Quality Management of Pavement Condition Data Collection (2009) / Chapter Skim
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Network-level data collection involves collection of large quantities of pavement condition data, which is often converted to individual condition indices or aggregated into composite condition indices. Owing to the large quantity of required data, collection methods typically involve windshield surveys and automated methods, as these techniques can generally be performed at highway speeds without affecting traffic or posing a hazard to data collection teams.
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FIGURE 4 In-house versus contracted pavement condition data collection.
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FIGURE 6 Criteria considered to outsource pavement condition data collection.
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The acceptance plan called for LADOTD personnel to evaluate the pavement images and condition data summary to look for discrepancies and the right-of-way images for quality assurance. Other examples are presented in the case studies reviewed in chapter five.
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Location referencing problems may affect the pavement condition data quality and the decisions supported by these data. For example, poor location data may make it difficult to overlap different pavement indicators (e.g., roughness and cracking)
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These changes may cause inconsistencies from year to year. Whereas many location referencing methods can be used successfully for pavement condition data collection, it is important that they are implemented using smart business practices to ensure the quality of the collected data.
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Quality time-series of pavement condition data are needed to develop reliable deterioration models, measure the impact of maintenance and rehabilitation treatments, develop multi-year work plans, and optimize the allocation of resources. Therefore, it is important that the new and legacy data are compatible or can be made compatible through an appropriate conversion.
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NETWORK COVERAGE AND SAMPLING Another important issue that affects the quality of the pavement condition data is the network spatial and temporal coverage. Network coverage and sample size are generally controlled by the type of data desired and their intended use.
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. The implementation of mechanistic–empirical pavement analysis and design methodologies is expected to affect pavement management practices and, in particular, pavement condition data collection.
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• Network spatial and temporal coverage -- expectations for quality and quantity of pavement condition data generally vary according to the type of information required by the agency (and its intended use) , how often a particular piece of data is used, and the difficulty in obtaining that particular data.
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