The Roadmap was developed based on stakeholder input obtained during three regional workshops. This section of the report documents the process followed to identify the needs that are included in the final document.
From the beginning, the FHWA intended the development of the Roadmap to be a collaborative process, involving representatives from each of the various stakeholder groups that either use pavement management data, support the use of pavement management concepts, or provide technical assistance or training to current or future pavement management practitioners.
Representatives from several stakeholder groups were invited to participate in one of three regional workshops held in Phoenix, Arizona; Dallas, Texas; and McLean, Virginia. The stakeholder groups that were invited to participate and the targeted number of representatives from each stakeholder group at each workshop are listed below:
A total of 87 participants accepted invitations to participate and were able to attend one of the three workshops. A complete listing of the attendees is provided in Appendix A of this report. In addition to FHWA, the participants represented thirty-one SHAs, seven other government agencies (i.e., cities, counties, and Canadian government agencies), thirteen private contractors, and six academic agencies. A Technical Panel consisting of pavement management practitioners from FHWA, SHAs, and academia provided technical guidance throughout the development of the Roadmap. Each workshop included breakout groups that provided an opportunity for the participants to exchange information on a peer-to-peer basis and to collaborate on the identification of research and development needs in this area.
The primary objective of the workshops was to identify research and workforce development needs within ten pre-established focus areas. These focus areas were identified through a literature search and represented topics that have been identified as subjects important to the pavement management community. The ten focus areas selected for discussion during the workshops included:
Figure 1 illustrates where each of the ten focus areas fit into the overall pavement management process.
Figure 1. Relationship between Focus Areas and Common Pavement Management Activities.
The participants at each workshop were responsible for identifying gaps in each of the focus areas and for developing both long-term and short-term research, development, and technology transfer needs to address these gaps. The needs statements were documented using a form that records the proposed project objective, description, cost, and duration. A total of 242 needs statements were produced during the three regional workshops. After eliminating duplications and combining statements with similar recommendations, a final list of forty-two needs statements was produced, with a heavy emphasis on short-term needs. Therefore, the Technical Panel was reconvened to identify additional long-term needs statements. The meeting resulted in five additional needs statements, which brought the total number to forty-seven. Each of the needs statements is included in the final Pavement Management Roadmap and is documented in full in Appendix B of this report.
During the process of combining and re-arranging needs statements, it became evident that many of the needs statements impacted several of the ten focus areas. As a result, it no longer seemed practical to tie the final needs statements to the original focus area topics. Instead, the resulting needs were organized and grouped into one of the following four themes that emerged from the process:
The final list of needs was presented to the workshop participants during a webconference, which introduced the four theme areas and the balloting process that would be used to rank the needs in terms of importance. Using an online balloting tool, individuals who had attended the workshops were invited to vote on the relative importance of each of the research needs. In addition, participants were asked to rank both the short-term and long-term needs within each theme area. A process was developed for combining the relative importance of each need statement with the rankings assigned by the participants, which resulted in the final prioritized listing of short-term and long-term needs included in the Pavement Management Roadmap. In addition, by having ranked the needs statements within each theme area separately, the highest priorities within each theme, as well as their overall importance across themes, could be produced. The final Pavement Management Roadmap is included in the next chapter of this document.
Although the final Pavement Management Roadmap is organized by themes, the stakeholder discussions during the three regional workshops identified gaps within each of the original focus areas. A summary of the state of the practice, the challenges that agencies are facing, and the needs identified during the workshops, is included in this section of the report.
Data collection techniques, equipment, and emerging needs comprise one focus area that has received considerable attention over the last several decades. This emphasis is due to the need to be able to rapidly collect and accurately characterize pavement condition data, which serves as the basis for reliably predicting network pavement conditions, recommending rehabilitation or preservation treatment types and timing, and supporting the increasing needs of asset management. In the last several years, there have been a number of advancements in the technology used to collect data (e.g., changes in sensors and image quality) that have impacted the types of pavement condition data that can be collected rapidly and the consistency in data from one year to the next. However, most agencies continue to rely on rutting, ride, and surface condition to support their pavement management activities.
One area of data collection that has seen considerable advancements is automated pavement condition assessment. Due to technological advancements, the assessment of pavement condition has transformed from laborious manual procedures to high-speed automated or semi-automated surveys that combine the use of sensors and digital images. Although the use of high-speed equipment has improved measurement accuracy, shortened the required time for data collection, and improved the safety of the rating crew, there is little consistency among states in how the data are collected and processed. The costs associated with the use of this technology are high, making data collection one of the most expensive aspects of pavement management. Therefore, several agencies are attempting to combine the data collection activities of several divisions to reduce redundancy and to maximize the benefit from those expenditures.
Changes in data collection equipment technology and vendors create a unique challenge to pavement management practitioners due to compatibility issues with historical data. In the absence of equations that correlate data from different data collection devices and vendors, pavement management engineers must determine whether historical data can be used to develop deterioration trends or whether the differences in the data prevent the historical data from being used.
On the positive side, changes in technology have provided an opportunity to collect data that could not easily be collected in the past. For example, recent advancements with Ground Penetrating Radar (GPR) have assisted SHAs in estimating pavement layer thicknesses and in conducting forensic studies. Another example of how technology has changed the type of data that can be collected is reflected in the use of moving deflection equipment to characterize pavement structural condition at the network level. Both of these examples illustrate new information that can be used to improve pavement management recommendations but that also have the potential to support the agency's pavement rehabilitation and design activities. This type of information will be increasingly important due to the data input requirements associated with the new Mechanistic-Empirical Pavement Design Guide (MEPDG) developed through the NCHRP and the reassessment of the Highway Performance Monitoring System (HPMS) reporting requirements used by the FHWA to communicate with Congress. There are other emerging needs that are influencing pavement management data collection activities, such as the increased use of preventive maintenance treatments and the focus on sustainable pavements. Therefore, it is appropriate to question whether the information needed to support the development of pavement management recommendations is adequately addressed through the procedures and techniques being used today. For instance, pavement management practitioners will have to determine the cost-benefit of being able to record fine, hairline cracks using high resolution cameras. Additionally, pavement management practitioners should consider whether information, such as oxidation or raveling, that is needed to trigger preventive maintenance treatments should be incorporated into their pavement management data collection efforts.
The last several years have also included initiatives that have attempted to standardize the collection and processing of pavement condition information. The American Association of State Highway and Transportation Officials (AASHTO) worked with the FHWA on the development of provisional protocols for collecting faulting, rutting, roughness, and cracking data on pavements, but there has not been universal agreement or acceptance of these protocols. The lack of standard methods for collecting pavement condition information places a burden on automated equipment vendors but provides state highway agencies with ultimate flexibility in how they collect and process the data. However, the lack of consistency in data collection and processing efforts makes it difficult to compare performance across agencies. Therefore, it may be important to discuss this issue as an industry to determine whether efforts to standardize pavement data collection activities are warranted and, if so, to identify how to move this issue forward in a meaningful way. Critics of past efforts at standardization indicate that agencies typically try to meet too many needs with the standards and end up failing to meet any of the original objectives. As a result, the standards are not meaningful to anyone and become more of a burden than a help.
There have been a significant number of research efforts in this focus area over the years. A summary of some of the subject areas that have been published in the last 5 years are listed below:
A number of challenges exist in the area of data collection, equipment, and emerging technologies. One of the biggest challenges, quality control/quality assurance activities, is so important that it has been established as its own focus area (further discussed in Focus Area 2). Therefore, the challenges listed here concentrate on other issues. For instance, pavement management data collection efforts require significant resources on a regular basis. Many SHAs are questioning these outlays and restricting the funding provided for equipment purchases or survey personnel. As a result, pavement managers are forced to evaluate the level of data accuracy needed, the amount of data needed to support pavement management, and the most cost effective method of collecting the information. Coupled with the emerging demands for pavement-related data associated with the HPMS reassessment and the implementation of the new MEPDG software, the pressures associated with data collection activities will likely increase in the next several years.
Another challenge involves the coordination of data collection activities (such as traffic, materials, maintenance, planning, and budget) within a given SHA. Successfully coordinating these efforts requires a common referencing system, similar data collection schedules, and compatible efforts for efficiently processing the data. These coordination efforts are rarely institutionalized; instead they rely on the efforts of a few individuals to make them happen. Therefore, there is a substantial risk that the coordinated data collection efforts could end if one or more of the key individuals moves into a new position or leaves the agency.
The amount of time required to process data for statewide networks is also a challenge in some agencies, especially if manual activities are involved in the process. In some states, the length of time between delivery of the condition data and the development of project and treatment recommendations is so short that there is not sufficient time to perform quality checks on the data. As a result, credibility issues may arise if errors are later found in the data.
Data consistency and compatibility issues continue to be challenges facing many SHAs. There are significant issues associated with preserving historical pavement condition data through transitions in data collection methodology (e.g., changes from manual to automated methods), changes in equipment, and changes in vendors. For the most part, little work has been done in this area to evaluate the impact of these changes on pavement management recommendations.
Participants in the workshops for this focus area recognized that with the advancements in data technology, the type of equipment used for data collection, and the consistency in data collection and processing activities varied significantly across the United States. Therefore, many of the problem statements addressed the lack of awareness regarding the state of the practice by recommending the development of calibration centers, the identification of best practices for data collection and reporting, and the development of pavement distress standards. Several problem statements also addressed issues that have been under development for a number of years, but still were in need of additional investigation. These topics included the development of a fully automated condition data processing tool and quantifying the benefits of network-level structural deflection testing. Finally, workshop participants noted a need to better quantify surface-related distresses that are typically difficult for a visual pavement condition system to quantify, such as raveling, oxidation, friction, splash/spray, and noise.
The three regional workshops produced a total of twenty-eight research needs, which were later combined into a total of seven problem statements.
Pavement management is a data-driven activity. Therefore, the reasonableness and reliability of the pavement management recommendations are directly linked to the quality of the data being used for decisions. There are a number of considerations that go into the determination of data quality, including completeness, correctness, validity, consistency, timeliness, and accuracy. Responsibility for verifying data quality is typically shared by both the data collection contractor and the agency. However, if the agency is responsible for collecting the data, all responsibility for data quality resides within the agency.
Managing data quality typically includes activities such as calibrating data collection equipment or inspection teams prior to the start of the surveys, reinspecting representative segments during the data collection process, and verifying the reasonableness and completeness of the data upon delivery. For pavement management data collection activities, calibration activities typically include collecting data on control sites where a baseline condition is established by the agency. For some types of equipment, such as falling weight deflectometers, calibration may be performed by a regional calibration center. During the production period when surveys are conducted, many agencies perform periodic "checks" on the data by re-inspecting a representative number of sites or by checking results at blind control sites (sites unknown to the data collection vendor). Once the surveys are completed, acceptance testing is often performed by the agency before inputting the data into the pavement management system. Acceptance testing typically checks for obvious errors or inconsistencies in the data and verification of the ratings on a representative sample of the data.
In an effort to promote consistency in data collection activities, a number of data collection guidelines and procedures have been developed. These include, but are not limited to, the following:
The method used to collect pavement condition information has a significant impact on data quality. Data collected using sensors (e.g., roughness and rutting) are typically considered to have less variability than manual distress surveys. Some agencies are able to perform manual pavement condition surveys with very little variability because of the consistency in raters from year to year. Other agencies use automated crack detection programs as a first cut at classifying distress information and then verify the information using semi-automated processes that allow an inspector to view the digital images at a workstation. The variability associated with each of these procedures has been difficult to quantify and compare.
In the last several years, there has been an increased focus on the development of methodologies and tools to promote data quality. For instance, the Oklahoma Department of Transportation developed a computerized tool to perform quality assurance checks on the processed pavement condition data provided by their contractor. The tool automates some of the basic checks on completeness, reasonableness, and consistency and flags sections that might need to be evaluated in more detail. Other SHAs, like the Virginia Departments of Transportation (VDOT) and the Maryland State Highway Administration (MDSHA), have focused on developing formal quality control/quality assurance plans (QC/QA). The process used by VDOT includes an independent outside assessment to validate and verify the data provided by its data collection vendor. MDSHA owns and operates its data collection vehicle but has developed QC/QA plans to ensure data quality. The QC plan includes steps to verify the data, to search for abnormalities, and to check that the data has been saved. The process also includes a subjective assessment of the crack detection process. As part of its QA process, an independent auditor verifies that the QC process was completed and further checks a representative sample of the data. If discrepancies are noted, the data are reviewed to determine whether the problems were caused by systematic errors or whether reprocessing is required.
To help tailor the data collection practices to the uses of the data, the World Bank has introduced the concept of Information Quality Levels for road management. This approach recognizes that there are five distinct levels of data used by transportation agencies ranging from very detailed data used for research to more general data used for reporting key performance measures (such as smooth roads). Each of the various levels of data requires different degrees of data sophistication and data quality. As a result, the expected use of the data has a significant impact on the data quality requirements. Thus, as the use of pavement management data expands to include new applications (e.g., to calibrate the MEPDG performance models), the adequacy of existing QC/QA procedures may need to be evaluated.
One of the important factors impacting data quality is the turnover of personnel within SHAs. Constant turnover of the individuals responsible for performing pavement condition surveys or verifying the quality of data received from a contractor require that ongoing training programs are in place to help ensure consistency in the data from one year to the next.
There have been a significant number of research efforts in this focus area over the years. A summary of some of the subject areas that have been published in the last 5 years are listed below:
As pavement managers review, maintain, and update the various data sources that are needed for pavement management, the required level of data quality should be established for each data element. As part of this activity, agencies will have to strike a balance between low-cost efforts that produce large amounts of low-quality data and more costly efforts that produce less data but provide a higher level of data quality. It often falls to the pavement manager to determine what data are needed and what level of quality is adequate for generating pavement management recommendations within existing resource constraints. Unfortunately, there is little information available in the literature to guide these decisions and to quantify the impact of data variability on pavement management decisions.
Resource constraints also have a significant influence on work in this area. Few agencies have the technical expertise to be able to develop QC/QA plans. As a result, they rely on the data collection vendors to have QC plans in place and do very little to verify the accuracy of the data provided. This issue is complicated further by the frequent changes in technology and the added variability in the data caused by equipment changes.
Participants in this focus area identified a number of needs in the areas of data quality and data variability. Specifically, participants noted the need to quantify the uncertainty and risk associated with variability in data collection, budget allocation, and model prediction. Several of the participants suggested linking data quality to the different types of business decisions, implying that more accurate information is needed for more critical decisions. Others suggested the development of guidelines for referencing pavement data geospatially. In addition, participants noted an overwhelming need for the identification and presentation of best practices for improving data quality in terms of collection, processing, and reporting.
Participants identified a total of twenty-four needs, which were combined into two problem statements.
With advancements in data collection practices and equipment (e.g., digital images for pavement condition surveys, transverse and longitudinal profiles, GPR, and moving deflection) comes a significant increase in data storage needs. Within the last decade, pavement management systems have required servers capable of storing terabytes of data, and storage needs will continue to increase with the development and implementation of new technologies. Not only must SHAs deal with how to store these data (e.g., multiple platforms, multiple servers, off-site backup, and potential purging of raw files), but agencies must also address how to manage, update, enhance, and share the information with other divisions within the agency.
Integration and sharing of data among agency divisions can be problematic, especially if a common referencing system is not used. Typically, the pavement management system is comprised of data obtained from various divisions within an SHA (e.g., traffic, materials, construction, and planning). The ability to associate all data with a given roadway location is critical to the performance, accuracy, enhancement, and continued use of the pavement management system. For example, having construction test results, such as density and asphalt content for hot-mix asphalt (HMA) pavements, could contribute to the improved prediction of early failures or help ensure improved pavement performance on a specific roadway segment.
With the development and potential implementation of the MEDPG, the ability to store, link, and retrieve the large magnitude of input and generated pavement design data would fit well within a pavement management system. Data already contained within a pavement management system, such as traffic data, pavement performance data, and existing layer thickness, are needed for calibration, verification, and operation of the MEPDG. As part of the AASHTO DARWin-ME solicitation, AASHTO is making a number of modifications to the software, one of which is establishing an input library database, which could easily be integrated with an SHA pavement management system. If these databases were integrated, SHAs could evaluate the performance of different pavement designs based on differences in materials, climate, traffic, and other design inputs.
Data integration issues are increasingly important in SHAs as agencies move away from independent "silos" for managing information towards a more integrated asset management approach. The ability to share information allows agencies to better coordinate their decisions, reduce data collection and management costs, and improve the accuracy and timeliness of information. There are a number of different approaches that can be taken to integrate data, including strategies that rely on a centralized database (e.g., a data warehouse) or strategies that use integrated databases that hide the complexity and distribution of the underlying databases. The organizational structure of the agency, the reliance on legacy systems, and the level of resources available to address data integration issues all influence the approach selected by the agency.
An agency's data integration activities influence the format in which pavement management data must be reported. As new data become available, such as GPR or structural information, pavement managers must address formatting issues to maximize the use of the information by the agency.
The application of geographic information systems (GIS) and geospatial technologies to support asset management decision making is reported to be a primary interest area among SHAs. However, the development of these spatial products on an enterprise basis continues to be a challenge for agencies. Therefore, a peer exchange on this subject will be conducted in 2010 to identify the challenges that hinder progress and to propose practical solutions for SHAs. The California, Washington, and Virginia Departments of Transportation have reportedly made substantial progress in this area.
A summary of some of the subject areas that have been published in the last 5 years are listed below:
As discussed previously, a number of challenges exist in the area of data storage and integration. The pavement manager is faced with determining how data will be stored, how best to share the data with other divisions within an agency, how to obtain needed data from other divisions, and how to maximize the usage of pavement management information within the agency. The challenges include both organizational and technical issues that must be addressed.
Organizationally, the agency structure (i.e., centralized or decentralized) influences the approach that may be used to integrate data. Information technology divisions can also have a significant influence on how easily new programs and new technology can be implemented within the agency.
There are also technical challenges that pavement managers must address. For instance, automated data collection images require extensive amounts of storage. As a result, agencies must decide how much of the data to keep for historical purposes. And, as emerging technology becomes available for pavement management's use, strategies must be developed for integrating that data into the existing systems.
The use of pavement management for calibrating the MEPDG software also poses a challenge for pavement managers because new data inputs must be managed. Agencies will have to decide whether those inputs will be incorporated into a pavement management database or whether there will be new, integrated databases created to link to pavement management performance data.
Similar considerations must be made for integrating preventive maintenance treatments into a pavement management system. Without integrated maintenance data, it is difficult for pavement managers to determine the performance of preventive maintenance treatments or to evaluate where excessive maintenance expenditures have been made.
Participants in this focus area addressed issues related to best practices for data storage with respect to capacity, organization, security, cost, and purging of historical data. In addition, a number of needs were identified in the area of data mining, focused specifically on how to use and leverage pavement management data for nontraditional purposes (e.g., asset value, new design procedures, impact of improved materials, or construction practices). The participants also identified research into strategies for addressing institutional issues associated with data management, including purchasing policies and controls.
Participants identified a total of eighteen needs, which were combined into three problem statements.
Performance modeling is one of the primary functions of a pavement management system. Pavement deterioration results from the complex interaction between such things as traffic, climate, materials, layer thickness, layer type, and construction practices. Performance models are developed to take these factors into account and predict pavement condition over time, which in turn can be used to predict overall network level conditions, identify treatment needs, select appropriate timings for different treatments, identify funding levels needed to achieve performance targets, and demonstrate the consequences associated with different investment strategies.
There are four broad categories of pavement performance models: deterministic, probabilistic, expert or knowledge-based, and biologically inspired models. The way a model will be used influences the selection of model type. The most common approaches used for network-level pavement management include deterministic and probabilistic models. The most recent research has focused on the use of biologically inspired models that include the use of genetic algorithms and/or artificial neural networks. The use of genetic algorithms results in models developed through an iterative process that mimics evolution. For example, a model is developed for a set of data. Based on the fit of the data, a new population is created from the original population by reproducing, crossing over, or mutating the original data. This evolution continues until an acceptable model is developed. Models developed using artificial neural networks are slightly different in that the models continue to evolve and improve through a computerized process. To date, these models have primarily been used by researchers, but there is potential application for use by the pavement management community.
The information used to develop performance models vary by agency. In general, state highway agencies use the family modeling approach in which pavement sections are grouped by characteristics such as pavement type, structural composition, geographic location, and traffic level or functional classification. Rates of deterioration are determined for each pavement family and the models are typically used to predict pavement condition indexes for indicators such as ride, structural condition, and functional condition; however, there are exceptions to these generalizations. For instance, the Minnesota Department of Transportation models individual distress progression to calculate future surface ratings. Other agencies, such as the Washington State and Colorado Departments of Transportation, develop individual performance models for each pavement section as long as there are three to five data points that show a reasonable deterioration trend. Where sufficient data points are not available, family models or default models are used.
Much of the attention in this area in recent years involves the use of pavement management data to calibrate and validate the performance models in the new MEPDG software. The use of pavement management data for the calibration of these models has prompted research into the availability of the necessary design inputs in pavement management and the development of strategies to address capabilities that do not currently exist. It has also prompted discussions about the eventual use of the MEPDG performance models and whether they will replace network-level pavement performance models in the pavement management system or whether both types of models will exist in the future.
A related discussion is taking place at the national level where research has led to the development of simplified MEPDG models that have been incorporated into an analysis tool that allows the FHWA to report pavement needs to Congress. The new models have prompted changes to the data requirements needed to support the Highway Performance Monitoring System (HPMS) that will be initiated in 2010.
The FHWA has announced the release of a new tool that can be used to determine the health of a pavement network using a Remaining Service Life (RSL) concept. The primary input to the tool is HPMS 2010 data, but pavement management data can also reportedly be used to support the analysis. Pavement health is evaluated in terms of pavement life, ride, or distress under various environmental and administrative conditions. The tool was demonstrated at the 8th National Conference on Transportation Asset Management in Portland, Oregon in October 2009.
Another initiative that is influencing the development of pavement performance models is the consideration of climate change, green initiatives, and sustainability in transportation agencies. Pavement managers of the future will likely have to consider how these factors can be taken into consideration in developing and refining pavement performance models. There are other industry changes that impact predicted pavement conditions, including increased truck weights and changes in construction materials and pavement design. It is not clear how well these types of initiatives have been considered in pavement management performance models in the past and what expectations there might be for the future.
There have been a significant number of research efforts in this focus area over the years. A summary of some of the subject areas that have been published in the last 5 years are listed below:
Pavement performance is influenced by many different factors, some of which are difficult to model. As a result, it is difficult to develop pavement performance models that predict future conditions with a high degree of certainty. It is especially difficult to develop models that reflect the ongoing changes that impact pavement design and construction. For example, this challenge has been demonstrated recently with the impact of Superpave mix design (and the use of modified binders) on the performance of HMA pavements. Many states have over 20 years of performance with Superpave mixes, but few agencies can report with certainty the effect these mixes have had on pavement performance. Similarly, the use of alternative materials and processes (e.g., crumb rubber asphalt, warm mix, two-lift concrete, and hot and cold in-place recycling) may also impact the accuracy of performance prediction. Moreover, the introduction of new materials means that long-term performance data are simply not available, which makes it difficult to forecast the future performance of those materials.
Many states are faced with addressing tire-pavement noise issues, which can have a significant societal impact, but certainly has not been incorporated in existing performance prediction models. Other factors include the impacts of climate change, use of sustainable materials, and modifications to truck weights (a current truck lobby is requesting an increase in maximum weight for trucks on interstate highways from 80,000 pounds to 97,000 pounds). The degree to which these factors should be considered in pavement management performance models and the development of a process to do so (while maintaining consistency in the measures reported to upper management and elected officials each year) certainly presents a challenge to pavement managers. Agencies in Canada are reportedly evaluating risks associated with climate change and the potential impact on transportation needs.
Another challenge facing pavement managers is addressing the question of how the performance prediction models contained within the MEPDG should or will interact with the existing pavement management performance models. It is unclear whether the MEPDG models will replace network-level family models in the future, or whether there is a need to maintain these models separately since both types of models serve different functions.
Even without these types of influences, pavement managers continue to face the challenge of developing reliable performance models using the information currently available. A significant dilemma for many agencies is trying to determine how to improve the reliability of these models in the absence of good information on pavement structure and treatment history. Other agencies acknowledge that pavement management models are "good enough" at the network level, even though they may not be sufficiently accurate at the project level. However, it is worth considering whether this viewpoint will withstand the test of time as computer capabilities evolve and better data become available through improved integration efforts.
Lastly, pavement managers must determine the influence of maintenance as performance models are developed. Some agencies assume that the "do nothing" models incorporate some level of maintenance to help ensure that the expected design life is achieved. However, if pavement management systems will be used to identify candidates for preventive maintenance, the performance of these treatments must be differentiated in order to calculate a benefit associated with its use. This need poses a challenge to pavement managers when preventive maintenance treatments can be used in both a preventive manner and as a stop-gap treatment to keep a highway section operational until funds become available for more substantial repairs.
Participants in this focus area identified a strong need to establish best practices for modeling pavement performance. At least fourteen needs statements addressed topics related to performance modeling, spanning topics such as the level of detail needed to produce accurate and reliable models; strategies for updating models to reflect changes in material properties, construction, or design practices; and procedures for evaluating the sensitivity of inputs on model prediction. Another area that the participants identified as a need was the use of pavement management information for national reporting, specifically related to HPMS. Finally, participants noted the need to investigate areas related to the use of a performance measure related to structural condition, a need to more accurately design and predict performance on low volume roadways, and a need for a tool that more easily quantifies performance impacts due to increases in legal load limits.
Participants identified a total of thirty-two needs, which were combined into seven problem statements.
The use of pavement management to recommend pavement treatments can vary from state to state. Some agencies determine treatment timing within the pavement management system but determine the treatment type independently; some apply a simplistic list of treatments (e.g., preventive maintenance, functional improvement, and structural improvement); others have developed elaborate decision trees that base treatment type and selection on factors such as pavement condition at time of treatment, prior treatment, pavement type and structure, functional class, and traffic volume. In addition, treatment type and selection can be based on other nonpavement related factors such as safety, noise, combining adjacent projects for economy of scale, delaying projects due to future planned projects, and public pressure.
The process for treatment selection within the pavement management community continues to evolve, as some SHAs are moving in a direction to obtain more data to support the treatment selection process, while others are generalizing the recommendations provided by pavement management so the districts and regions have more influence in the final treatment selection using locally known factors and considerations. The agencies that are looking to enhance their treatment recommendations are considering data not previously available as part of the pavement management process, such as surface texture characteristics, noise, road safety, and structural condition. These additions acknowledge the fact that pavement distress information alone is not sufficient to accurately project current and future pavement needs.
At the same time, some agencies are moving in an opposite direction, modifying their pavement management analysis to be more general about the types of recommendations being made. For instance, one SHA recently moved towards recommendations regarding the level of treatment required rather than attempt to determine the specific type of treatment required; the final treatment selection decision is made by the Regions. In this particular SHA, Regional Pavement Management Engineers have copies of the pavement management software to help with the treatment selection process, but few reportedly have a strong degree of confidence in operating the software and instead rely on reports from the central office or their own field inspection results.
A factor influencing the reasonableness of the costs associated with pavement management recommendations concerns the number of add-ons required for a project. A pavement management analysis typically calculates only the cost of the pavement improvement, although the cost of some projects can escalate considerably when roadway hardware improvements, American Disability Act enhancements, and safety issues are addressed. The degree to which these costs are considered in a pavement management system varies among SHAs.
Most of the recent efforts in this area are concerned with the incorporation of preventive maintenance treatments into the analysis. The degree to which preventive maintenance treatments can be recommended by pavement management is largely dependent on the availability of the types of information that trigger these treatments and the ability to model performance so that benefits are calculated.
One of the challenges faced by pavement management practitioners is determining when and how to incorporate new treatments into the analysis process or how to update the decision factors used on existing treatments. Many times, this process requires collecting performance, material, and construction data for the development of performance models, but it also requires a process for tracking where these treatments have been used. Since this information is often the responsibility of someone outside pavement management, it is difficult to obtain this type of information on a consistent basis.
There is also a challenge associated with developing the models needed for treatment selection, especially when a treatment has not been used extensively in the agency. Determining how to quantify differences in treatment performance can be difficult and often requires that treatment performance be monitored over time. However, as the industry discovered from the use of Superpave mixes, the location of specific project sections where changes in mix design have been used are not frequently known by pavement management.
Similar types of challenges exist in trying to develop rules for triggering preventive maintenance treatments. Since pavement management was originally developed to identify and prioritize rehabilitation and reconstruction activities, most pavement condition survey procedures focus on capturing substantial amounts of pavement deterioration. In many instances, the information needed to trigger preventive maintenance treatments effectively is not being collected on a network-wide basis so it cannot easily be incorporated into the treatment rules.
It is also difficult for agencies to accurately estimate the costs associated with treatments because of rapidly changing prices and the scope changes that can occur before construction starts. This demands that pavement management evaluate the number and types of project add-ons that can be incorporated into the treatment selection process. For example, some agencies identify shoulder and lane width into their pavement management system so they can determine whether current standards are met before recommending an overlay in the pavement management system. However, not all pavement management systems have been designed to evaluate these needs.
Workshop participants identified the need to summarize best practices for evaluating the decision factors used in the treatment selection process, including both pavement preservation and rehabilitation. In relation to budgeting, the participants felt that having a best practice document that provided a survey of state procedures for allocating funds based on pavement management data would be beneficial. Other suggestions were provided for minimizing project delays associated with the contracting process and improving the breadth of factors considered in developing project and treatment recommendations. The latter needs statements were focused primarily on improving the match between pavement management recommendations and funded improvement programs. According to the workshop participants, this meant expanding pavement management to consider nontraditional factors such as congestion, sustainability, user costs, and other emerging issues.
A total of twenty-four needs were identified and combined into two problem statements.
Pavement management systems include some type of optimization tool that facilitates the prioritization of current and future needs to make the best use of available funds. Most agencies currently use some form of single- or multi-year prioritization in which feasible treatment options are ranked based on criteria, such as benefit-to-cost ratio or cost-effectiveness. In single-year prioritization, the needs for each year are considered independently, while multi-year prioritization considers the needs in each of the analysis years in unison.
The recommendations from the pavement management system typically serve as the starting point for developing the improvement program. The recommendations from pavement management are used in varying degrees by others involved in the project and treatment selection process. In some agencies, pavement management provides pavement condition information as a reference, while other agencies require that a certain percentage of the final program must match the recommendations from pavement management. The latter approach is used to help ensure that funding is spent wisely especially in a decentralized organization. However, matching criteria are difficult to develop and enforce. As a result, some agencies report that their criteria are so general that they are essentially useless. Nevertheless, as agencies move towards more decentralized decision making, these types of initiatives may become increasingly important.
There are several factors that are part of the project selection process that are not currently considered by pavement management. For example, a number of agencies, especially those in Canada, are beginning to incorporate risk into their decision processes. In particular, they are considering the likelihood of failure occurring and the associated consequence should it occur. Therefore, a higher priority is placed on those projects that demonstrate a high probability for failure and would have a large negative consequence on the agency if failure occurred. In addition, agencies are evaluating how to take into consideration the contribution of pavement projects on initiatives such as safety, sustainability, and climate change. The anticipated impact of decisions on the users of the highway facilities is utilized largely around the world, but is not a significant part of the project selection process in the United States. There is little information in the literature on strategies that incorporate these factors.
Pavement management is not limited to influencing project selection decisions. In some agencies, it provides the basis for developing long-term strategies that are incorporated into an agency's strategic plan. Other agencies have adopted integrated maintenance management and pavement management software to better link maintenance and operations decisions with capital improvement decisions. Pavement management is an integral part of asset management, because it represents one of the largest agency investments in the transportation infrastructure.
Pavement management provides valuable information to support the development of performance targets and the investment levels required to achieve agency goals. However, this assumes that pavement management is capturing the benefits associated with the investments in the pavement network and that those benefits relate to the performance metrics being reported.
This is not always the case. For example, many agencies have performance targets that identify the percent of the roads in good condition based on ride or pavement distress. This works adequately for treatments such as overlays that have a positive impact on these metrics. However, it may not work as well for preventive maintenance treatments that have little impact on traditional performance metrics such as ride (e.g., crack sealing and chip seals). As a result, it is difficult to quantify the benefits associated with these treatments and to defend the investments being made in these programs. Intuitively, agency engineers know the treatments make sense, but unless the benefits can be quantified and measured, it is difficult to demonstrate that investments in pavement preservation are effective.
Pavement management can also be used for the allocation of funding to address agency needs. Most commonly, agencies use the pavement management system to determine needs on a statewide basis and on a district (or regional) basis. The ratio of the district needs to the total needs becomes one of the primary considerations in allocating funds across the state. However, since this approach presents a financial incentive to have a large percentage of needs, it tends to support districts that may not be practicing sound pavement management practices.
A summary of some of the subject areas that have been published in the last 5 years are listed below:
Ultimately, pavement management provides agencies with recommendations for using available funding as effectively as possible. Project recommendations are most often based on maximizing the benefit/cost or cost effectiveness of a program for a given funding level.
However, in real life there is not always a direct correlation between the recommendations from pavement management and the construction projects that are funded. There are many factors influencing the final selection of projects (e.g., political, economic, and technical issues) that indirectly influence the degree of credibility and acceptance of pavement management within an agency. Organizational factors and the degree to which an agency is decentralized also influence the final project selection process.
Therefore, one of the challenges for pavement management is to strike a balance between the development of reasonable project recommendations that influence the construction program and the data requirements needed to support this level of sophistication. Agencies must determine who has responsibility for the final treatment decision and what information should be used to influence its selection. For instance, agencies will have to decide if they want pavement management information to be a primary driver in the decision process or whether it should play a supporting role by providing condition (and other) information to decision makers. Additionally, agencies must decide what factors will be considered in the pavement management analysis and which factors are appropriate to evaluate outside of the pavement management analysis. Because of the differences in the way SHAs operate, no one solution will satisfy all agencies.
An underlying decision in this process is determining where project and treatment decisions will be made within an organization. If the decisions will be made in the districts or regions, then it is more difficult to achieve a statewide performance target. However, if the decisions are heavily influenced by the central office, the districts and regions often fight the recommendations. Moreover, differences in the types of treatments being recommended (i.e., capital projects or maintenance projects) may influence the strategy selected. Ultimately, if project and treatment decisions are being made in the field offices, agencies should determine whether pavement management tools will be required to support these decisions or whether some type of matching criteria are needed to help meet agency-wide goals and targets.
For pavement management to be most beneficial to support asset management decisions, it is important that the analysis results quantify the benefits associated with the options being considered. Agencies have found it to be difficult to adequately represent the benefits associated with the use of preventive maintenance treatments because the typical types of performance measures that are being reported are not substantially influenced by these treatments. For example, if an agency reports pavement conditions in terms of ride, then investments in chip seals and crack sealing will show little to no benefit (and may actually cause a rougher ride). As agencies are being held more accountable for the way funding is being used, investments in treatments that do not show a benefit may be restricted. This shift may require changes to the types of performance metrics being used in the future, although ride is popular because it is an end-user response and is relatively easy to collect.
Agencies will also be faced with challenges related to improving their allocation of funding across the states. Traditionally, funding allocation decisions have been based on needs, which may penalize districts that effectively use pavement preservation techniques. This type of challenge may be difficult to overcome if the allocation formulas are legislated.
One of the biggest challenges that agencies face is building and maintaining support for pavement management. Unless pavement management is fully integrated into the decision process, some executives may consider pavement management to be a resource that is susceptible to funding cuts. Pavement management concepts are not generally well known at the executive levels so there is a continuous need to promote the concepts and educate decision makers in this area. This effort is time consuming for pavement management practitioners, and most engineers are not comfortable in this environment. And, to date, efforts have not been very effective in communicating in a way that resonates with decision makers.
As in the previous focus area, the participants in the workshops identified a number of research and development areas to support this effort. Suggested topic areas included expanding the factors currently considered in pavement management to include safety, congestion, and environmental factors; user costs; and other emerging issues. A key emphasis of these efforts includes addressing institutional issues that commonly prevent the use of this information, such as organizational structure, industry pressures, risk considerations, or legal issues. Several needs statements also addressed the consideration of risk, variability, and uncertainty in pavement management data and their impact on pavement management recommendations. There were also a number of suggested needs statements oriented towards strengthening the link between pavement management inputs and performance targets.
Many of the problem statements in this area supported the need for increased emphasis on developing the skills of pavement management practitioners and increasing buy-in among internal and external stakeholders. The topics focused on communication, buy-in, and training were common to most of the focus areas discussed at the workshops.
A total of five needs statements were produced from the twenty-four problem statements identified during the workshop.
Pavement management capabilities have evolved significantly since the concepts were first introduced nearly 45 years ago. A large part of the evolution is associated with the advancements that have taken place in terms of computer technology and data collection equipment. Other changes relate to modifications in the materials, treatments, and construction practices being used. This latter set of changes has more of an impact on the types of models and recommendations being used in pavement management than the practice itself.
The data requirements for pavement management are changing as a result of these and other changes. For example, in 2010 new requirements will be in place for reporting HPMS data to the FHWA. Furthermore, as discussed elsewhere in this document, the new MEPDG software is based on a large number of inputs and requires calibration of the performance models to reflect agency performance, and agencies will be examining ways to employ pavement management data to calibrate the procedure for their pavements.
At the same time, agencies are increasingly using performance measurement as a way to monitor progress and to establish agency goals. These metrics influence the allocation of funding and are frequently used to establish agency priorities. Therefore, pavement management systems must collect and report performance data that supports the analysis of these metrics.
The types of analyses being performed using pavement management tools are also changing. While traditional pavement management systems consider pavement improvements on a section-by-section basis, several SHAs with heavily congested urban areas are demanding that entire highway corridors be analyzed and managed.
Changes in contracting procedures have also influenced the data required of pavement management. Specifically, the increased use of public-private-partnerships and performance-based specifications have forced agencies to develop means of collecting defensible information that can be used to define payouts, including incentive and disincentive clauses.
There have been a number of research efforts in this focus area over the years. A summary of some of the subject areas that have been published in the last 5 years are listed below:
There are a number of challenges that will impact the degree to which pavement management can respond to the changes discussed previously. One of the largest challenges relates to the availability of the data and expertise needed to support these initiatives. This problem is especially true as agencies downsize and the remaining staff are spread increasingly thin in a number of different program areas. Developing a skilled workforce that has the technical expertise required to adapt to these changes will be increasingly difficult.
Another challenge involves national efforts to increase consistency in the way pavement condition information is measured and reported. Several significant efforts are moving in the direction of using consistent measures (e.g., MEPDG and HPMS), but it is unclear whether SHAs will modify their historical data collection methods, supplement their existing approaches with information to satisfy the new initiatives, or ignore the requests to report data in a certain way. What is clear is that until there is more consistency in data collection procedures, it will continue to be difficult to meaningfully report pavement conditions on a national basis. Whether this has been part of our communication problem has yet to be debated.
SHAs are also challenged by their ability to use traditional pavement management software to analyze different funding scenarios quickly and efficiently. In many instances, agency management needs the results of a "what if" scenario in a very short period of time, which is often difficult to accomplish depending on the complexity of the pavement management system. Being able to quickly provide meaningful results may require different tools than those currently being used for project and treatment selection activities. In addition, it is difficult for some agencies to isolate the needs of highway corridors because of the way most existing pavement management systems analyze sections independently.
Participants in the workshops identified research and development needs that would enable pavement management to be more responsive to the new technologies that have emerged in recent years. For example, suggested needs involved the development of a general process for incorporating emerging technology into a pavement management analysis. More specifically, several of the groups suggested that research efforts focus on supporting innovative contracting, automated condition data processing, pavement design, and data mining (to better leverage the use of pavement management data). Additional needs focused on improving pavement condition data quality and reporting.
In addition, workshop participants suggested that studies be conducted on quantifying the benefits associated with pavement research and quantifying the costs and benefits associated with "pay per use" strategies. The first of these two initiatives provides a mechanism for documenting the on-going benefits associated with research activities. The second recognizes the changes that are expected to take place in transportation funding and prepares pavement management practitioners for these adjustments. In addition, several of the participants suggested that a synthesis study be conducted to evaluate how transportation agencies have successfully responded to the changing environment in the past so that effective strategies can be identified for use by others. Finally, participants recommended research into the use of new technology, such as social networking, to communicate with practitioners. A total of nine research needs were developed from the original list of thirty-two.
Pavement management is an expensive and labor-intensive proposition. Agencies support pavement management efforts through investments in personnel, software, and on-going data collection activities. There are annual costs associated with the maintenance of software licenses and data collection efforts. Agencies that own their own data collection equipment must upgrade on a regular basis and calibrate the equipment regularly. There are also on-going training requirements as pavement management staff change and as new technology becomes available.
While it may be possible to capture most of the costs associated with pavement management, it is much more difficult to quantify the benefits associated with these programs. For the most part, pavement management has promoted subjective benefits such as improved decision making, better use of available funds, and improved communication. At least two agencies, including the Arizona Department of Transportation and the Alberta Ministry of Transportation, have conducted studies to quantify the benefits associated with pavement management by attempting to document the cost effectiveness of the programs. Both studies quantified benefit in terms of the improved conditions associated with the use of pavement management software and compared the benefits to the costs of software, data collection, and personnel. These studies provide the foundation for quantifying the benefits associated with pavement management, but more rigorous approaches are needed to convince decision makers of the benefits to pavement management. Alternative approaches that demonstrate the return on the investments made in pavement management show promise.
Some of the benefits that an agency may realize from pavement management extend beyond improved pavement conditions. For example, some agencies have been able to demonstrate improved surface texture characteristics that have reduced the number of wet-weather crashes or reduced noise levels leading to improved customer satisfaction. However, these types of benefits are not easily quantified on a network-wide basis as part of a pavement management analysis. They are typically investigated outside of pavement management by other divisions.
Perhaps the greatest challenge in this area is quantifying the benefits associated with pavement management. At least two researchers have initiated efforts in this area by quantifying the dollars saved through the use of improvement programs that are more effective than the traditional "worst first" strategies that place the highest priority on pavements in poor condition. However, researchers have found it difficult to quantify the costs and benefits associated with pavement management and the metrics used have not resonated with executives and other decision makers. As a result, the industry has relied on promoting subjective and anecdotal evidence of benefits. Developing strategies to address these shortcomings may be beneficial to the industry.
Closely related to the inability to estimate benefits is the difficulty in quantifying the benefits associated with increased investments in pavement management. For example, if $300k is currently being spent on data collection activities, what would an additional $100k investment provide? Would it lead to better quality data? Would it allow the agency to verify the quality of the data provided by a vendor? Would it lead to a reduced risk to the agency? What economic benefit does an increase in network conditions provide? The answers to these types of questions are not well understood, making it difficult to defend budget recommendations or changes in technology.
Another challenge facing pavement management is the lack of a catch phrase that emphasizes its benefits. For example, the pavement preservation community has relied on its slogan "right road, right treatment, right time" to communicate its objective. There is no such phrase for pavement management to quickly communicate its purpose (although the pavement preservation slogan would certainly fit).
Due to the absence of clear processes for calculating the benefits associated with pavement management, the workshop participants identified needs statements to develop methods to quantify the benefits of pavement management and the information it provides to various stakeholders. In a related area, participants suggested that research investigate the impact of different investment levels in pavement management on the quality of the recommendations provided. This information was considered to be important for promoting continued financial investment in pavement management. Once processes are developed to quantify benefits, participants suggested the development of strategies to promote pavement management as a decision support tool using public relations campaigns and other approaches.
The remaining needs statements focused on the availability of information to support pavement management. For instance, participants suggested a clearinghouse for better access to available resources, pavement management courses in civil engineering curriculums in colleges and universities, and independent technical assessments to help agencies enhance existing capabilities.
The original twenty problem statements in this focus area were condensed into five.
In recent years there has been tremendous momentum for the increased use of preventive maintenance treatments as an important component of a cost-effective pavement preservation program. However, there is little quantifiable evidence of the benefits of preventive maintenance treatments because most pavement management systems are unable to adequately quantify the benefits using existing metrics. As a result, most agencies rely on anecdotal evidence that pavement preservation makes sense and benefits an agency in terms of reduced life-cycle costs, improved pavement performance, and improved safety characteristics. Efforts to demonstrate the benefits associated with these programs have proven to be difficult, in part because the performance metrics used to report network health (such as ride) are not always improved through the use of preventive maintenance treatments. Therefore, some agencies have identified the need to develop and implement new types of performance metrics that better capture the benefits provided by strong pavement preservation programs that include the use of preventive maintenance treatments.
Another complicating factor that hinders efforts to quantify the benefits of effective pavement management is the separation of pavement management and pavement preservation within the agency. In many SHAs, pavement management and pavement preservation efforts are separate initiatives performed by different divisions. Some agencies have attempted to bridge these divides by creating a Pavement Preservation Engineer position that is based in the Maintenance and Operations Division with collateral duties that include coordinating with the Pavement Management Unit. In agencies with strong pavement preservation programs, this is a popular model to follow.
The industry has also supported the separation of pavement management and pavement preservation activities through separate conferences, separate teams within FHWA, and separate committees within the Transportation Research Board structure. However, because of travel restrictions, budget constraints, and increased efforts to streamline organizational activities, agencies are beginning to question whether it continues to make sense to keep these activities separate. These agencies see pavement preservation as little more than an effective pavement management strategy. Therefore, the need for separate programs is difficult to support.
At the same time, the FHWA is initiating efforts to increase the profile of pavement management by emphasizing "the management of pavements" more than the use of a computerized software program. This places more of an emphasis on the role of pavement management within the organization to support decision making and less of an emphasis on the data collection activities that many associate with pavement management. The long-term effect that this shift will have on pavement management, and the ultimate success of changing pavement management's profile, is unknown at this time.
At the same time, there are some inherent issues with existing pavement management process that may be limiting an agency's ability to demonstrate the benefits of preventive maintenance treatments. For instance, not all pavement condition surveys adequately address the types of deterioration that trigger the need for preventive maintenance treatments. Few agencies are initiating efforts to change their data collection procedures to better identify preventive maintenance triggers.
The use of preventive maintenance treatments is not currently considered in the MEPDG software, although some research has been conducted to develop a framework for doing so. The absence of these treatments in the design software has led some researchers to question whether the use of preventive maintenance treatments are assumed as part of the original design life, or whether they should be considered in the same manner as other treatments (i.e., overlays) as extensions to the original design life.
As previously discussed, it has proven difficult to quantify the benefits associated with the use of preventive maintenance treatments as part of a pavement preservation program. This is due, in part, to the lack of performance data on preventive maintenance treatments. Because of the types of pavement condition data normally collected during network level surveys, the benefits associated with preventive maintenance treatments, such as surface sealing to prevent moisture infiltration or oxidation, are not currently captured or quantified. As a result, it is difficult to defend the continued expenditure of funds on preventive maintenance programs.
Another challenge facing the pavement management community is the continued distinction between pavement preservation and pavement management. These programs are often represented separately by industry, and they are managed by different divisions within a SHA. There is increasing support for promoting pavement preservation as an effective pavement management strategy, but that may be difficult to adopt in agencies where these programs are considered to be separate and distinct. As long as there continues to be some confusion in the differences between pavement management and pavement preservation, both programs will struggle with establishing their identities and building support within the industry.
Ultimately, pavement management practitioners must determine the role of preventive maintenance activities in the management of pavements. For instance, is the use of planned maintenance activities a requirement for pavements to reach their design life, or are these treatments applied to extend pavement life beyond the original design period? The answer to this question may have a significant influence on how preventive maintenance treatments are considered in a pavement management system.
Participants in this focus area recognized that some work has been done in an attempt to integrate preventive maintenance treatments into a pavement management system, but suggested that additional activities be conducted to further the consideration of early-intervention treatments in a pavement management program. Specific suggestions related to the quantification of costs and benefits associated with preventive maintenance treatments, the identification of appropriate intervention levels, and the development of guidelines for agencies seeking assistance in this area.
In addition to the development of general guidance, workshop participants identified the need to determine the impact of preventive maintenance treatments on pavement performance to help identify the optimal timing for these treatments. Additional needs statements addressed strategies for communicating the benefits of pavement preservation, supporting pavement preservation funding levels, and developing effective performance measures to support pavement preservation.
The three regional workshops produced sixteen needs statements, which were later combined into two topics.
As technology, construction practices, and organizational policies and programs change, pavement management must continue to evolve to reflect the impact of these changes on project and treatment recommendations and priorities. Without the ability to adapt to these changing influences, pavement management will not survive.
Focus Area 7 concentrated on the changing needs and emerging technology that will influence pavement management in the future. In this focus area, the institutional issues and national initiatives expected to influence pavement management were addressed. This provided an opportunity for workshop participants to identify other types of emerging trends that may influence pavement management 5 to 10 years into the future.
For example, a number of agencies have placed an increased emphasis on the use of asset management principles to guide investment allocation decisions and to establish performance targets. Exactly what role pavement management will have in supporting investment allocation decisions is not known, but it is clear that pavements and bridges lead other assets in terms of the data available to support the agency's asset management efforts. Some agencies have created separate asset management divisions, which are responsible for the data collection activities needed to acquire inventory and condition information. Other agencies have not changed the organizational structure but created executive committees that combine the recommendations from each asset class to determine a final program. The movement towards the increased use of performance measures and asset management principles is expected to place more of an emphasis on pavement management results in the future. However, it is not clear whether pavement projects will compete favorably with other projects in an asset management environment.
Over time there has also been an increased focus on being able to compare performance from one agency to another as part of benchmarking activities. Most recently, there has been a great deal of interest in the performance metrics being used by SHAs to determine whether there are common measures that should be reported. For instance, NCHRP Report 632 documents a framework for identifying common performance indicators for managing interstate pavements. This has increased the demand for more consistency in data measurement and reporting and exposed the difficulty in getting states to agree on common metrics that may result in compatibility issues with historical data. However, the new HPMS reassessment requirements may cause some states to move in the direction of changing the way some distress information is reported.
Although the technology associated with pavement management has improved tremendously in recent years, there is little evidence that the recommendations are being increasingly utilized. Instead, many agencies continue to rely on political influences and regional pressures as the primary driver of the construction program. The challenge for pavement management practitioners is developing a strategy that makes better use of technology to defend project and treatment recommendations. For example, in criminal cases the legal industry was able to make a monumental shift in the way trials are conducted by introducing DNA evidence. Can a similar shift take place in pavement management using new technology or analytical procedures?
The changes in the availability of funding have also significantly influenced pavement management over the years. While transportation has been funded at inadequate levels for many years, industry organizations are strategizing about new methods of paying for infrastructure improvements. Suggestions for toll roads, increased privatization of portions of the road system, changes to the gas tax structure, and increased funding in the highway bill have all been discussed and debated. States are increasingly finding it difficult to come up with state matches for federal funds and, as a result, have placed more of an emphasis on maintaining the existing infrastructure rather than investing in expansion efforts. Whether these trends will continue and for how long is not known. What is known is that the transportation network will continue to deteriorate if increases in funding and more flexibility in how funding can be used are not provided.
The environment in which pavement management operates has also seen significant changes in the past 20 to 30 years. Many transportation agencies have experienced downsizing, which has resulted in the significant loss of institutional knowledge. Organizational silos still exist, but there is an increasing amount of interaction between divisions and more data sharing than in years past. This change is largely due to the decreasing availability of funds and the increased pressure to eliminate duplication and to consolidate activities where practical. In many organizations, this sharing of information is not the result of organizational changes to foster improved communication and interaction, but typically results from the initiative of a few key champions.
There are several challenges that must be recognized during the discussion of this focus area topic. For instance, one challenge concerns the participants' ability to forecast future trends accurately in an environment that is heavily influenced by political factors that impact funding, policy, and national initiatives. Another challenge involves developing a strategy that positions pavement management in a way that allows pavement management practitioners to adapt to changes as they occur.
The organizational and institutional changes will also demand that the civil engineer of the future have a broader range of skills than in the past, so workforce development activities must also be identified to address those needs. The decreased availability of funding has impacted agencies' ability to provide workforce development, so nontraditional methods of acquiring these skills will have to be created.
It is also obvious that transportation agencies have not been effective in communicating the need for increased funding to reduce the risk associated with deteriorating pavement conditions. Unless transportation officials are able to find a forum for effectively communicating their needs, it is likely that the existing funding situation will not change. This will place more of a burden on pavement management practitioners to use the available information for an increasing number of purposes.
The regional workshops produced many needs statements intended to evaluate the impact of organizational structure, funding allocations, and earmarks on pavement management recommendations. Others suggested the need to help develop guidelines that would build support for pavement management among agency leaders and field personnel.
Participants in this area also recognized the need for better access to shared resources for pavement management practitioners. Therefore, suggestions for a national pavement management partnership were offered as one way to provide this knowledge. Other participants suggested constant funding to support pavement management activities and research into pavement management's role in an asset management environment.
A total of twenty-four research and development topics were suggested in this focus area, which were later combined into five needs statements.
As discussed earlier, duplications within the 242 needs statements developed through the three regional workshops were eliminated, and similar topics were combined to reduce the final number of research, development, and technology transfer recommendations to forty-seven. This total includes twenty-three short-term needs (to be conducted within the next 5 years) and twenty-four long-term needs (that should be addressed in the next 6 to 10 years). The needs statements were later organized by theme, which had facilitated the combination of needs statements that had been suggested under multiple focus areas. The four theme areas included in the Pavement Management Roadmap are summarized below:
The final list of needs statements that are included in the Pavement Management Roadmap are presented in tables 1 through 4. Each table lists the short-term and long-term needs identified in each theme area, as well as a summary of the focus area and regional workshop at which the idea emerged.
|Title||Contributing Problem Statement Title||Originating Regional Workshop||Originating Focus Area|
|Establish & Develop Equipment Calibration Centers and Guidelines||Calibration Centers or IRI, Rut, and Fault Measurements||Phoenix||1|
|Establish & Develop Equipment Calibration Centers and Guidelines||Reference Calibration for Profile, Noise, Texture, GPR||McLean||1|
|Establish & Develop Equipment Calibration Centers and Guidelines||Calibration and Development of Standards||Phoenix||2|
|Establish & Develop Equipment Calibration Centers and Guidelines||QA Process||Phoenix||2|
|Pavement Management Clearinghouse||Develop a Continuous Catalog of Data Collection Technology||Phoenix||1|
|Pavement Management Clearinghouse||Develop a National Pavement Management Resource Center||Phoenix||8|
|Pavement Management Clearinghouse||Develop Knowledge Sharing Tools||McLean||8|
|Pavement Management Clearinghouse||Formalize a Pavement Management Partnership to Advance the State of the Practice||McLean||10|
|Pavement Management Clearinghouse||Data Collection User's Group/Peer Exchange||McLean||2|
|Pavement Management Clearinghouse||Establish Contractor Clearinghouse||Dallas||5|
|Development of Pavement Distress Standards||Best Practices for Standardization of AASHTO Protocols||McLean||1|
|Development of Pavement Distress Standards||Quality Management Standards for Network-Level Pavement Data Collection||McLean||2|
|Development of Pavement Distress Standards||National Standards for Pavement Data - Performance Based Federal Aid Program||McLean||3|
|Development of Pavement Distress Standards||Improving Protocol Design with Advancing Technologies||Dallas||1|
|Development of Pavement Distress Standards||Best Practices of Profile Measurement and Analysis||Dallas||1|
|Development of Improved Methodologies for Evaluating Data Quality||Definition of Quality Management Principles for PMS Data Collection||McLean||2|
|Development of Improved Methodologies for Evaluating Data Quality||Best Practices for Quality Management||McLean||2|
|Development of Improved Methodologies for Evaluating Data Quality||Communicating Data Quality and Managing Expectations||McLean||2|
|Development of Improved Methodologies for Evaluating Data Quality||Defining Data Quality Requirements for Different Business Decisions||McLean||2|
|Development of Improved Methodologies for Evaluating Data Quality||Minimum Data Quality Standards for Pavement Management Data by Decision Level||Dallas||2|
|Development of Improved Methodologies for Evaluating Data Quality||Development of Pavement Management Quality Guide||Dallas||2|
|Development of Improved Methodologies for Evaluating Data Quality||Develop Techniques to Manage Data from Various Sources and Technology||Dallas||2|
|Development of Improved Methodologies for Evaluating Data Quality||Assessing Data Quality in Data Provided by Non-Agency Sources||Dallas||2|
|Development of Improved Methodologies for Evaluating Data Quality||Issues with Outsourced Information Technology Services||Dallas||3|
|Development of Improved Methodologies for Evaluating Data Quality||Improve Data Collecting and Analysis Consistency||Phoenix||7|
|Development of Improved Methodologies for Evaluating Data Quality||Best Practices for Data Collection and Analysis||Phoenix||7|
|Development of Improved Methodologies for Evaluating Data Quality||Development of More Sophisticated Methodologies for Evaluating Data Quality||Dallas||7|
|Best Practices Guide for Pavement Management||Impacts of Data Collection Frequency, Reporting on Pavement Decision Making||Phoenix||1|
|Best Practices Guide for Pavement Management||Outlining Mechanisms to Improve Agency Business Practices||Dallas||10|
|Best Practices Guide for Pavement Management||Identify Statutory Barriers that Prevent Effective Pavement Management Implementation||Dallas||10|
|Best Practices Guide for Pavement Management||Best Practices for Data Collection Needs to Support Decisions||Dallas||1|
|Best Practices Guide for Pavement Management||Best Practices for Disseminating Technology Transfer||Dallas||1|
|Best Practices Guide for Pavement Management||Effective Use of GPR||Dallas||1|
|Best Practices Guide for Pavement Management||Guidelines for Referencing Pavement Data Geospatially||Dallas||2|
|Best Practices Guide for Pavement Management||Data Storage Issues||Dallas||3|
|Best Practices Guide for Pavement Management||Research to Determine the Level of Accuracy Required||Dallas||4|
|Best Practices Guide for Pavement Management||Establishment of Feedback Loop||Dallas||4|
|Best Practices Guide for Pavement Management||Develop Incentives in Budget Allocations for Proper Project Selection||Dallas||5|
|Best Practices Guide for Pavement Management||Define Performance Curves Using Appropriate Parameters for Pavement Preservation Treatments||Dallas||5|
|Best Practices Guide for Pavement Management||Create Guidance Document that Defines When and Where to Use Structural Evaluation||Dallas||5|
|Best Practices Guide for Pavement Management||Pavement Management Influence on STIP, Strategic Plans, and Budget Allocation||Dallas||6|
|Best Practices Guide for Pavement Management||Effective Communications of PMS Info to Decision Making Process||Dallas||6|
|Best Practices Guide for Pavement Management||Organizational Effects of PMS||Dallas||6|
|Best Practices Guide for Pavement Management||Identifying Organizational Components that Lead to Successful PMS||McLean||10|
|Best Practices Guide for Pavement Management||Advancement in Data Collection Equipment Technology||McLean||1|
|Best Practices Guide for Pavement Management||Guidelines for Reporting Pavement Management Outputs||McLean||1|
|Best Practices Guide for Pavement Management||Best Practices for Data Collection and Reporting||McLean||1|
|Best Practices Guide for Pavement Management||Modeling Impact of Climate Change on Pavement Performance||McLean||4|
|Best Practices Guide for Pavement Management||Traffic Data Acquisition to Allow Performance Models to be Examined Based on Change in Condition Over Cumulative Loads||McLean||4|
|Best Practices Guide for Pavement Management||Guidance on Methods for Evaluating and Updating Models||McLean||4|
|Best Practices Guide for Pavement Management||Methods of Determining Model Reliability and Assessing Level of Reliability Needed at the Network Level||McLean||4|
|Best Practices Guide for Pavement Management||Guidelines for Picking Best Measures for Your Program||McLean||4|
|Best Practices Guide for Pavement Management||Guidance on Collecting Data for Changes in Design or Materials||McLean||4|
|Best Practices Guide for Pavement Management||Synthesis of Best Practices Using Multiple Triggers for a Treatment or Various Treatments of Physical and Environmental Conditions||McLean||5|
|Best Practices Guide for Pavement Management||Development and Implementation of Best Practices for a Practical, Needs-Based Budgeting Approach||McLean||5|
|Best Practices Guide for Pavement Management||Responsibility for Project-Level Decisions||McLean||6|
|Best Practices Guide for Pavement Management||Impact of Organizational Structure on Pavement Management||Phoenix||10|
|Best Practices Guide for Pavement Management||When to Decide to Abandon Historical Data||Phoenix||1|
|Best Practices Guide for Pavement Management||Benefits and Limitations of Automated Data Collection||Phoenix||1|
|Best Practices Guide for Pavement Management||Benefits and Limitations of Network Level GPR||Phoenix||1|
|Best Practices Guide for Pavement Management||Development of Quality Tolerances Based on Types of Data Collected||Phoenix||2|
|Best Practices Guide for Pavement Management||How to Store and Purge Safe, Secure, Up-to-Date Pavement Management Data||Phoenix||3|
|Best Practices Guide for Pavement Management||Effective Communication Issues||Phoenix||3|
|Best Practices Guide for Pavement Management||Impact of Model Details on Results||Phoenix||4|
|Best Practices Guide for Pavement Management||Use of Performance Models for Public Relations and Education||Phoenix||4|
|Best Practices Guide for Pavement Management||Develop a Repository of Models for Use by Other Agencies||Phoenix||4|
|Best Practices Guide for Pavement Management||Identify Construction and Material Parameters to Fine Tune Treatment Selection||Phoenix||5|
|Best Practices Guide for Pavement Management||Identify Impact Associated with Staff Reductions and Budgetary Constraints on Treatment Selection at the Network, Project, and Research Levels||Phoenix||5|
|Best Practices Guide for Pavement Management||Business Process - Allocation of Resources and Strategic Planning||Phoenix||6|
|Best Practices Guide for Pavement Management||Business Process - Network/Project Level Linkage||Phoenix||6|
|Synthesis of External Issues Driving Pavement Management||External Influences on Pavement Management||Dallas||6|
|Synthesis of External Issues Driving Pavement Management||Evaluation of External Issues Driving Pavement Management Needs||Dallas||7|
|Independent Technical Assessments of Pavement Management||Independent Technical Assessments by FHWA||McLean||8|
|Comprehensive Study to Guide the Integration of Pavement Preservation and Pavement Management||Determination of Required Inputs and Expected Outcomes to Effectively Integrate Pavement Preservation Strategies into Pavement Management||Phoenix||9|
|Comprehensive Study to Guide the Integration of Pavement Preservation and Pavement Management||Quantification of Costs and Benefits Associated with Different Levels of Pavement Preservation and Pavement Management Integration||Phoenix||9|
|Comprehensive Study to Guide the Integration of Pavement Preservation and Pavement Management||Developing a Plan and Implementation Guidelines for Integration of Pavement Management and Pavement Preservation||Phoenix||9|
|Comprehensive Study to Guide the Integration of Pavement Preservation and Pavement Management||Development of Minimum Levels and Best Practices of Integrating Pavement Preservation with Pavement Management||Phoenix||9|
|Comprehensive Study to Guide the Integration of Pavement Preservation and Pavement Management||Develop a Synthesis for Integration of Pavement Management and Pavement Preservation Practices||McLean||9|
|Comprehensive Study to Guide the Integration of Pavement Preservation and Pavement Management||Costs, Benefits, and Risks of Integrating Pavement Preservation into Pavement Management||McLean||9|
|Comprehensive Study to Guide the Integration of Pavement Preservation and Pavement Management||Development of Tools and Recommendations for Integrating Pavement Preservation into a PMS||McLean||9|
|Comprehensive Study to Guide the Integration of Pavement Preservation and Pavement Management||Define Preventive Maintenance to Include Activities Throughout Pavement Life||Dallas||9|
|Investigation Into the Risk, Uncertainty, and Variability in Pavement Management Decisions||Relationships Between Data Quality and Performance Models||Phoenix||2|
|Investigation Into the Risk, Uncertainty, and Variability in Pavement Management Decisions||Risk and Cost of Bad Data||Phoenix||2|
|Investigation Into the Risk, Uncertainty, and Variability in Pavement Management Decisions||Cost-Effectiveness of Data Quality||McLean||2|
|Investigation Into the Risk, Uncertainty, and Variability in Pavement Management Decisions||Determine the Costs and Benefits of Collecting Quality and Quantity of Data||Dallas||2|
|Investigation Into the Risk, Uncertainty, and Variability in Pavement Management Decisions||Business Process Issues - Accountability||Phoenix||6|
|Investigation Into the Risk, Uncertainty, and Variability in Pavement Management Decisions||How to Define the Success of the PMS||Phoenix||6|
|Investigation Into the Risk, Uncertainty, and Variability in Pavement Management Decisions||Uncertainties and Reliability of Pavement Management Results||McLean||6|
|Investigation Into the Risk, Uncertainty, and Variability in Pavement Management Decisions||Quality/Quantity of Pavement Management Data||Dallas||6|
|Investigation Into the Risk, Uncertainty, and Variability in Pavement Management Decisions||Precision and Bias Statements for Pavement Testing Equipment||Dallas||1|
|Methods of Defining and Calculating the Effect of Pavement Preservation Treatments on Pavement Life||Determine Required Inputs and Expected Outcomes to Effectively Integrate Pavement Preservation Strategies into Pavement Management||Phoenix||9|
|Methods of Defining and Calculating the Effect of Pavement Preservation Treatments on Pavement Life||Guidelines for Distribution of Funding Among Various Strategies for Managing Pavements||Phoenix||6|
|Methods of Defining and Calculating the Effect of Pavement Preservation Treatments on Pavement Life||Cost, Benefit, and Risk of Integrating Pavement Preservation and Pavement Management||McLean||9|
|Methods of Defining and Calculating the Effect of Pavement Preservation Treatments on Pavement Life||Quantify and Communicate the Benefits of Preventive Maintenance on Pavement Performance||McLean||9|
|Methods of Defining and Calculating the Effect of Pavement Preservation Treatments on Pavement Life||Definition of Pavement Preservation Benefits||Dallas||9|
|Methods of Defining and Calculating the Effect of Pavement Preservation Treatments on Pavement Life||Models for Preventive Maintenance||Phoenix||4|
|Methods of Defining and Calculating the Effect of Pavement Preservation Treatments on Pavement Life||Better Understanding of Which Maintenance Activities Impact Pavement Performance||McLean||4|
|Methods of Defining and Calculating the Effect of Pavement Preservation Treatments on Pavement Life||Methods of Assessing Impact of Changes in Routine Maintenance||McLean||4|
|Methods of Defining and Calculating the Effect of Pavement Preservation Treatments on Pavement Life||Ways to Model Preventive Maintenance Activities||Dallas||4|
|Methods of Defining and Calculating the Effect of Pavement Preservation Treatments on Pavement Life||Identify Criteria Needed to Determine Treatments at the Network, Project, and Research Levels||Phoenix||5|
|Methods of Defining and Calculating the Effect of Pavement Preservation Treatments on Pavement Life||Incorporating Accurate and Complete Maintenance, Preservation, and Pavement Construction History||McLean||5|
|Methods of Defining and Calculating the Effect of Pavement Preservation Treatments on Pavement Life||Define Parameters Required for Integrating Pavement Preservation into Pavement Management||Dallas||5|
|Title||Contributing Problem Statement Title||Originating Regional Workshop||Originating Focus Area|
|Annual Approval of SP&R Funding||Annual Approval of SP&R Funding||Phoenix||1|
|Addressing Trade-Offs, Metric Issues, and Purchasing Controls/Policies||Political and Organizational Issues/Inertia||Phoenix||3|
|Framework for Minimizing the Delivery of Treatment Applications||Improving the Contracting Process to Accommodate Timely Treatment Selection||McLean||5|
|Communicating Pavement Management Information and Benefits||Communication: Agency Staff Through Decision Makers||Phoenix||6|
|Communicating Pavement Management Information and Benefits||Communicating With External Stakeholders||Phoenix||6|
|Communicating Pavement Management Information and Benefits||Conveying and Communicating Output from Pavement Management||Phoenix||10|
|Communicating Pavement Management Information and Benefits||Communicating the State of Pavements With Upper Management||McLean||6|
|Communicating Pavement Management Information and Benefits||Communicating With the Public on the Cost of Pavement Infrastructure||McLean||6|
|Communicating Pavement Management Information and Benefits||Best Practices for Reporting Strategic Pavement Needs to Management and Legislators||McLean||7|
|Communicating Pavement Management Information and Benefits||Strategies for Effectively Marketing Pavement Management||McLean||10|
|Communicating Pavement Management Information and Benefits||Selling Pavement Management to Politicians and Administrators||McLean||10|
|Communicating Pavement Management Information and Benefits||Develop Communication Tools for Use With Agency Staff and Decision Makers on Treatment and Treatment Selection||Dallas||5|
|Communicating Pavement Management Information and Benefits||Communicating (Internal and External) Issues and Solutions of Integrating Pavement Preservation and Pavement Management||Phoenix||9|
|Communicating Pavement Management Information and Benefits||Quantify and Communicate the Benefits of Preventive Maintenance on Pavement Performance||McLean||9|
|Communicating Pavement Management Information and Benefits||Develop Effective Leadership Support and Accountability||McLean||10|
|Communicating Pavement Management Information and Benefits||Techniques for Gaining Buy-In from Decision Makers for Effective Pavement Management||McLean||10|
|Communicating Pavement Management Information and Benefits||Selling Pavement Management to District (Field) Engineers||Dallas||10|
|Communicating Pavement Management Information and Benefits||Use of Social Network Tools for Pavement Management Communications||Phoenix||7|
|Improving the Skills of Pavement Managers||Pavement Management Workforce Development||McLean||7|
|Improving the Skills of Pavement Managers||Broaden Skills of Pavement Managers to be More Successful||McLean||10|
|Improving the Skills of Pavement Managers||Institutionalizing Pavement Management through Workforce Development||McLean||10|
|Improving the Skills of Pavement Managers||Maintaining Pavement Management Staffing and Skills for Proper Decision Making||Dallas||6|
|Improving the Skills of Pavement Managers||Pavement Management Training||Dallas||7|
|Improving the Skills of Pavement Managers||Staffing and Succession Planning - FHWA Support for Education||Dallas||10|
|Improving the Skills of Pavement Managers||Development and Delivery of Training for Data Collection||McLean||2|
|Improving the Skills of Pavement Managers||Training and Curriculum for Pavement Data Quality||Dallas||2|
|Improving the Skills of Pavement Managers||Continuous Education of Workforce on the Evolution of Pavement Management Data||Phoenix||3|
|Improving the Skills of Pavement Managers||Cross Agency Institutional Issues in Data Management||Dallas||3|
|Improving the Skills of Pavement Managers||Training Guide Outlining Pavement Management Fundamentals||Phoenix||4|
|Improving the Skills of Pavement Managers||Pavement Management Academy||Phoenix||4|
|Improving the Skills of Pavement Managers||Information on Where Maintenance is Applied and What Was Done||McLean||4|
|Improving the Skills of Pavement Managers||Training Locals on Pavement Management Through LTAP||McLean||4|
|Improving the Skills of Pavement Managers||Training on How to Do Modeling for Practitioners||Dallas||4|
|Improving the Skills of Pavement Managers||Decisions Aligned with Data||Dallas||4|
|Improving the Skills of Pavement Managers||Best Practices to Capture Construction, Preservation, and Maintenance Treatments||McLean||5|
|Improving the Skills of Pavement Managers||Develop Effective Feedback from Pavement Preservation and Rehabilitation into the Pavement Management Database||Dallas||5|
|Improving the Skills of Pavement Managers||Integrate Pavement Preservation into Pavement Management||Dallas||5|
|Improving the Skills of Pavement Managers||Develop an Agency-Specific Pavement Management Process Manual||Dallas||2|
|Improving the Skills of Pavement Managers||Need Attractive Career Path for Pavement Management Practitioners||Dallas||4|
|Identify IT Needs to Effectively Manage a Pavement Management System||Challenges Associated with Centrally Managed IT Environments||Dallas||3|
|Methods to Promote Pavement Management as a Management Tool||Effective Communication Toolset for Pavement Managers||Phoenix||8|
|Methods to Promote Pavement Management as a Management Tool||National Promotional Clip Promoting Pavement Management||Phoenix||8|
|Methods to Promote Pavement Management as a Management Tool||Conveying and Communicating Output from Pavement Management||Phoenix||10|
|Methods to Promote Pavement Management as a Management Tool||Promotion of Pavement Management Benefits to Non-Technical Audiences (Executives and Legislators)||McLean||8|
|Methods to Promote Pavement Management as a Management Tool||Strategies for Effectively Marketing Pavement Management||McLean||10|
|Methods to Promote Pavement Management as a Management Tool||Guidance on Understanding Benefits for Various Stakeholders, Including Defining Performance Measures and Goals||Dallas||8|
|Methods to Promote Pavement Management as a Management Tool||Develop Methods to Sell Pavement Management as a Management Tool||Dallas||8|
|Methods to Promote Pavement Management as a Management Tool||Selling Pavement Management to Politicians and Administrators||Dallas||10|
|Impact of Pavement Management Investment Levels on Benefits||Method to Quantify the Benefit of Information for Pavement Management||Phoenix||8|
|Impact of Pavement Management Investment Levels on Benefits||Quantify Risks and Consequences of Changes in the Availability of Pavement Information||McLean||8|
|Suggested Topics for Pavement Management into the Civil Engineering Curriculum||Education of Future Practitioners in Pavement Management||McLean||8|
|Constant Funding for Pavement Management||Establish Need for Consistent Funding to Allow Appropriate Planning by Pavement Management Staff||Dallas||10|
|Constant Funding for Pavement Management||Synthesis of Current Practices for Allocating Funding Resources While Dealing With Institutional Influences||McLean||9|
|Constant Funding for Pavement Management||Quantifying the Effects of Sub-Optimal Decisions on Network Performance||McLean||9|
|Recommended Methodology to Calculate Pavement Asset Value and Communicate to Stakeholders||Recommended Methodology to Calculate Pavement Asset Value and Communicate to Stakeholders||Technical Panel Meeting||N/A|
|Title||Contributing Problem Statement Title||Originating Regional Workshop||Originating Focus Area|
|Pavement Management Data Mining: Improving Current Uses and Leveraging New Applications of Pavement Management Data||Input of Pavement Construction and Maintenance Data into Pavement Management||Dallas||2|
|Pavement Management Data Mining: Improving Current Uses and Leveraging New Applications of Pavement Management Data||Data Integration Benefits||Phoenix||3|
|Pavement Management Data Mining: Improving Current Uses and Leveraging New Applications of Pavement Management Data||Increasing Data Integration to Improve Stewardship||Phoenix||3|
|Addressing Customer Service with Data Integration Systems||Phoenix||3|
|Synthesis of Data Integration Systems||Phoenix||3|
|Using Successful Pavement Management Practices to Frame and Guide Management System Development in Other Asset Areas||Dallas||3|
|Addressing Near-Term Data Storage and Integration Technology Issues||Dallas||3|
|Pavement Management Challenges and Practices Within Tolling Agencies||Dallas||3|
|Leveraging Pavement Management With Related Data Sources||McLean||7|
|Merging of Data Sets Across Multiple Agencies Within a State||McLean||7|
|Modeling Load Limit Impacts||Modeling the Impacts of Load Limits on Pavement Performance||McLean||4|
|Use of Pavement Management Information for National Reporting||Develop National Performance Measures||McLean||4|
|Pavement Management Data as Compared and Contrasted and Used Against Item Data||Phoenix||7|
|Annual State of the Practice Report to FHWA||McLean||8|
|Justification for Using Pavement Management Data in Lieu of HPMS for Reporting to FHWA||Dallas||7|
|Expanding Treatment Selection Accountability in the Future||McLean||5|
|Development and Use of Effective Performance Measures||Goal Setting for Effective Pavement Management||Phoenix||6|
|Development and Use of Effective Performance Measures||Synthesis on Consistent Terminology, Performance Targets, Measures, and Threshold Triggers||McLean||6|
|Development and Use of Effective Performance Measures||Correlation Between Pavement Management Inputs and Performance Measures Reported||McLean||6|
|Development and Use of Effective Performance Measures||Managing Pavements as an Investment||McLean||7|
|Development and Use of Effective Performance Measures||Keeping Pavement Management Relative to the Asset Management Process||McLean||7|
|Development and Use of Effective Performance Measures||Pavement Management as a Part of Asset Management||Dallas||6|
|Development and Use of Effective Performance Measures||Measures Needed in Pavement Management to Support Pavement Preservation and Definition of Pavement Preservation Benefits||Dallas||9|
|Development and Use of Effective Performance Measures||Develop Guidance on Use of Performance Measures in Decision Making||Dallas||9|
|Development and Use of Effective Performance Measures||Goals and Performance Targets Related to Pavements||Phoenix||10|
|Development and Use of Effective Performance Measures||Effect of Asset Management on Pavement Management||Phoenix||10|
|Development and Use of Effective Performance Measures||Establishing a Performance Reporting System for Pavement Management Data||Dallas||10|
|Developing and Supporting a Pavement Management Business Plan||Developing and Supporting a Pavement Management Business Plan||Technical Panel Meeting||N/A|
|Using Pavement Management Data to Support Design Activities||Stronger Relationship Between Design and Pavement Management Models||McLean||4|
|Using Pavement Management Data to Support Design Activities||Best Practices for Incorporating the MEPDG into Pavement Management||McLean||7|
|Using Pavement Management Data to Support Design Activities||Feedback to Pavement Design||Dallas||7|
|Using Pavement Management Data to Support Design Activities||Advancing Analytical Tools for Continual Prediction Calibration||Dallas||7|
|Methodologies to Reliably Support Innovative Contracting||Use of Pavement Management in Performance-Based Warranty Contracts and Public-Private Partnerships||Phoenix||7|
|Methodologies to Reliably Support Innovative Contracting||Preparing Pavement Management to Reliably Support Innovative Contracting Processes||McLean||7|
|Methodologies to Reliably Support Innovative Contracting||Impact of Innovative Contracting Practices on Pavement Management||McLean||7|
|Identify Data Needs to Support Other Processes||Baseline Inventory of Network Needed for Decisions and Managing Pavements||Phoenix||2|
|Identify Data Needs to Support Other Processes||Pavement Management Data Integration to Support Future Transportation Needs||Phoenix||3|
|Identify Data Needs to Support Other Processes||Guidelines and Data to Support Transportation Asset Management Systems||McLean||3|
|National Funding Allocations That Account for State Priorities||Identifying How Individual State Priorities Hinder Development of National Standards||Phoenix||10|
|Impacts of Earmarks on Pavement Performance||Impacts of Earmarks on Long Range Plans and Pavement Conditions||Phoenix||10|
|Title||Contributing Problem Statement Title||Originating Regional Workshop||Originating Focus Area|
|Development of Automated Condition Data Processing Tools||Develop a Fully Automated Distress Identification||Phoenix||1|
|Development of Automated Condition Data Processing Tools||Evaluation of Latest Technologies for Implementation in Pavement Management||Dallas||1|
|Development of Automated Condition Data Processing Tools||Software Needs for Fully Automated Data Processing||Dallas||7|
|Analysis of Trade-Offs Associated with Alternate Methods of Data Collection||Analysis of Trade-Offs Associated with Alternate Methods of Data Collection||Technical Panel Meeting||N/A|
|Improving Factors Considered in Project and Treatment Selection Decisions||Strengthen Treatment Selection Through Workforce Development by Associating Condition Triggers with Improved Pavement Performance||McLean||5|
|Improving Factors Considered in Project and Treatment Selection Decisions||Using LCCA to Quantify Treatment Selections||McLean||5|
|Improving Factors Considered in Project and Treatment Selection Decisions||Develop Criteria, Create Manual and Training for Treatment Selection||Dallas||5|
|Improving Factors Considered in Project and Treatment Selection Decisions||Linkage of Pavement Management to Other Programs (e.g., Safety, Congestion, and Environment)||Phoenix||6|
|Improving Factors Considered in Project and Treatment Selection Decisions||Pavement Management Enhancements to Address Emerging Issues||McLean||6|
|Improving Factors Considered in Project and Treatment Selection Decisions||Incorporating User Costs in the Pavement Management Process||McLean||6|
|Improving Factors Considered in Project and Treatment Selection Decisions||Project Treatment Selection||Dallas||6|
|Improving Factors Considered in Project and Treatment Selection Decisions||Decision Support for Pavement Management||Dallas||7|
|Improving Factors Considered in Project and Treatment Selection Decisions||Identification of Non-Traditional Benefits for Inclusion in Optimization Analysis||McLean||5|
|Improving Factors Considered in Project and Treatment Selection Decisions||Characterizing Effective and Realistic Optimization Techniques for Implementable Pavement Treatment Solutions||McLean||5|
|Methods to Quantify the Benefits of Pavement Management||How Do We Address the Broad Reach of Pavement Management?||Phoenix||8|
|Methods to Quantify the Benefits of Pavement Management||Quantifying Pavement Management Benefits Related to User Costs||Phoenix||8|
|Methods to Quantify the Benefits of Pavement Management||Quantify the Benefits Derived from Pavement Management||Phoenix||10|
|Methods to Quantify the Benefits of Pavement Management||Synthesis of Current Methods for Quantifying Benefits||McLean||8|
|Methods to Quantify the Benefits of Pavement Management||Methods of Capturing Pavement Management Impacts on Other Programs and Identifying Societal Benefits (e.g., Economic and Environmental)||McLean||8|
|Methods to Quantify the Benefits of Pavement Management||Using Pavement Management to Support the Bottom Line in Private, Public/Private Transportation Asset Management Agencies||Dallas||8|
|Methods to Quantify the Benefits of Pavement Management||Methods to Quantify Benefits of Pavement Management Systems||Dallas||8|
|Methods to Quantify the Benefits of Pavement Management||Develop Guidance in Using Pavement Management to Justify and Defend Engineering Decisions||Dallas||8|
|Methods to Quantify the Benefits of Pavement Management||Links Between Infrastructure Health measures and Other Performance Measures||McLean||4|
|Pavement Management in a Changing World||Effectively Managing Pavements in Changing Environments||McLean||10|
|Pavement Management in a Changing World||Future Trends Influencing Enhancements for Pavement Management Systems||Dallas||10|
|Pavement Management in a Changing World||Impact of Increased Data Requirements on Pavement Management (e.g., HPMS, MEPDG, and HERS-ST)||Dallas||10|
|Automation of Surface Texture Characteristics||New Applications for Use of Macrotexture||Phoenix||1|
|Automation of Surface Texture Characteristics||Automation of Surface Texture Characteristics||Dallas||1|
|Automation of Surface Texture Characteristics||Identification of Non-Traditional Factors Impacting Pavement Deterioration||Dallas||4|
|Automation of Surface Texture Characteristics||Develop Additional Pavement Condition Measures in the Decision Making Process for Proper Treatment Selection||McLean||5|
|Method for Effectively Modeling Structural Condition||Models to Capture Both Functional and Structural Components||Phoenix||4|
|Method for Effectively Modeling Structural Condition||Methods of Modeling Structural Adequacy||McLean||4|
|Method for Effectively Modeling Structural Condition||Methods of Effectively Modeling Structural Condition||Dallas||4|
|Method for Effectively Modeling Structural Condition||Quantification of Network Level Structural Condition Using High-Speed Deflection Testing||McLean||1|
|Method for Effectively Modeling Structural Condition||Quantifying the Benefits of Structural Capacity Testing||Dallas||1|
|Method for Effectively Modeling Structural Condition||Optimizing the Efficiency of Deflection Testing||Dallas||1|
|Method for Effectively Modeling Structural Condition||Automation of Material Properties Characterization||Dallas||1|
|Impact of Climate Change on Performance Prediction||Impact of Climate Change and Sustainability Efforts on Models||Phoenix||4|
|Develop Default Models for Low-Volume Roads||Nationally Developed Default Models for Low-Volume Roads for Pavement Management and MEPDG||McLean||4|
|Performance Models That Consider Series of Treatments||Performance Models That Consider a Series of Treatments||Technical Panel Meeting||N/A|
|Quantifying the Benefits of Pavement Research||Market Analysis of Pavement Research Benefits||McLean||7|
|Quantifying the Cost of Pavement Use||Quantifying the Cost of Pavement Use||McLean||7|
|Identifying Strategies for Incorporating Emerging Technologies into the Pavement Management System||Sustainability in Changing Needs and Emerging Technology in Data Collection and Analysis||Phoenix||7|
|Identifying Strategies for Incorporating Emerging Technologies into the Pavement Management System||Clearinghouse for Evaluation of New Technologies||Phoenix||7|
|Identifying Strategies for Incorporating Emerging Technologies into the Pavement Management System||Optimizing Pavement Surface Properties||McLean||7|
|Identifying Strategies for Incorporating Emerging Technologies into the Pavement Management System||Development of Methodologies and Analysis Tools to Incorporate Sustainability||McLean||7|
|Identifying Strategies for Incorporating Emerging Technologies into the Pavement Management System||Emerging Technologies in Electronic Data Collection||McLean||7|
|Identifying Strategies for Incorporating Emerging Technologies into the Pavement Management System||Identification and Validation of Emerging Hardware Technologies||Dallas||7|
|Identifying Strategies for Incorporating Emerging Technologies into the Pavement Management System||Identify Emerging Technologies That Drive Pavement Management Needs||Dallas||7|
|Develop Nondestructive Testing (NDT) for Measurement of In-Place Material Properties||Develop Technology and Equipment That Can Measure In-Place HMA Density, Full Width (NDT)||Phoenix||1|
|Use of Aerial Images for Distress Analysis||Use of Aerial Images for Distress Analysis||Phoenix||1|
|Use of Aerial Images for Distress Analysis||Use or Appropriate Application of New Technologies for Data Collection||Phoenix||2|
|Development and Integration of Wireless Sensors With Pavement Management||Development and Integration of Wireless Sensors With Pavement Management||Technical Panel Meeting||N/A|
The attendees from the three regional workshops were invited to participate in a webconference at which the combined list of needs was presented. Immediately following the webconference, participants were given an opportunity to vote on the relative importance of each of the needs statements and their perceived priorities within each theme. As part of this activity, participants were asked to assign a relative importance to each needs statement, using the following terms:
In addition, participants were asked to rank the needs statements within each of the theme areas, on a scale of 1 to 5, with 1 being the high priority. To facilitate the ranking, short-term and long-term needs within each theme were ranked separately. As a result, each participant provided a total of eight ranked lists (four theme areas multiplied by two lists for short-term and long-term needs). A computerized balloting tool was used to facilitate this activity and a total of fifty-three individuals participated in the ranking exercise.
The results of the balloting were used to develop the prioritized list of short-term and long-term needs included in the next chapter. For use in the ranking process the relative importance levels were assigned the following values: very important = 3 points, important = 2 points, and not very important = 1 point. The priorities were established by multiplying the average relative importance and the average ranking assigned by the participants for each needs statement. The results produced, in essence, a weighted average that could be used to develop a ranked list that combines the results from each theme, regardless of the number of needs statements within the theme.