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6 Effect of the size of the network being evaluated on the ification, and their effect on data rejection. Although the same quality management process; tools are often used in more than one stage of the quality man- How agencies are addressing issues associated with loca- agement process, they are organized in the most common tion referencing systems; configuration. Quality control tools discussed include person- Time-history issues associated with the introduction of nel training and certification, equipment/method calibration, new techniques and/or changes in data service provider; verification, and certification, data verification procedures, Gaps in knowledge and needed improvements to current and software data checks. Quality acceptance tools include practice; and control and verification test sites, establishing acceptance cri- Specific research and development needs. teria, sampling, database checks, and time-history compar- isons. The chapter also covers some specific issues associated Although the synthesis focuses on the collection of dis- with the acceptance of data collection assembled by contracted tress data at the network level, it also covers smoothness, service providers. friction, and structural capacity data collection processes and some elements of current quality management practices for Chapter five documents the data management practices of project-level data collection. four transportation agencies. For each agency reviewed, the chapter discusses the quality management procedures applied The synthesis contains six chapters. Chapter one provides before collecting the data, as the data are being collected, dur- the background for the synthesis, including the objectives ing the post-processing of the data, and during the analysis of and scope of work, major definitions relevant to the synthe- the data for supporting pavement management decisions and sis, the methodology used for collecting and analyzing the other business processes. Each case study highlights some information, some general background on the types of pave- of the distinct aspects of the agencies' quality management ment condition data collected, the importance of quality data, practices. and the impact of these data on the quality of the supported pavement management decisions. It also provides a brief Finally, chapter six provides a summary of the key findings description of the organization of the report and how the var- of the synthesis project, summarizes the state of the practice ious components of the research were used to develop the on quality management of pavement condition data, and report's conclusions. highlights examples of good practices. This final chapter also identifies gaps in knowledge and needed improvements to Chapter two discusses in detail the type of data collected by current practice, and notes areas that have specific research highway agencies to determine the pavement's structural and and development needs. functional condition to support pavement management deci- sions. Specific issues covered included network- vs. project- level data collection, outsourcings of pavement condition data BACKGROUND collection to service providers, location referencing, pavement The Data Warehousing Institute estimates that poor data qual- characteristics evaluated, and network coverage. The chapter ity costs American business $600 billion annually (2). Trans- also addresses time-history issues associated with introduc- portation agencies are no exception; quality data are essential tion of new techniques and/or changes in service providers to support asset (and in particular pavement) management deci- and additional challenges arising for adoption of new business sions at all organizational levels. However, the level of detail processes, such as the Highway Performance Monitoring and "quality" of the information required is heavily depen- System (HPMS) reassessment and implementation of the dent on the level of decision making being supported. This Mechanistic-Empirical Pavement Design Guide (MEPDG). section provides a brief overview of the types of condition data collected for managing pavements, defines quality and the Chapter three presents the main data quality management main quality terms used in pavement data collection, and intro- concepts and principles and summarizes the general poli- duces the main issues associated with the quality of pavement cies and guidelines currently being followed by transportation condition data. agencies to conduct quality management activities. In partic- ular, the chapter expands on data quality management plans and the distinction between quality control, quality acceptance, Pavement Management and independent assurance. It also covers reference value determination, sources of variability in pavement data col- Pavement management is a key asset management business lection, and the effect of the size of the network being evalu- process that allows department of transportation (DOT) per- ated on the quality management process. sonnel to make cost-effective decisions regarding the preser- vation and renewal of the pavements under their jurisdiction. Chapter four focuses on the specific quality management Pavement management provided the framework for the devel- techniques that are being applied for pavement condition data opment of asset management, and pavements account for a collection. It discusses the tools and processes being followed large percentage of the total assets managed by a typical for quality control, quality acceptance, and independent ver- DOT (3). A PMS is a set of decision-support tools (and meth-

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7 ods) that can assist decision makers in finding cost-effective processes needed for asset management (12). However, the strategies for providing, evaluating, and maintaining pave- use of the aforementioned technologies has in some cases led ments in a serviceable condition (4). An effective PMS, as agencies to collect very large amounts of data and create vast with any decision support tool, includes reliable and sufficient databases that have not always been useful or necessary for data, calibrated analysis models and procedures, and tools that supporting network-level decision processes. help visualize and quantify the impact of the possible solu- tions considered. The 2001 AASHTO Pavement Management It is important that the agencies tailor the data collection Guide discussed in detail the technologies and processes used practices to the use of the data and the level of decisions being for the selection, collection, reporting, management, and analy- supported. Because of excessive data collection requirements, sis of data used in pavement management at the state level (1). PMS are sometimes seen as too data-intensive and too expen- The data needed in a PMS include inventory information (e.g., sive to sustain. To avoid this situation, three guiding prin- pavement structure, geometrics, costs, and environment), road ciples are recommended: (1) collect only the data needed; usage (traffic volume and loading), pavement condition (ride (2) collect data to the lowest level of detail sufficient to make quality, surface distresses, friction, and/or structural capacity), appropriate decisions; and (3) collect data only when they are and pavement construction, maintenance, and rehabilitation needed (13). To help tailor the data collection practices to history. In particular, the quality of the pavement condition the uses of the data, Paterson and Scullion (14) introduced the data is critical for producing informed decisions. concept of Information Quality Levels (IQL) for road man- agement. This concept helps highway agencies structure road Pavement management tools are currently used to support management information into different levels that correlate strategic decisions across various asset types within agency- to the degree of sophistication required for decision making wide asset management systems, network-level project selec- and, thus, the appropriate methods for collecting and pro- tion and resource allocation decisions, and project-level cessing data. Within the proposed framework, very detailed decisions. Strategic-level tools typically support trade-off data (low-level data) can be condensed or aggregated into analysis across asset classes and agency programs, and high- progressively simpler forms (higher-level data). Bennett and level impact analysis. Network-level analysis tools support Paterson (15) defined five levels as presented in "A Guide to planning and programming decisions for the entire network Calibration and Adoption of HDM-4" (see Figure 1). They or system (5). Examples include tools to evaluate the condition ranged from very detailed data in an IQL-1 (research and of the pavement network and predict pavement performance benchmark data for other measurement methods) to a very over time; identify appropriate preservation and rehabilita- general IQL-5 (top-level data, such as key performance mea- tion projects; evaluate the different alternatives and deter- sures or indicators, which typically might combine key attri- mine the network needs; prioritize or optimize the allocation butes from several pieces of information). of resources to generate plans, programs, and budgets; and assess the impact of the funding decisions. Project-level analy- Another relevant and current issue is that the pavement sis tools are then used to select the final alternatives and design condition data collection technologies are advancing rapidly. the projects included in the work program. Examples include NCHRP Synthesis 334 (6) found that essentially all North tools for pavement-type selection, life-cycle cost analysis, American highway agencies are collecting pavement condi- pavement analysis, and structural design. tion data through some automated means. Furthermore, the synthesis also found that 33 agencies (out of 56) use service providers (also called vendors or contractors) to collect at Pavement Management Data Collection least some of the automated data. This creates new challenges Pavement condition data collection is one of the key com- for ensuring data consistency over time, as these automated ponents of pavement management. Several NCHRP synthe- data collection technologies may measure different pavement ses have covered this topic; Table 1 summarizes the most characteristics than those determined visually. The study also recent ones. found that significant advances have been made in the area of quality assurance. In particular, the synthesis highlights some The type of data collected in a PMS include smoothness good examples from Canadian provinces, especially with (ride quality), surface distresses (rutting, cracking, faulting, sensor-related processes. etc.), frictional properties of the surface (tire/pavement fric- tion or skid resistance and, more recently, macrotexture), and Quality Management--General Terminology structural capacity (deflections). The way in which transporta- tion agencies collect, store, and analyze data has evolved Quality is a desired essential or distinctive characteristic, prop- along with advances in technology, such as mobile comput- erty, or attribute of something (or its degree of excellence). ing, advanced sensors, imaging technologies, distributed data- To consistently achieve a quality product or service, it is nec- bases, and spatial technologies. These technologies have essary to adopt appropriate quality management practices. enabled the data collection and integration procedures nec- Although the concept of quality management is well devel- essary to support the comprehensive analyses and evaluation oped and has been extensively used in industrial production

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8 TABLE 1 RECENT NCHRP SYNTHESES RELATED TO PAVEMENT CONDITION DATA COLLECTION No. Title Year Content 334 Automated Pavement 2004 Examines automated collection and processing of pavement (6) Distress Collection condition data techniques typically used in network-level pavement Techniques management, contracting issues, quality assurance, costs and benefits of automated techniques, monitoring frequencies and sampling protocols in use, degree of adoption of national standards for data collection, and contrast between the state-of-the-art and the state-of- the-practice. 335 Pavement Management 2004 Examines the use of GIS and other spatial technologies in PMS, and (3) Applications Using discusses how the technologies have been combined to enhance the Geographic Information highway management process. It discusses data collection and Systems (GIS) integration, and location referencing systems. 291 Evaluation of Pavement 2000 Discusses the methods used for evaluating wet pavement friction (7) Friction Characteristics characteristics of new and restored pavements and reviews models used for measuring and evaluating friction and texture, causes for friction changes over time, and aggregate and mix design to provide adequate friction. 268 Relationship Between 1998 Presents a comprehensive synopsis of pavement/tire noise as it relates (8) Pavement Surface Texture to roadways. Detailed information is presented on measurement and Highway Traffic Noise techniques, reported noise emission results for pavement type and texture, effects of pavement wear, surface friction, and maintenance and safety considerations. 203 Current Practices in 1994 Examines practices for the collection, reporting, and application of (9) Determining Pavement pavement condition data for their service in PMS, focusing on four Condition primary measures of pavement condition: distress, smoothness, structural capacity, and friction evaluations. It describes the types of equipment used and how the data are used to affect decision making by transportation managers. 167 Measurements, 1990 Examines the various devices and specifications that were being used (10) Specifications, and to measure smoothness and ensure that newly constructed pavements Achievement of will provide a smooth ride. Smoothness for Pavement Construction 126 Equipment for Obtaining 1986 Identified equipment that was associated with the collection of (11) Pavement Condition and structural capacity, surface distress, friction, smoothness, and traffic Traffic Loading Data loading data. Costs, maintenance requirements, advantages and disadvantages, and new equipment developments are briefly discussed. HIGH LEVEL DATA System Performance IQL-5 Performance Monitoring Network Planning and IQL-4 Structure Condition Performance Evaluation Level Data Program Analysis or IQL-3 Ride Distress Friction Detailed Planning Project Level or IQL-2 Detailed Programming IQL-1 Project Detail or Research LOW LEVEL DATA FIGURE 1 Information quality level [after Bennett and Paterson (15)].

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9 Before Data Collection During Production After Production (Data Collection & Processing) Quality Control Personnel training/certification On-vehicle real-time data checks Distress rating data checks Equipment calibration/ Periodic diagnostics/data checks Final database checks Quality Management certification/ inspections Incoming data and video check Completeness checks ... ... ... Quality Acceptance Initial Control Site Testing Complete database checks Final database reviews Review qualifications or Control/ verification site testing GIS-based quality checks certifications Sampling for quality acceptance Time history comparisons ... ... ... Independent Assurance Consistency checks Completeness checks Sampling and re-analyzing Time History Comparisons ... ... FIGURE 2 Pavement condition data quality management framework. processes, it has not been systematically applied to pavement those used to control the data collection activities, either data collection. This issue is discussed in detail in the follow- by a data collection service provider or a road agency ing chapters. collecting data in-house, so that quality pavement con- dition data can be obtained. The following definitions were obtained by adapting those Quality Acceptance: Those planned and systematic found in the quality management field to the pavement data actions necessary to verify that the data meet the quality collection activities. Special attention was given to the defi- requirements before it is accepted and used to support nitions provided in the Transportation Research Circular pavement management decisions. These actions govern E-C037: Glossary of Highway Quality Assurance Terms (16 ), the acceptance of the pavement condition data collected which defined standard terminology for highway quality with using either a service provider or in-house resources. a focus on construction processes and to the terminology used Quality acceptance is often referred to as quality assur- in NCHRP Synthesis 334 (6). ance in the pavement engineering and management field. Quality Assurance: The part of quality management focus- Quality: "The degree to which a set of inherent charac- ing on increasing the ability to fulfill requirements. It teristics fulfill requirements" (17 ). These requirements includes all those planned and systematic actions nec- could be features and characteristics of a product that essary to provide confidence that a product or facility are specified in a contract or identified and defined will perform satisfactorily in service. Because this term internally by the company or agency based on the cus- is often used in practice to refer to quality acceptance tomer expectations. The product could be a physical activities, to avoid confusion it is not used in the remain- entity (e.g., a calculator) or a service (e.g., auto repair, ing sections of the synthesis. or, as is the focus of this synthesis, data collection). Independent Assurance: A management tool that requires Quality Management: The overarching system of poli- a third party, not directly responsible for process control cies and procedures that govern the performance of or acceptance, to provide an independent assessment quality control and acceptance activities; that is, the of a product or service and/or the reliability of test totality of the effort to ensure quality in the pavement results obtained from process control and acceptance condition data. testing (16 ). Quality System: The organizational structure, procedures, processes, and resources needed to implement quality management to meet the quality objectives. Figure 2 summarizes the terminology used in this synthe- Quality Control: Those actions and considerations nec- sis, and provides examples of activities in each data quality essary to assess and adjust production processes so as management phase. The figure also shows examples of activ- to control the level of quality being produced in the ities typically included in these processes. These and other end product. It is also called process control. For pur- relevant activities will be introduced in chapter two and dis- poses of this synthesis, quality control activities are cussed in detail in chapters three and four.