Automated Data Collection and Quality Management for Pavement Condition Reporting (2022)

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3   Background The assessment of pavement surface conditions has evolved from manual to semiautomated to fully automated methods. Manual surveys of pavement condition, conducted by walking or traveling at slow speeds and noting the existing surface distress (e.g., cracking, patching), were the predominant survey method for the majority of state highway agencies (SHAs) prior to the 1990s. By the mid-1990s to early 2000s, surveys of pavement distress had transitioned to more automated methods. These methods typically included high-speed cameras to capture surface images (video followed by digital imagery), which then required visual review to identify distress type, severity, and extent (also referred to as “semiautomated surveys”). However, challenges with achieving high-quality images and maintaining pace with rapidly advancing digital image technology, resulted in another shift to two- and three-dimensional (2D and 3D, respectively) laser image technology. Today, 2D/3D technology is the predominant method for quantifying pavement surface distress (also referred to as “fully automated methods”). Automated pavement condition surveys (APCS) provide the opportunity to assess current pavement surface conditions, including cracking, rutting, faulting, and roughness, for a single lane in a single-vehicle pass. The transition from manual to fully automated methods allows for 100% coverage of the roadway surface, collection at posted highway speeds, and improved safety conditions; however, agencies are faced with assessing the impacts that the results of automated data collection may have on pavement management activities (e.g., index calcula- tions, reporting). Federal regulations provided in the Code of Federal Regulations (CFR) Part 490, National Performance Management Measures (PM2), require SHAs to report pavement condition and to establish pavement performance targets for the National Highway System (NHS) (23 CFR 490). The majority of SHAs have already transitioned to an APCS. While SHAs gain more data, knowl- edge, and experience with APCSs, this migration also brings new challenges. These challenges, due in large part to the rapidly evolving APCS technology, include increased data quality control and acceptance activities, changes in the data analysis process, and impacts on decision-making activities (e.g., changes in performance models, decision trees). Synthesis Objectives The objectives of this synthesis are as follows: • Document the experiences, challenges, and state-of-the-practice solutions used by SHAs that are in the midst of transitioning or have already transitioned to automated/semiautomated processes for collecting pavement data and • Summarize the data for state and federal requirements for reporting pavement condition. C H A P T E R   1 Introduction

4 Automated Data Collection and Quality Management for Pavement Condition Reporting Synthesis Scope and Approach The study focused on agency practices for collecting data on pavement condition and on state and national requirements for reporting pavement condition. The information collected, as a minimum, included the following: • Pavement condition survey method; • Types of condition data collected; • Certification, verification, and audit processes for pavement condition surveys; • Cost factors (e.g., data storage, surveying equipment, service providers, software); • Compatibility of automated data with historical data (e.g., definitions, measurements); • Challenges in transitioning condition indices from manual to APCS; and • Methods of analyzing data for PM2 reporting (e.g., a separate analysis algorithm, a transfer function). Methods for collecting the desired information included a literature review, an agency ques- tionnaire, and follow-up questions to obtain additional details regarding APCS implementa- tion, PM2 reporting, and internal agency reporting of pavement condition. The results of the literature search were used to supplement the development of an agency questionnaire of prac- tice. The agency questionnaire was distributed to each SHA, the Puerto Rico Highways and Transportation Authority, and the District DOT by means of a web-based survey platform. In order to obtain more detailed information related to agency practice, follow-up questions were sent to agencies that indicated a willingness to support the case examples. Follow-up ques- tions investigated, for example, the details on required changes and challenges related to imple- menting an APCS, changes and challenges for meeting PM2 reporting requirements, and how the agency utilizes the APCS results in support of agency activities and decision-making efforts. The information obtained from the literature review, the questionnaire of practice, and the follow-up questions provided the basis for the information contained in this synthesis. Report Organization The remainder of this synthesis is organized into the following chapters: • Chapter  2: Literature Review. This chapter summarizes findings of the literature review. Relevant topics covered in the literature review include summary of methods for collecting pavement condition data, agency data quality management plans (DQMPs), and national reporting requirements. • Chapter 3: State of the Practice. This chapter summarizes the results from the agency ques- tionnaire and includes topics related to methods for collecting pavement condition data, costs, storage needs, sampling and acceptance efforts, and pavement condition measures and reporting requirements. • Chapter 4: Case Examples. This chapter summarizes information provided by SHAs on the details of implementing an APCS, PM2 reporting, and agency use of APCS results. • Chapter 5: Summary of Findings. The synthesis concludes with a summary of key findings and suggested areas for further research and outreach to improve the use of APCS results and agency and national reporting requirements. • Appendices: Appendix A includes the list of questions from the agency web-based question- naire distributed to the SHAs, and Appendix B summarizes the SHA responses to each ques- tion of the questionnaire.