Final_Report_3.pdf

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

Management Requirements

Final Report

Federal Highway Administration Office of Infrastructure

Research Development and Technology 6300 Georgetown Pike

McLean, VA 22101-2296

August 2016

Notice

This document is disseminated under the sponsorship of the U.S. Department of Transportation in the interest of information exchange. The U.S. Government assumes no liability for the use of the information contained in this document. This report does not constitute a standard, specification, or regulation.

The U.S. Government does not endorse products or manufacturers. Trademarks or manufacturers’ names appear in this report only because they are considered essential to the objective of the document.

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The Federal Highway Administration (FHWA) provides high-quality information to serve Government, industry, and the public in a manner that promotes public understanding. Standards and policies are used to ensure and maximize the quality, objectivity, utility, and integrity of its information. FHWA periodically reviews quality issues and adjusts its programs and processes to ensure continuous quality improvement.

Technical Report Documentation Page

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

4. Title and Subtitle 5. Report Date

Identification of Effective Next Generation Pavement Performance Measures and Asset Management Methodologies to Support MAP-21 Performance Management Requirements

August 2016

6. Performing Organization Code

8. Performing Organization Report No.

7. Author(s)

Kathryn A. Zimmerman, Brad W. Allen, Prashant V. Ram, Gregory M. Duncan, Omar Smadi, Kelly L. Smith, Kartik R. Manda, Basak A. Bektas

9. Performing Organization Name and Address 10. Work Unit No. (TRAIS)

Applied Pavement Technology, Inc.

115 W. Main Street, Suite 400 Urbana, IL 61801

11. Contract or Grant No.

DTFH6115C00045

13. Type of Report and Period Covered

12. Sponsoring Agency Name and Address Final Report (September 2015 – September

2016) Federal Highway Administration Office of Infrastructure Research Development and Technology 6300 Georgetown Pike McLean, VA 22101-2296

14. Sponsoring Agency Code

15. Supplementary Notes

COR: Nadarajah (Siva) Sivaneswaran

16. Abstract

The primary objectives of this project were two-fold: (1) identify or conceptually develop more strategic pavement performance measures, and their means of collection, in order to strengthen performance management and better link investments to long-term performance, and (2) identify or conceptually develop methodologies to enable full implementation of a comprehensive asset management plan, including trade-off analysis from a common ground among disparate assets that are traditionally individually assessed and managed. To accomplish these objectives, several tasks were performed consisting of a literature search and review of a variety of conventional and unconventional performance metrics, a feasibility assessment and prioritization of promising metrics, an investigation of methodologies for evaluating trade-offs when making investment decisions across asset classes, identification of the preferred methodology and development of a conceptual application of that methodology, and the development of work plans for further evaluating the recommended performance measures and cross-asset allocation methodology.

The study resulted in the development of a high-level process flow for the proposed cross-asset analysis framework. The key components of the proposed cross-asset analysis tool (CAAT) are described along with a simple conceptual example illustrating each step of the proposed framework.

17. Key Words 18. Distribution Statement

Pavement, asset management, strategic performance measure, condition metric, financial metric, life-cycle cost metric, cross-asset trade-off analysis, cross-asset allocation, multi-criteria decision making, multi-objective decision analysis

19. Security Classify. (of this report) 20. Security Classify. (of this page) 21. No of Pages 22. Price

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

August 31, 2016 Effective Next Generation Pavement Performance Measures ii

SI* (MODERN METRIC) CONVERSION FACTORS

APPROXIMATE CONVERSIONS TO SI UNITS

Symbol When You Know Multiply By To Find Symbol

LENGTH

in inches 25.4 millimeters mm ft feet 0.305 meters m yd yards 0.914 meters m mi miles 1.61 kilometers km

AREA

in2 square inches 645.2 square millimeters mm2 ft2 square feet 0.093 square meters m2 yd2 square yard 0.836 square meters m2 ac acres 0.405 hectares ha mi2 square miles 2.59 square kilometers km2

VOLUME

fl oz fluid ounces 29.57 milliliters mL gal gallons 3.785 liters L ft3 cubic feet 0.028 cubic meters m3 yd3 cubic yards 0.765 cubic meters m3

NOTE: volumes greater than 1000 L shall be shown in m3

MASS

oz ounces 28.35 grams g lb pounds 0.454 kilograms kg T short tons (2000 lb) 0.907 megagrams (or "metric ton") Mg (or "t")

TEMPERATURE (exact degrees) oF Fahrenheit 5 (F-32)/9 Celsius oC or (F-32)/1.8

ILLUMINATION

fc foot-candles 10.76 lux lx fl foot-Lamberts 3.426 candela/m2 cd/m2

FORCE and PRESSURE or STRESS lbf poundforce 4.45 newtons N lbf/in2 poundforce per square inch 6.89 kilopascals kPa

APPROXIMATE CONVERSIONS FROM SI UNITS

Symbol When You Know Multiply By To Find Symbol

LENGTH

mm millimeters 0.039 inches in m meters 3.28 feet ft m meters 1.09 yards yd km kilometers 0.621 miles mi

AREA

mm2 square millimeters 0.0016 square inches in2 m2 square meters 10.764 square feet ft2 m2 square meters 1.195 square yards yd2 ha hectares 2.47 acres ac km2 square kilometers 0.386 square miles mi2

VOLUME

mL milliliters 0.034 fluid ounces fl oz L liters 0.264 gallons gal m3 cubic meters 35.314 cubic feet ft3 m3 cubic meters 1.307 cubic yards yd3

MASS

g grams 0.035 ounces oz kg kilograms 2.202 pounds lb Mg (or "t") megagrams (or "metric ton") 1.103 short tons (2000 lb) T

TEMPERATURE (exact degrees) oC Celsius 1.8C+32 Fahrenheit oF

ILLUMINATION

lx lux 0.0929 foot-candles fc cd/m2 candela/m2 0.2919 foot-Lamberts fl

FORCE and PRESSURE or STRESS N newtons 0.225 poundforce lbf kPa kilopascals 0.145 poundforce per square inch lbf/in2

*SI is the symbol for th International System of Units. Appropriate rounding should be made to comply with Section 4 of ASTM E380. e (Revised March 2003)

Effective Next Generation Pavement Performance Measures August 31, 2016 iii

TABLE OF CONTENTS

CHAPTER 1. INTRODUCTION

Background and Problem Statement Research Objectives Research Approach

CHAPTER 2. NEXT GENERATION PERFORMANCE MEASURES

Introduction Literature Search and Review

Current State of the Practice Metrics Used in Other Industries

Preliminary Assessment of Performance Metrics USED in other industries Feasibility Assessment

Prioritization and Recommendations Next Generation Performance Measures

Condition-Based Indicators Financial Measures Life-Cycle Cost Measures

Risk and Uncertainty Moving Forward

CHAPTER 3. METHODOLOGY TO ENABLE FULL ASSET MANAGEMENT

IMPLEMENTATION

Introduction Investigation of Methodologies

Findings Proposed Framework for Cross-Asset Trade-Off Analysis

Background Limitations of Current Systems Terminology Measures Proposed Framework Overview Framework Components Process Flow of Proposed Cross-Asset Analysis Tool Application within Asset Management Practices Salient Features of the Proposed Framework

CHAPTER 4. FUTURE WORK PLANS

Work Plan to Validate the Task 1 Activities

Background Objectives Potential Benefits Relationship to the Existing Body of Knowledge Anticipated Work Tasks Estimated Time Needed to Complete the Research Anticipated Funding Requirements

Work Plan to Validate the Task 2 Activities Background iv

Objectives Potential Benefits Relationship to the Existing Body of Knowledge Anticipated Work Tasks Estimated Time Needed to Complete the Research Anticipated Funding Requirements

REFERENCES

APPENDIX A. ANNOTATED BIBLIOGRAPHY

v

LIST OF FIGURES

Figure 1-1. Delayed impact of ARRA investments by the Tennessee DOT (Graphic provided by

Tennessee DOT) Figure 2-1. Metrics required to enable a full asset management implementation Figure 2-2. Illustration of the concept of asset value deterioration Figure 2-3. Life extension factor for an example preservation treatment Figure 3-1. A simplistic illustration of a risk/reward-based allocation process (AASHTO 2015).

Figure 3-2. Overview of AssetManager NT and PT (NCHRP 2005) Figure 3-3. Basic steps of the AHP (AUSTROADS 2007) Figure 3-4. Decision hierarchy levels within the AHP (AUSTROADS 2007) Figure 3-5. Example application of the AHP (AUSTROADS 2007) Figure 3-6. Basic framework of the proposed methodology illustrating types of costs considered in the analysis (MRUTC 2007) Figure 3-7. Generalized framework for project evaluation (MRUTC 2007) Figure 3-8. Typical steps in an MCDM / MODA (Bai, Labi, and Li 2008) Figure 3-9. Framework for conducting trade-off analysis (Bai and Labi 2009) Figure 3-10. Allocation logic overview (NCHRP 2012) Figure 3-11. Resource allocation model and logic flow (NCHRP 2012) Figure 3-12. Conceptual framework of the FDM (Porras-Alvarado, Han, and Zhang 2015) Figure 3-13. Use of existing management system to develop a pool of candidate projects

(NCHRP 2015)

Figure 3-14. Cross-asset resource allocation framework (NCHRP 2015) Figure 3-15. Framework for the analysis Figure 3-16. Illustration of process flow for proposed cross-asset trade-off analysis framework.

Figure 3-17. Example performance models used by asset management systems Figure 3-18. Mock-up of an interactive dashboard to present the main outputs from the trade-off analysis Figure 3-19. Comparison of available funds to funds needed to achieve SOGR and optimal recommended program Figure 3-20. Comparison of available funds to need by funding source Figure 3-21. Comparison between performance levels and established targets Figure 3-22. Hypothetical comparison of AADT, funding and attainment of pavement goals between urban and rural areas vi

LIST OF TABLES

Table 1-1. Summary of proposed pavement performance measures (FHWA 2015) Table 2-1. Financial performance metrics identified Table 2-2. Performance metrics identified in the construction management and planning sector.

Table 2-3. Performance metrics identified in the transportation sector Table 2-4. Potential metrics to evaluate pavement performance Table 2-5. Summary of most feasible next generation metrics Table 3-1. A simple multi-criteria decision matrix (AASHTO 2015) Table 3-2. Framework for trade-off analysis (U.S. Army Corps 2002) Table 3-3. Types of trade-off analysis considered (Bai, Labi, and Li 2008) Table 3-4. Main characteristics of techniques used to support MCDM / MODA (Wu et al. 2012).

Table 3-5. Cross-asset optimization in State DOT asset management programs (from TRB

2008, Caltrans 2012, FHWA 2016b) Table 3-6. Implementation ideas from national and international transportation agencies and potential application in trade-off analysis and cross-asset allocation Table 3-7. Illustration of a multi-strategy analysis recommendations from a PMS Table 3-8. Example asset inventory and condition data, current state Table 3-9. Current average network conditions Table 3-10. Multi-strategy decision matrix example for pavements Table 3-11. Example of cost and impact of the ideal treatment strategy for Year 1 Table 3-12. Hypothetical utility function weightings Table 3-13. Impact of weighting on utility function results Table 3-14. Impact of weightings on priority rankings Table 3-15. Example reports

CHAPTER 1. INTRODUCTION

BACKGROUND AND PROBLEM STATEMENT

With the passage of highway legislation commonly known as the Moving Ahead for Progress in the 21st Century (MAP-21) Act in July 2012, and the subsequent legislation known as the Fixing America’s Surface Transportation (FAST) Act in December 2015, there is an increased emphasis on performance-based management of the nation’s highway system. This approach promotes the development and use of objective, performance-based data for the Federal-Aid Highway Program in the seven national goal areas listed below:

1. Safety – to achieve a significant reduction in traffic fatalities and serious injuries on all public roads.

2. Infrastructure Condition – to maintain the highway infrastructure asset system (pavements and bridges) in a state of good repair.

3. Congestion Reduction – to achieve a significant reduction in congestion on the National Highway System (NHS).

4. System Reliability – to improve the efficiency of the surface transportation system.

5. Freight Movement and Economic Vitality – to improve the national freight network, strengthen the ability of rural communities to access national and international trade markets, and support regional economic development.

6. Environmental Sustainability – to enhance the performance of the transportation system while protecting and enhancing the natural environment.

7. Reduced Project Delivery Delays – to reduce project costs, promote jobs and the economy, and expedite the movement of people and goods by accelerating project completion through eliminating delays in the project development and delivery process, including reducing regulatory burdens and improving agencies’ work practices.

In addition, MAP-21 requires that each State Department of Transportation (DOT) develop a risk-based asset management plan that establishes performance targets for pavement and bridge condition on the NHS, and includes a financial plan and investment strategies that will enable the DOT to achieve its performance targets within the period covered in the plan. The plan should provide sufficient documentation to enable the Federal Highway Administration (FHWA) to determine that the agency’s process meets the requirements established by the Secretary of Transportation.

While MAP-21 and FAST Act legislation establish the requirements for the Federal-Aid Highway Program, the rulemaking process provides transportation agencies with the FHWA’s interpretation of the legislation. The interpretation is published in terms of a Notice of Proposed Rule Making (NPRM) that provides for a 60- to 90-day comment period, during which time transportation agencies, practitioners, and other interested parties can examine and comment on the proposed rules. The FHWA is required to respond to the comments provided and prepare the final set of rules that will be enforced while MAP-21, FAST, and similar supportive legislation is in effect. The FHWA has been issuing the NPRMs under a staggered schedule, with the Safety, Infrastructure Condition, and Transportation Asset Management rules issued previously. The comment period for these rules is closed and the final set of rules are being established. NPRMs covering traffic congestion, safety, emissions, freight movement, and system reliability are in various stages of receiving comment, publishing, and implementation.

One of the objectives of the NPRM is to implement a common set of measures that will lead to improved consistency at the national level, enabling the FHWA to better track and forecast the impact of Federal-Aid Highway Program spending on the nation’s transportation system. At the State level, the legislation is expected to result in better investment decisions that consider risk, place a priority on preserving the existing system, and improve agency accountability and transparency. As a result, DOTs are focusing on using their limited resources and capability to shift their organizational cultures toward data-driven decisions that allow them to allocate available funding across disparate classes of assets to yield the highest overall system performance at the lowest, practical life-cycle cost. For pavement and bridge investments, the decision processes, the planned investments, and the expected performance are documented in the risk-based asset management plan and used to communicate transportation needs to stakeholders while also demonstrating that the agency is operating as a trustworthy steward of the highway system.

The development of the NPRMs for Infrastructure Condition and Transportation Asset Management has benefitted from the prior activities and momentum gained through the assemblage of Expert Task Groups (ETGs) and the work conducted by various Standing Committees and Subcommittees of the American Association of State Highway and Transportation Officials (AASHTO). For instance, the AASHTO Subcommittee on Performance Measures (SCOPM) formed a Task Force on Performance Measure Development, Coordination, and Reporting that recommended “the initial set of national-level performance measures should build upon existing performance measures, management practices, data sets and reporting processes (AASHTO 2013).” This recommendation was reflected in the NPRM for Infrastructure Condition. For instance, the four proposed measures to assess pavement condition are: (1) Percentage of pavements on the Interstate System in Good condition; (2) Percentage of pavements on the Interstate System in Poor condition; (3) Percentage of pavements on the NHS (excluding the Interstate System) in Good condition; and (4) a Percentage of pavements on the NHS (excluding the Interstate System) in Poor condition. The NPRM further defined Good, Fair, and Poor pavement conditions using the International Roughness Index (IRI), rut depth, faulting, and percent cracking, as shown in table 1-1.

Table 1-1. Summary of proposed pavement performance measures (FHWA 2015).

Condition Indicator

Performance Measure

Good Fair Poor

International Roughness Index (IRI) (in/mi) < 95

95 to 170 (non-urban areas) 95 to 220 (urban areas)

> 170 (non-urban areas) > 220 (urban areas)

Cracking (%) ≤ 5 5 to 10 > 10

Rutting (in) (asphalt only) < 0.20 0.20 to 0.40 > 0.40

Faulting (in) (concrete only) < 0.05 0.05 to 0.015 > 0.15

These metrics will be collected and reported for each 0.1-mi section of roadway in one travel lane for non-interstate NHS routes and in one travel lane for each direction on Interstate routes.

Based on the surface type (i.e., asphalt or concrete), each section will be evaluated for the three applicable metrics. If two or more metrics are rated as Poor, or if data is missing or invalid, the entire 0.1-mi section is categorized as Poor. If all three metrics are rated as Good, then the entire section is categorized as Good. All other combinations will result in the entire section being categorized as Fair.

While these metrics are relevant to reporting the overall performance of the NHS and they reflect measures that are currently available, the NPRM indicates that there is a need to identify and integrate “next-generation” measures that better align to enhanced long-term performance and investment decisions being made. These “next-generation” measures could also address some of the recognized short-comings to the proposed metrics. For instance, one of the disadvantages to the use of surface condition as a metric is that it is a “lag” indicator rather than a “leading” indicator. A lag indicator is often easy to measure but hard to use to achieve goals. The monitoring of a person’s weight is a good example of a lag indicator. By monitoring the scale regularly, a person can easily determine whether a target weight has been achieved. However, the measurement of weight alone does not provide any guidance about what to do if the target is not achieved. Two leading indicators, calorie intake and calories burned, are better indicators of the actions needed in order to achieve a weight target. Ideally, new leading performance indicators could be developed for pavements to help drive cost-effective investment decisions to help achieve performance targets.

Another disadvantage to some of the common lagging indicators is that they often take time to see significant changes in performance. This point can be illustrated using an example from the Tennessee DOT. During the American Recovery and Reinvestment Act (ARRA) of 2009, the DOT’s allocations for pavement preservation projects were increased by approximately $49 million; however, the trend lines produced by the pavement management system (PMS) reflected only a modest bump in performance which did not become apparent until 3 years after the DOT returned to baseline funding strategies, as shown in figure 1-1.

Figure 1-1. Delayed impact of ARRA investments by the Tennessee DOT

(Graphic provided by Tennessee DOT).

This time lag is recognized by individuals involved in the planning and programming activities at DOTs. As stated in the FHWA’s Performance-Based Planning and Programming Guidebook, “a time lag exists between the implementation of many transportation improvements and the resulting changes to performance indicators, making the connection between decision making and results unclear” (ICF International 2013). As these examples show, some project benefits are not realized immediately. Instead, they may be realized over time so their effect on system performance may not be directly apparent.

Current pavement performance metrics also fail to establish a clear link to investments that reduce the overall life-cycle cost of maintaining the highway system. For example, the IRI may not improve from the application of some pavement preservation treatments that could lower life-cycle costs. Additionally, pavement condition metrics reflect investments that have already been made and provide no direction to the agency for strategies that could be used to lower the whole life cost of preserving system conditions. Finally, the current metrics do not reflect any benefit to other performance metrics, such as safety or economic vitality.

In a recent National Cooperative Highway Research Program (NCHRP) project, a comparison was made between Highway Performance Monitoring System (HPMS) data submitted to the FHWA and pavement management data provided by representative DOTs. One of the study’s findings indicates that although there is general consistency in the HPMS and pavement management data, the data reported to HPMS are not reflective of the data used to recommend projects in the pavement management system (Zimmerman et.al. 2013). Therefore, even though consistent performance data are being reported to FHWA through HPMS, there is no assurance that the data are relevant to the investment decisions that are being made by the DOTs. The study provided several recommendations, including the following that are specifically relevant to this current research effort (Zimmerman et. al. 2013):

• Metrics other than IRI are needed to report pavement condition on a national basis.

• The new metrics that are used should more closely match the data currently collected by SHAs in support of their pavement management efforts.

• Inconsistencies in pavement management data collection activities should be addressed to improve the reliability of the reported data.

There have been a number of different types of performance metrics that have been considered to address these challenges. As reported in the NPRM, stakeholders considered the suitability of using the Remaining Service Life (RSL) approach as one of the pavement metrics. Those in favor of the use of RSL reported that it would provide validation to immediate actions and project programming; however, the connection to cost effectiveness and lowering the life-cycle cost is not tangible. The use of RSL is also problematic because of the variability associated with assigning a threshold value used to set the RSL at zero.

In Australia, there has been a strong focus on the use of financial metrics as one measure of system “health.” For instance, the Australians report the fair market value of transportation assets using depreciated replacement cost rather than historical costs as is more common in the United States (IPWEA 2010). Procedures have been developed that enable local agencies in Australia to track changes in the value of transportation assets over time, which has led to stakeholder recognition that the infrastructure is an asset with value that needs to be preserved. In addition, the Australians have developed financial metrics that provide an indication of whether an adequate level of investment is being made to offset the rate of asset depreciation. They also report future maintenance needs as an unfunded liability on their financial statements, which forces local agencies to recognize and acknowledge their commitment to preserving infrastructure assets into the future.

The use of effective performance measures and targets can have a profound impact on the effectiveness of an organization. Practitioners need leading measures when being held accountable for results so they better understand how their actions influence the results that are achieved. This can be illustrated using the weight example again. Most people find it very difficult to manage a diet using a lagging measure such as a weekly weigh-in. Results are better and feedback more advantageous when real time measures are being used, such as calories consumed in meals and burned during exercise. This is one of the reasons why fitness monitors that measure steps and other activities are becoming increasingly popular.

While current pavement performance measures are illustrative of current state of good repair, leading measures for pavements will help enable agencies to make better decisions and manage the highway assets more cost-effectively.

In addition to identifying and developing effective performance measures, agencies need to be able to use this data effectively to manage the highway infrastructure as a “system” rather than a network of individual assets. One of the challenges with this approach is the lack of tools that enable agencies to evaluate investment options across assets. As a result, most agencies continue to manage assets within individual silos, such as pavements and/or bridges.

A recent NCHRP project (08-91) attempted to address this challenge. As documented in NCHRP Report 806, the project resulted in the development of a framework for allocating funding by prioritizing distinct projects across disparate asset families. By compiling data relating to each potential project, and weighting the importance of the contribution of the project to identified goals of the agency, a relative benefit is captured for each project. By comparing unit-less benefit values, the researchers proposed that agencies can rank projects across different asset classes. While cost is a consideration in the framework, evaluating whether the treatment or project fits within the “lowest life-cycle cost” is assumed to have taken place in a linear process within the management system recommending the project.

A limitation to the NCHRP 08-91 framework is that it does not consider the viability of projects that do not fare well in the prioritization project. For example, if a seal coat project for a road segment were identified as the lowest life-cycle option but was not included in the funded program for the year, it is possible the project would no longer be a suitable candidate for preventive maintenance in the next year. Since the framework does not consider these types of consequences, an important factor in making investment decisions is ignored.

A Transportation Asset Management Plan (TAMP) provides a framework for presenting investment trade-offs and communicating to stakeholders that planned level of investment, the expected level of service, and the residual risks that the agency must manage. The development and implementation of TAMPs in the United States is new to DOTs but they have been used extensively outside of the United States. Although less than 10 agencies in the United States have published draft TAMPs to date, there is already mounting evidence that the Plans are driving change within DOTs. For instance, the Utah DOT used an asset management risk assessment to determine that available funding would be concentrated on addressing the needs of its high-volume facilities at the expense of the performance of the low-volume roads. The Ohio DOT is implementing a chip seal program on its low-volume roads in an effort to reduce the overall life-cycle cost of managing its highway system. Other DOTs, such as the New York State DOT, are using the TAMP to manage the rate of system deterioration caused by severe funding constraints. Initially, most DOTs have focused on pavements and bridges in their initial TAMPs, but some agencies are including other assets such as culverts, overhead sign structures, and tower lighting. As the benefits to developing a TAMP are realized, and agencies expand the number of assets included in their TAMPs, it is anticipated that there will be more interest in evaluating investment options across assets and incorporating a broader range of performance measures in the document. This will require the development of a methodology that enables DOTs to fully implement their TAMPs, focusing on investments that reduce life-cycle costs and evaluating trade-offs across assets as additional assets and performance metrics are incorporated into the TAMPs.

RESEARCH OBJECTIVES

In light of the information presented above, research is needed to explore strategic, yet practical, pavement performance measures to strengthen the FHWA’s performance management activities and link pavement investments to long-term performance improvements. Hence, the FHWA established the following objectives for this project:

• Identify, or conceptually develop, more strategic pavement performance measures, and their means of collection, in order to strengthen performance management and better link investments to long term performance. Consideration shall include how the data supporting the next generation method would be collected, analyzed, and presented.

• Identify, or conceptually develop, methodologies to enable full implementation of a comprehensive asset management plan, including trade-off analysis from a common ground among disparate assets that are traditionally individually assessed and managed.

RESEARCH APPROACH

To accomplish the project objectives, the project was organized into the following tasks and subtasks:

• Task 1: Identify or Conceptually Develop Strategic Pavement Performance Measures.

– Subtask 1A: Identify Potential Pavement Performance Measures.

– Subtask 1B: Investigate the Feasibility of the Potential Pavement Performance Measures.

– Subtask 1C: Prioritize and Summarize the Recommended Pavement Performance Measures.

• Task 2: Identify or Conceptually Develop Methodologies to Enable the Full Implementation of a Comprehensive TAMP.

– Subtask 2A: Investigate Methodologies to Explore.

– Subtask 2B: Describe the Application of the Methodology Within Asset Management.

– Subtask 2C: Conceptually Illustrate the Application of the Methodology on Pavements, Bridges, and Culverts.

• Task 3: Develop Work Plans for Future FHWA Projects to Validate Task 1 and Task 2 Outcomes.

• Task 4: Develop and Submit a Final Report.

To begin Task 1, the project team collected and reviewed a wide array of literature on performance measures used by transportation agencies and by organizations in other industries.

A total of 20 promising metrics were identified in the literature, of which 13 were deemed worthy of a more detailed and comprehensive evaluation.

Task 2 also began with a literature search and review. This effort focused on exploring cross-asset trade-off and cross-asset allocation methodologies being used in the transportation area, as well as in other relevant markets. Implementation ideas from various transportation agencies in the U.S. and around the world that could potentially be integrated into the trade-off analysis process were gathered and documented in a technical memorandum for FHWA review. Based on the findings of this effort and Task 1, a proposed methodology was developed that uses common, next-generation performance measures as the foundation of trade-off decisions and outcome reporting. The methodology was then described and conceptually illustrated in terms of its application with the pavements, bridges, and culverts asset areas.

During the work conducted during Tasks 1 and 2, it became readily apparent that there were gaps in available data and analysis tools to support the use of the most promising measures in transportation agencies. Additional work is needed to validate and test the use of the proposed performance measures and cross-asset methodology. Two work plans were developed during this task to address these needs. One of the work plans addresses the further development and validation of the performance measures suggested under Task 1. The second work plan outlines the steps needed to develop and test the cross-asset analysis methodology developed under Task 2.

CHAPTER 2. NEXT GENERATION PERFORMANCE MEASURES

INTRODUCTION

The first task under this project was to identify or conceptually develop strategic pavement performance measures that strengthen performance management and better link investment decisions to long-term performance. This task consisted of the three work activities listed below.

• Subtask 1A, Identify Potential Pavement Performance Measures. A thorough literature search and review of performance measures used by national and international transportation agencies and other industries was performed. A number of measures and metrics were identified that can either be used directly or their concepts can be used to develop similar performance measures to suit pavement performance monitoring.

• Subtask 1B, Investigate the Feasibility of the Potential Pavement Performance Measures.

Key features and parameters were identified to evaluate the feasibility of the metrics selected in Task 1A. The metrics were then ranked against these features.

• Subtask 1C, Prioritize and Summarize the Recommended Pavement Performance Measures. After a series of analysis iterations and discussions within the project team, the most promising metrics were prioritized and recommended for consideration based on the applicability of the metric to serve as a strategic leading indicator of pavement performance and its applicability to the methodology developed under Task 2.

This chapter describes the activities undertaken in task 1 and presents the findings and recommendations.

LITERATURE SEARCH AND REVIEW

Current State of the Practice A number of reports, syntheses, and summaries were investigated to identify the breadth of performance metrics currently used by transportation agencies in the United States. The literature indicates that a wide variety of pavement scoring techniques are used by departments of transportation (DOTs) to evaluate their pavements. For example, agencies commonly use the pavement condition rating (PCR), international roughness index (IRI), overall condition index (OCI), pavement condition index (PCI), and other similar condition rating metrics to provide highway agencies with an estimate of the current and historical condition of pavement assets.

Mathematical models and software tools are then used to project future conditions, from which maintenance and rehabilitation plans can be developed to meet pre-defined performance targets at the lowest practical life-cycle cost (Cambridge Systematics 2000; Cambridge Systematics, PB Consulting, and Texas Transportation Institute 2006; Papagiannakis et al. 2009; Amekudzi and Meyer 2011;, Baker et al. 2012). In 2013, the FHWA conducted a research study in an effort to develop next generation pavement performance measures, however, the study purely focused on existing pavement condition-based measures. The primary focus was how existing metrics such as IRI, rutting, and cracking could be improved to better reflect the overall functional condition of a pavement (Simpson et al. 2013).

The literature also revealed that most of the metrics used by highway agencies represent lag indicators of performance; they focus on the tangible results or outcomes of pavement condition.

While these metrics help prioritize pavement management activities, they are reactive in nature and their influence on outcomes is indirect. It is the opinion of this team that condition measures have been adequately developed for assets such as pavement and bridges and hence the literature review focused on other types of performance indicators, particularly indicators that help assess the financial health of an agency from a life-cycle perspective.

A number of other metrics that are less commonly used were also identified. Remaining service life (RSL) is a “lead” metric that is currently used by a few agencies. A number of limitations have been noted with this metric, such as the lack of a consistent definition across agencies, varied interpretations of the set definitions, varied interpretation of the term ‘Zero RSL’, and the use of a counter-intuitive negative value for RSL. Attempts are currently being made to standardize the process and overcome the limitations in this method (Elkins et al. 2013a). A similar management concept being proposed based on the RSL is the Remaining Service Interval (RSI), which aims to evaluate both functional and structural health of a pavement structure. RSI represents an alternative to discussing the “life” of a treatment because pavements and other assets are repairable systems with correctable component failures. The use of this concept and some implementation guidelines have been published by Elkins et al. 2013a; Elkins et al. 2013b;

and Thyagarajan, Sivaneswaran, Petros 2014; and FHWA 2016a).

Transportation agencies in Australia and other countries use a number of financial metrics which help them track agency performance and set future targets (Howard, Dixon and Comrie 2011;

MacDonald et al. 2004;, IPWEA 2010; Proctor, Varma, and Varnedoe 2012; and Parker 2012).

Measures such as the Asset Sustainability Index (which is computed as the ratio of the amount budgeted for infrastructure preservation divided by the amount needed to adequately sustain infrastructure condition at a targeted condition over the long-term) play an important performance-measurement role, particularly for capital-intensive organizations. Such metrics address the long-term performance of highway assets based upon expected expenditure levels.

The metrics encourage a long-term, asset-management-based approach to managing infrastructure, not just to meet condition targets today, but to sustain those targets into the future.

Although the asset sustainability indices and ratios are simple in concept, the metrics can be very informative and useful for long-range plans, short-term state transportation improvement programs or for public budgeting decisions particularly when tracked over time. These metrics boil down complex, long-term infrastructure condition and investment analysis into a suite of easy-to-illustrate metrics. Financial metrics can be compared across asset classes, because their units of measure are consistent.

An annotated bibliography used by the team to summarize related publications is included in Appendix A.

Metrics Used in Other Industries Performance metrics across a number of other industries were also reviewed. Several interesting metrics were identified that showed the potential for adaptation to a pavement performance metric. These metrics included the following:

• Manufacturing: Ratio of planned work to total work, asset utilization, cycle time, mean time between failures (Weber and Thomas 2005, Amrina and Yusof 2011).

• Health Care: Readmission rates, satisfaction with physician, backorder percentage (Mayer 2012, Brookings Institution 2014).

• Transit: Average miles between reported vehicle defects, backlog value, planned on-time shipment, planned resource utilization, percentage of slow zone mileage (FHU 2012, Trépanier, Morency, and Agard 2009).

• Construction: Cost predictability, percentage of equipment downtime, number of defects, ratio of value added to value invested, time to rectify defects (Cambridge Systematics 2000).

• Finance: Asset sustainability index, return on investment, benefit cost ratio (Howard, Dixon and Comrie 2011, MacDonald, et al. 2004, IPWEA 2010, Proctor, Varma, and Varnedoe 2012, and Parker 2012).

Performance metrics used by the manufacturing industry typically focus on equipment utilization efficiency. The health care sector uses metrics that focus on customer satisfaction. The transit industry has adopted metrics that primarily consider timely implementation and delivery of service. Similar to the manufacturing industry, the construction industry uses metrics that focus on minimizing defects. The financial sector’s metrics center around the profitability of investments made. It is believed that many of the above metrics can be adapted to be a meaningful measure of pavement performance for transportation agencies at a strategic level.

The most promising metrics are discussed in the following section.

PRELIMINARY ASSESSMENT OF PERFORMANCE METRICS USED IN OTHER

INDUSTRIES

During the course of the review, it was recognized that most metrics used were lag indicators that focused on past results rather than lead indicators that focused more on the process that yielded the results. Based on the results of the literature review, several metrics with the potential to serve as strategic leading indicators of pavement performance were identified. These include metrics that can be directly applied to practice, as well as conceptual metrics that would require development and implementation. Tables 2-1, 2-2, and 2-3 summarize the metrics of interest from the financial sector, construction planning and management, and the transportation sector.

The tables present a definition for each metric, describe how the metric is used within its sector, and indicate how the metric is used.

Table 2-1. Financial performance metrics identified.

Financial Performance Metric (as reported in literature)

Agencies/Organization/Industry Using this Metric

What does it mean and what can this metric be used for?

Operating Surplus Ratio (OSR):

An operating surplus (or deficit) arises when operating income exceeds (or is less than) operating expenses for a given period of time.

The OSR is the percentage by which major controllable income (may include grants, subsidies and other contributions) varies from operating expenses.

Transportation agencies (local and state) in Australia, New Zealand, Scotland, and England.

A positive value indicates the percentage of operating income available to fund other proposed expenditures.

A negative value indicates the percentage increase in total income required to achieve a breakeven operating result, or the backlog created during a period of time.

Table 2-1. Financial performance metrics identified (continued).

Financial Performance Metric (as reported in literature)

Agencies/Organization/Industry Using this Metric

What does it mean and what can this metric be used for?

The OSR is calculated by dividing the surplus (or deficit) by the income needed to fund operating expenses. (1)

Asset Sustainability Ratio (ASR): Ratio of asset replacement expenditure to depreciation for a given time period. (1)

Transportation agencies (local and state) in Australia, New Zealand, Scotland, and England.

The ASR helps agencies measure whether assets are being replaced at the rate they are wearing out. If the expenditure on renewing or replacing existing asses is at least equal to depreciation over time, then the agency is ensuring that asset value is sustained.

Asset Consumption Ratio (ACR): Ratio of depreciated replacement cost of an asset to the current replacement value (CRV). (1)

Transportation agencies (local and state) in Australia, New Zealand, Scotland, and England.

The ACR communicates the average proportion of “as new” condition left in an asset. It shows the depreciated replacement cost of the asset relative to its “as new” value.

The ACR seeks to highlight the aged condition of the asset.

Asset Renewal Funding Ratio (ARFR):

Ratio of net present value [NPV] of asset renewal funding (projected outlays) over a longer time period relative to NPV of projected expenditures for the same period.(1)

Transportation agencies (local and state) in Australia, New Zealand, Scotland, and England.

Conveys the agency’s ability to fund its future investment needs and therefore continue to provide existing levels-of-service without additional operating income or reductions in operating expenses.

Asset Sustainability Index (ASI): Ratio of amount budgeted for infrastructure preservation divided by the amount needed to adequately sustain infrastructure condition at a targeted condition over the long-term. (2)

Potential use of this metric for transportation agencies in the US has been demonstrated by FHWA (2012) and is based on practices adopted by local agencies in Great Britain and Australia.

The ASI is intended to be a long-term planning metric that helps in communicating the optimum amount of investment needed to sustain conditions at a targeted level.

Recapitalization Rate (RCR): Ratio of expected service life (based on performance trends observed through field data) to funded service life (service life expected). (3)

Department of Defense (DoD) The RCR measures the impact of the funding received on the expected service life of the asset. Ratios of less than 1 indicate that funding levels are inadequate.

Facilities Revitalization Rate (FRR): Ratio of the current replacement value of the facility to the annual facility revitalization funding.

(3)

National Aeronautics and Space Administration (NASA)

The FRR is an indication of how often a facility is completely revitalized. The FRR accounts for repairs and upgrades needed due to obsolescence, modernization, aging materials, and new requirements.

Deferred Maintenance Reduction (DMR):

Amount of backlog/deferred maintenance reduced annually. (3)

National Nuclear Security Administration (NNSA)

Measures the dollar amount of backlog eliminated annually.

Facilities Operating CRV Index (FOCI):

Ratio of annual operating expenditures to the CRV of the asset. (3)

Federal Facilities Council Measures the annual operation and maintenance expenditures as a function of the current replacement value of the facility/asset.

Facility Condition Index (FCI): Ratio of backlog or deferred maintenance cost to the

CRV. (3)

Federal Facilities Council The FCI helps measure the current condition of the facilities to assess the amount of work, if any, is recommended to maintain or improve conditions to acceptable levels.

Return on Investment (ROI): Ratio of benefit to initial investments made. (4)

Popular metric in accounting, investment banking, stock market.

Helps in evaluating the efficiency of an investment or to compare the efficiency of a number of different investment options.

Equivalent Uniform Annual Cost (EUAC):

The NPV of all discounted costs and benefits

Widely used in life-cycle cost analysis (LCCA).

EUAC is the cost per year of owning and operating an asset over its entire lifespan or

Financial Performance Metric (as reported in literature)

Agencies/Organization/Industry Using this Metric

What does it mean and what can this metric be used for?

for an alternative as if they were to occur uniformly throughout the analysis period. (5) assumed analysis period. EUAC is particularly useful when budgets are established on an annual basis.

Benefit Cost Ratio (BCR): A ratio representing the relationship between the cost and benefits of a proposed project. (6)

BCR is a popular metric in the financial sector.

The BCR is used to detail the relationship between potential benefits and costs, both qualitative and quantitative, of undertaking new projects or replacing old ones. When multiple choices are available, the BCR helps in making the decisions that give the best “bang for the buck”.

Sources:

(1) Australian Infrastructure Financial Management Guidelines (2010)

(2) FHWA (2012)

(3) Federal Facilities Council (2005)

(4) Various industries such as accounting, investment banking, stock market etc.

(5) Walls and Smith (1998)

(6) Investopedia

Table 2-2. Performance metrics identified in the construction management and planning sector.

Construction Management or Planning Performance Metric

(as reported in literature) Agencies/ Organization/ Industry

Using this Metric What does it mean and what can this metric be used for?

Work Requests (WR): The percentage of work requests remaining in “request” status for less than 5 days, over a specified time period (for example the last 30 days). (1)

Metric is used by maintenance divisions in the manufacturing industry.

Helps monitor the response rate for requests and helps improve it. When monitored closely, it helps in minimizing catastrophic failures and unplanned down time.

Quality of Planning (QP): The percentage of work orders with man-hour estimates within 10 percent of actual expenditure over the specified time period. (1)

Metric is commonly used in industries involved in maintenance and construction activities and in medium-or large-scale industries that track budgets and expenditures.

Can be used to evaluate the efficiency of estimation within an agency.

Quality of Work Execution (QWE): The percentage of activities that need to be rescheduled due to improper practices or poor quality of work execution. (1) (2)

Metric is used by maintenance divisions in many large scale manufacturing industries.

Helps track quality of workmanship.

If the metric exceeds a predefined permissible value then it indicates training program failures or poor quality measures.

Sources:

(1) Key Performance Indicators, Measuring and Managing the Function (2005)

(2) Manufacturing KPIs, Spider strategies website https://www.fhwa.dot.gov/infrastructure/asstmgmt/013017.pdf http://www.investopedia.com/terms/b/bcr.asp

Table 2-3. Performance metrics identified in the transportation sector.

Traffic and Sustainability Performance Metric (as reported in literature)

Agencies/ Organization/ Industry Using this Metric

What does it mean and what can this metric be used for?

Customer Benefit Percentage…