Automated Data Collection and Quality Management for Pavement Condition Reporting (2022) / Chapter Skim
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Pages 35-56
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35 A questionnaire was developed to determine agency practices for pavement condition surveys, use of pavement performance measures, and pavement condition reporting requirements. The questionnaire was provided to highway agencies in all 50 U.S.
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36 Automated Data Collection and Quality Management for Pavement Condition Reporting Automated Pavement Condition Surveys For agencies that conduct semi- and fully automated APCSs, the majority (24 agencies) have been doing so for more than 10 years (Figure 16)
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State of the Practice 37 methods include using agency staff and equipment to conduct data collection, processing, and analysis (14 agencies) and contracting with vendors to conduct data collection, processing, and analysis (14 agencies)
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38 Automated Data Collection and Quality Management for Pavement Condition Reporting The information illustrated in Figure 18 is summarized below and in Table 24 according to the method of pavement condition survey (i.e., fully automated, semiautomated, manual, and various combinations) : • Of the agencies that use agency staff and equipment to conduct the pavement condition survey, 5 use fully automated methods, 3 use a combination of fully and semiautomated methods, 2 use manual methods, and the remaining 4 use a combination of manual and fully automated (2 agencies)
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State of the Practice 39 activities. Figure 20 provides a summary of agency responses to a variety of activities in preparation of APCS implementation.
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40 Automated Data Collection and Quality Management for Pavement Condition Reporting Of those agencies that have transitioned to APCS, the majority (30 agencies) noted challenges with validation and verification of data quality (Figure 21)
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Number of responses: 42 2 6 12 14 17 18 19 22 30 0 10 20 30 40 Reduced data quality No challenges Training staff on data collection Coordination of data collection activities Training staff on data analysis Integrating results into the pavement management system Significant difference compared to manual surveys Consistency of results Validation/verification of data quality Number of Agencies Figure 21. Challenges with transitioning to APCS.
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42 Automated Data Collection and Quality Management for Pavement Condition Reporting • Other disadvantages included dependence on a single vendor (11 agencies) ; technology evaluation forcing early equipment replacement (9 agencies)
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State of the Practice 43 Additional agency-noted advantages to APCS include • Ease of use within the geographic information system (GIS) ; • More fully automated identification of crack type and severity from 3D technology and, possibly, more consistency in the annual data set and from year to year; • Provision of frequent updates of right-of-way (ROW)
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44 Automated Data Collection and Quality Management for Pavement Condition Reporting $131 per lane mile (10 agencies)
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State of the Practice 45 Survey Method Data Collection Data Analysis No. of Agencies Weighted Cost per Lane Mile ($)
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46 Automated Data Collection and Quality Management for Pavement Condition Reporting rate as a percentage of total annual lane miles collected and the estimated hours to complete the activities for acceptance of the annual pavement condition survey. Table 26 summarizes agencyreported sample sizes versus total annual lane miles surveyed.
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State of the Practice 47 testing is similar for agencies that use a combination of semi- and fully automated methods (12 agencies) compared with agencies that use only fully automated methods (10 agencies)
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48 Automated Data Collection and Quality Management for Pavement Condition Reporting • The image location is incorrect. • Semiautomated rated distresses such as patching and raveling are the biggest challenge.
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State of the Practice 49 and an agency-specific cracking index (21 agencies)
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50 Automated Data Collection and Quality Management for Pavement Condition Reporting PM2 Requirements Considering that the primary focus of PM2 was to improve and ensure the quality of pavement condition, most agency responses were in relation to development, implementation, and documentation of the components of the DQMP (e.g., quality control, calibration, staff training)
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State of the Practice 51 Other agency responses included the following: • All data collection and management is done with GIS. • An annual rater training for manual cracking is used for verification comparisons for HPMS and agency data collection needs.
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52 Automated Data Collection and Quality Management for Pavement Condition Reporting • Insufficient data initially -- mostly for the local NHS; only IRI had been collected; • Time and effort for collecting new data specific to the HPMS (e.g., percent cracking) ; • A disconnect between agency and NHS performance measures; • Agency performance measures that are significantly different from NHS performance measures with regard to percent good and percent poor; and • Separate pavement performance models based on FHWA performance measures (i.e., IRI, faulting, rutting, percent cracking)
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State of the Practice 53 • Challenge in accurate collecting of IRI data in urban areas due to speed requirements; • Level of detail required by PM2 (0.1 mi) and reporting around bridges; • Delay in collection of pavement data in 2020 due to the COVID-19 pandemic, which added challenges in completing the required NHS and HPMS pavement condition surveys; and • Reporting construction locations.
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54 Automated Data Collection and Quality Management for Pavement Condition Reporting • "Upon request, to anyone within the agency, universities for research, and private consultants." • "Design." • "Division offices." • "Highway Systems Office." • "Performance measures." • "State legislature." • "Annual pavement condition report available online." • "Pavement conditions (map and data) are freely available to everyone through a public GIS web application." To capture examples of agency pavement condition results, the research team asked the agencies whether they produce an annual report and whether the report is readily available.
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State of the Practice 55 standardization; lack of information on how agencies have successfully transitioned to an APCS; compatibility issues with historical records; an impact on performance models, cost, and time to conduct the survey; and data quality concerns. The primary benefits of transitioning from manual to an APCS included improved efficiency and safety.
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56 Automated Data Collection and Quality Management for Pavement Condition Reporting condition, 6 reported using semiautomated methods, 17 reported using a combination of semi- and fully automated methods, 3 reported using a combination of manual and semiautomated methods, and 7 reported using a combination of manual and fully automated methods. The majority of the agencies have been conducting APCS for 10 or more years (24 agencies)
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