

performance, scientific and statistical understanding of 

deterioration processes, cost structure, project delivery 

capabilities, and treatment effectiveness. The tools 

then convert this information to network performance 

estimates at a future time.

Some of the needed tools already exist in the form of 

asset management systems like pavement management 

systems (PMSs) and bridge management systems 

(BMSs). What is missing is the ability to examine this 

tradeoff in an asset-generic way so that decisions that are 

not asset-specific (such as budgeting and programming) 

can be related to stakeholder-relevant measures of 

network performance while maintaining consistency 

with the technical analysis already provided by the 

management systems.

The researchers proposed TAMMs to support tradeoff 

analysis among multiple objectives and multiple asset 

classes that are traditionally managed separately, 

including at least pavements and bridges. The challenge 

in developing this methodology was to fairly reflect the 

diverse ways that different asset classes can affect road 

users and transportation system objectives. 

The methodology needed to be attainable using existing 

pavement and bridge management systems capabilities. 

The tradeoff analysis also needed to address common 

planning use cases in a familiar and implementable 

way for State DOTs and other transportation 

infrastructure owners.

Figure 1 illustrates how existing management system 

capabilities can be harnessed to support a cross-asset 

tradeoff analysis. Management systems produce sets 

of investment candidates, characterized by attributes 

that can be expressed in a sufficiently generic way 

to make them comparable. The tradeoff analysis can 

then use a prioritization scheme to provide decision 

support for common transportation asset management 

business processes.

TAMMs commonly integrate tradeoff analyses with 

priority setting because both functions demand 

compatible expressions of objectives and constraints. 

Thus, the TAMM was conceived as a prioritization 

approach that can be made sensitive to common 

performance goals, including condition, cost, safety, 

and mobility.

The proposed TAMM used social cost to evaluate 

tradeoffs, specifically, the savings in social cost if an 

investment is selected today rather than delaying it. 

Assuming the agency wishes to keep its network in 
3

Figure 1. Illustration. Harnessing management systems to support cross-asset tradeoff analysis.

Source: FHWA.

service as a long-term social concern, the tradeoff 

analysis can be formulated as a problem of minimizing 

social cost. The analysis will include the consideration 

of long-term agency and user costs, either combined or 

independently depending on agency preferences, and 

can be constrained by funding availability and the desire 

to keep the network operating at the desired level of 

service. Designed in this way, an agency can use both 

agency and user cost components to evaluate tradeoffs 

or limit the analysis to consider only agency cost 

components if desired.

Existing models and research provide practical ways 

of computing the cost components. Ultimately, the 

proposed TAMM is used in the same way as any 

benefit-cost ratio (BCR) in a benefit-cost analysis and 

is compatible with common algorithms for priority 

setting and tradeoff analysis under fiscal constraints 

(World Bank 2022).

Investment candidates are prioritized by BCR each 

year. Top-ranked candidates are selected, subject 

to funding constraints. Candidates that cannot be 

selected are delayed, which may cause additional social 

costs. Performance outcomes for the network are the 

combined result of asset deterioration and the effects 

of all the investments that can be performed within the 

funding constraint.

New decision scenarios can be prepared by making 

changes in the policies that generate the investment 

candidates. A quicker method allows weighting 

factors to be applied to the components of social cost 

representing agency costs, safety-related user costs, and 

mobility-related user costs. This quicker method uses 

social cost in the same manner as a utility function. 

Initially, all types of costs may be given equal weight, 

but the agency may decide to increase the weight 

assigned to an objective (or to a portion of the network) 

to improve the performance of that portion of the 

program. The idea of shifting money to a part of the 

program that needs better performance is intuitive to 

program managers.

Using the outcome measures (based on asset condition, 

safety, mobility, and environmental sustainability) and 

the BCR, the researchers developed the methodology 

to forecast the results of any given decision scenario. 

A series of decision scenarios can then be developed 

that represent alternative fiscal scenarios, alternative 

allocations of resources, and/or alternative policies. 

A set of targets, expressed in the form of the outcome 

measures (percent good condition, percent poor 

condition, percent sufficient for safety, percent sufficient 

for mobility) can be used to assess whether a given 

scenario is likely to achieve its intended objectives. 

If the targets are not all satisfied, adjustments can be 

made in the decision scenario to find a more satisfactory 

solution. If no solution satisfies all the targets, then it 

may be necessary to adjust the targets.

Figure 2 represents a flow chart of this tradeoff analysis 

algorithm. The algorithm is a common feature of 

PMSs and BMSs and a feature of many homegrown 

spreadsheet programs that agencies have built to support 

capital-budgeting exercises. The main difference in 

this case is that the performance measures used in the 

algorithm are constructed to be as asset generic as 

possible to enable tradeoffs involving multiple classes of 

assets (especially pavements and bridges).
4

Figure 2. Illustration. Tradeoff analysis algorithm.

Source: FHWA.
5

To assist in the implementation of the proposed 

TAMM, the researchers developed and pilot tested a 

spreadsheet model at three State highway agencies––

Idaho Transportation Department, South Dakota DOT, 

and Texas DOT. The findings from the implementation 

efforts are discussed later in this TechBrief. 

FACTORS CONSIDERED IN 

DEVELOPING THE TAMM

This study focused on the development of a tradeoff 

analysis methodology that can serve the diversity of 

asset classes and cut across their boundaries to support 

business processes that manage the infrastructure 

network holistically. To enhance implementation 

feasibility, the methodology relied on existing 

management systems as far as possible and focused on 

a performance measure that can be used to prioritize 

investments and allocate resources fairly across all 

classes of assets. Such a measure could then be used in 

planning tools already familiar to transportation agencies 

to serve their business needs. The search for this measure 

produced the following findings:

• A focus on the network was facilitated by 

concentrating on existing Federal legislation and rules 

that agencies already observe that cut across asset 

class boundaries. For example, the focus included 

the statement of national transportation goals in the 

requirements of transportation performance measures 

and the requirements of management systems 

(23 U.S.C. §150(b); 23 CFR 490; 23 CFR 515).

• State DOTs often have legislation or strategic plans 

that closely follow the national goals, particularly 

condition, safety, mobility (for people and freight), 

and environmental sustainability. Some agencies 

also have additional objectives for factors such as 

customer satisfaction. Different asset classes affect 

these objectives in different ways. In some cases, 

the objectives are taken as constraints rather than 

variables to be optimized. For example, PMSs often 

do not attempt to quantify the mobility benefits of 

projects but merely apply constraints to limit mobility 

impacts due to deteriorated conditions.

• Federal transportation performance management rules 

give a precise definition for condition measures that 

are well established in current practice and support 

certain essential processes such as tracking of trends 

and management of targets (23 CFR Part 490). The 

pavement and bridge measures are superficially 

similar in that they both are weighted averages of 

the assets in good or poor condition. However, the 

definitions of good and poor fundamentally differ 

among asset classes, so these measures are not 

comparable across asset classes and cannot be used to 

compare dissimilar investments or to provide a basis 

for resource allocation. Further, these measures are 

only defined for pavement and bridge conditions, not 

for other asset classes or other performance concerns.

• Federal management system requirements reflect 

existing agency requirements and long-standing 

research, particularly the practice of lifecycle cost 

analysis (LCCA), risk management, and benefit-cost 

priority setting (23 CFR 515.17). However, many 

existing management systems, especially PMSs, are 

not configured by the agency for LCCA and risk.

• The research team, while reviewing the current stateof-the-art practice, observed that commonly used asset 

management systems were not using a performance 

measure that could be interpreted consistently across 

asset classes for tradeoff analysis. However, certain 

related software tools, such as FHWA’s Highway 

Economic Requirements System (HERS), have some 

of the needed capabilities but lack others for lifecycle 

analysis and preservation planning (FHWA 2000). 

Moreover, existing management systems may have 

the necessary data and basic analysis capabilities to 

derive a usable performance measure.

• The representation of transportation goals or objectives 

as constraints in decision trees, rather than as variables 

to be optimized, limits the ability to readily perform a 

tradeoff analysis that investigates a range of alternative 

allocations of resources among asset classes or among 

performance concerns due to the iterative nature of the 

analysis. This inherent feature of many PMSs makes 

it difficult and time-consuming to develop a sufficient 

range of scenarios to support cross-asset tradeoff 

analysis. This issue is less of a concern with BMSs 

because they were designed from the start to work 

with highly diverse inventories, including nonbridge 

structures, and to model performance concerns other 

than condition.

DEVELOPMENTAL STRATEGIES TO 

SUPPORT TAMM IMPLEMENTATION

The barriers to improved management system 

capabilities to support cross-asset tradeoff analysis 

are found on both the supply and demand sides of 

the economic equation for developing and using 

management systems. On the supply side, no standard 

framework exists for the data needed to support crossasset tradeoff analysis. As a result, developers lack a 

data model and analytical process that are sufficiently 

stable so that the cost of developing the system can be 

spread over multiple agencies.
6

On the demand side, the agency decisionmakers for 

selecting management systems are either pavement 

experts or bridge experts, but rarely both. Expertise in 

both areas, however, is beneficial. Cross-asset tradeoff 

analysis is a third area of expertise that is not often 

recognized. Additionally, most agency programming 

staff are not aware that cross-asset tradeoff analysis is 

feasible as an application that can be built on existing 

management systems. 

Developers need to have expertise on appropriate 

features for cross-asset analysis. The most important 

areas to develop are as follows:

• Cross-disciplinary understanding and experience 

with multiple asset classes (including pavements 

and bridges) from a program management or 

research perspective.

• Lifecycle thinking, especially as practiced for 

network-level applications.

• User cost models and related methods of econometric 

analysis of public policy.

• Risk analysis encompassing the probability of 

extreme events, the effects of such events on 

transportation assets, and the resulting disruptions to 

transportation services.

• Software architecture and design expertise with 

large computational systems to optimize execution 

efficiency, including algorithm design and 

multithreaded programming.

Expertise in the above areas exist and are readily 

available, but companies developing management 

systems need sufficient assurance that an investment 

in these skills will satisfy their business needs. In the 

transportation industry, assurance could be provided 

by means of standard-setting processes or joint 

development projects.

TAMM VALIDATION FINDINGS

The next-generation TAMM was conceptualized as 

a means of integrating multiple asset classes within 

appropriate common business processes that might be 

implemented within the next 10 yr using data and tools 

likely to be available within that time frame. These 

cross-asset business processes include the determination 

of funding levels, network policy formation, resource 

allocation, project development, priority programming, 

and delivery. Implementation feasibility was a central 

concern, leading to decisions to keep the framework as 

simple as possible and relying on existing systems as 

much as possible.

If an agency’s current process for budget allocation is 

simply the continuation of historical norms, the idea 

of using a tradeoff analysis may be new. Applying 

a tradeoff analysis requires stakeholders to consider 

changes in historical allocations, which may imply 

changes in staffing and other resources and may affect 

the workload of the contractor community. These 

changes make the impacts of such decisions much bigger 

than the scope of existing management systems. Part of 

the value of using an economic performance measure to 

evaluate tradeoffs is the ability to estimate the economic 

benefit of a change in historical norms, which can be 

weighed against the costs.

The complex part of implementing the proposed 

methodology is computing the required performance 

measures, including forecasts of outcomes and the 

benefit-cost priority measures. In the pilot studies, the 

benefit-cost calculation was generated exogenously in a 

manner considered temporary, using iterative processes 

for pavements and an open-source spreadsheet for 

bridges. These calculations did not work as well as 

expected (especially for pavements) due to the lack 

of necessary models (e.g., safety, mobility) within the 

PMS. A better approach is to enhance the PMS and 

BMS analyses and reporting capabilities to perform 

the necessary lifecycle and user cost analyses and then 

make the results available to outside programs for other 

purposes, including cross-asset tradeoff analysis. Such 

models are valuable for many purposes in pavement 

and bridge management because they fully reflect the 

economic benefits of infrastructure renewal work.

A working prototype of an investment candidate file 

was developed under this project that system developers 

can use to help them design an output format for the 

necessary data. In some cases, existing management 

systems may already perform the necessary calculations 

and merely need an appropriate format for exporting the 

results. Other cases may be more complex, especially in 

a PMS where safety and mobility are reflected only in 

constraints (such as “must levels”) rather than as user 

costs. The biggest problem noted in the pilot studies was 

the challenge of using the PMS to fully account for the 

benefits of pavement work.

Developers are often concerned about the computational 

intensity of long-term economic analysis. Remarkably, 

this concern has been a constant for the more than 

40 years that such tools have been in common use, 

even as the speed of computers has increased by many 

orders of magnitude. It may be that user expectations 

of such tools are increasing as fast as processor speed, 

leaving the widespread implementation constantly just 

beyond the horizon and allowing for further delays 
7

in adoption. Existing software tools such as HERS, 

the National Bridge Investment Analysis System, and 

StruPlan (an open-source, long-range renewal planning 

tool for transportation structures) show that execution 

times can be kept reasonable if system requirements 

are appropriately bounded and modern computational 

techniques and algorithms are used (FHWA 2000; 

Cambridge Systematics, Inc. 2011; Thompson 2021).

The researchers found few agencies having asset 

management systems for asset classes other than 

pavements and bridges capable of performing the 

necessary analysis. The main exceptions were agencies 

that incorporate nonbridge structures, such as tunnels, 

sign supports, and retaining walls, within their BMS. The 

proposed methodology is especially suitable for asset 

classes where preservation is a common action. Aside 

from nonbridge structures, assets could include unstable 

slopes, buildings (including rest areas), drainage facilities, 

intermodal facilities, signs, and barriers.

IMPLEMENTATION BENEFITS

The benefits of the proposed next-generation TAMM 

lie in the ability to manage an infrastructure network, 

maintaining an appropriate balance in resource 

allocations and performance among all the components 

of the network. This balanced approach helps to 

ensure that the desired level of service is provided at 

the lowest long-term cost, considering the differential 

levels of deterioration rates, cost, and risk that exist 

within the network. Elements of these benefits include 

the following:

• Existing management systems can continue to 

advance on their own lifecycles independently, taking 

advantage of the long-standing frameworks, expertise, 

training, tools, and research existing within each 

disciplinary area.

• Differences in performance among the parts of a 

network are evaluated objectively and equalized to the 

appropriate extent to best serve public needs.

• Differences in performance that remain within the 

network are justifiable based on objective analysis, 

helping the agency avoid unintended misallocation, 

especially among socioeconomic classes of users or 

geographic areas.

• Increments of transportation funding are allocated to 

the parts of the network that can most effectively use 

them to improve network performance.

• The long-term cost of keeping the network in service 

is minimized.

• Benefits of infrastructure renewal are estimated in a 

manner that is more consistent and complete and more 

easily communicated to stakeholders.

• Risk of extreme events, climate change, and advanced 

deterioration is allocated and balanced in a consistent 

way across all network components.

These benefits have always been part of the promise 

of asset management—a promise the proposed 

methodology will help to realize.

REFERENCES

Cambridge Systematics, Inc. 2011. National 

Bridge Investment Analysis System (NBIAS): 

Version 4.0 Technical Manual. Washington, DC: 

Federal Highway Administration.

Federal Highway Administration. n.d.a. “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-nextgeneration-pavement-performance-measures-assetmanagement, last accessed March 30, 2023. 

Federal Highway Administration. 2000. Highway Economic 

Requirements System. Technical Report. Washington, DC: 

Federal Highway Administration. https://rosap.ntl.bts.gov/

view/dot/58541, last accessed April 18, 2023.

Maggiore, M., K. M. Ford, High Street Consulting 

Group, and Burns and McDonnell. 2015. Guide to 

Cross-Asset Resource Allocation and the Impact 

on Transportation System Performance. National 

Cooperative Highway Research Program Report 806. 

Washington, DC: Transportation Research Board.

National Archives and Records Administration. 2022. 

Asset Management Plans. 23 CFR Part 515. https://

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

part-515, last accessed March 24, 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. 

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

and B. Allen. 2023. Next-Generation Pavement 

Performance Measures. Report No. FHWA-HRT-23-076. 

Washington, DC: Federal Highway Administration. 
8

Researchers—This TechBrief was developed by Paul Thompson (ORCID: 0000-0002-6559-7966), Prashant Ram 

(ORCID: 0000-0003-4229-998X), 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, 

Illinois 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 found at https://www.fhwa.dot.gov/pavement.

Key Words—Pavement management systems (PMSs), performance measures, asset management, leading 

indicators, transportation asset management methodologies, 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. 

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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 

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Quality Assurance Statement—FHWA provides high-quality information to serve Government, industry, and the 

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its programs and processes to ensure continuous quality improvement.

SEPTEMBER 2023 FHWA-HRT-23-075

HRDI-20/09-23(WEB)E

Recommended citation: Federal Highway Administration, 

Next-Generation Transportation Asset Management Methodology 

(Washington, DC: 2023) https://doi.org/10.21949/1521380

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.

Thompson, P. D. 2021. “StruPlan. Open-Source 

Long-range Renewal Planning for Transportation 

Structures” (webpage) http://struplan.com/, last accessed 

May 17, 2023.

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 1, 2023.

U.S. House of Representatives. 2022. National Goals and 

Performance Management Measures. 23 U.S.C. §150(b). 

https://uscode.house.gov/view.xhtml?req=

(title:23%20section:150%20edition:prelim)%20OR%20

(granuleid:USC-prelim-title23-section150)&f=treesort&

edition=prelim&num=0&jumpTo=true#substructurelocation_b, last accessed March 30, 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. 

23 U.S.C. §115 (2022): Advance Construction.

World Bank Group. 2023. “World Bank Road Software 

Tools” (web page). https://collaboration.worldbank.org/

content/sites/collaboration-for-development/en/groups/worldbank-road-software-tools.html, last accessed July 6, 2023.