Automated Pavement Distress Collection Techniques (2004)

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43 Agencies were queried on the costs, advantages, and disad- vantages of pavement data collected and processed through automated means. Those responses are discussed and sum- marized in this chapter. COSTS OF AUTOMATED DATA COLLECTION AND PROCESSING The reader is cautioned that the cost data provided are very limited and may be subject to significant errors owing to var- ious logistical, traffic, and geographical variables. Although the agencies were queried on the individual costs of various data items, in most cases that type of information was not available and the reported costs were combined to no more than two separate items: images and sensor data. Only two agencies were able to provide separate costs for roughness, rut- depth measurements, and joint-faulting measurements. Two others did roughness and rut-depth measurements, but no joint-faulting measurements. This is a natural consequence of the way much of the data collection is contracted. Although image collection and sensor data collection are two very dif- ferent activities, they often are done in one pass by a single vehicle, and they tend to be contracted together. Table 16 provides a summary of costs reported by the agencies (detailed questionnaire responses are given in Tables B1 through B4 in Appendix B). Note that although not tech- nically a part of this synthesis, the costs of manual surface dis- tress data collection are included for comparison purposes. Generally, costs given in Table 16 do not include agency costs of administering contracts, etc. In many cases, pavement data collection pricing is com- bined so that it is difficult to determine the costs of individ- ual data elements. This is especially true for sensor-measured items, because they typically are all collected in one pass of a data collection van. When that is the case, vendors often provide one combined price for IRI, rut depths, and joint faulting. Surface distress (cracking) data are often separated, because collecting them is an entirely different operation— although the surface imaging itself may be done at the same time that the sensor testing is done. As expected, costs of data collection are very much related to the volume of work in a given location and on how much travel is involved. The Eastern Federal Lands Division of the FHWA, for example, handles data collection for the National Park Service (NPS) on roads that are widely separated and that often involve small quantities at a given location. The combined price the division pays for imaging, image data reduction, and sensor data collection and processing ranges from $200 to $300 per mi depending on the location within the country. Because of the extreme values, Federal Lands’ data are not reflected in Table 16. Another cost variable can be traffic volume. Vermont re- ported a $20 per mi cost increase for Interstate pavements compared with others. Vermont also reported costs of up to $170 per mi in urban areas for total combined costs of image and sensor data with processing. Other agencies reporting higher than average total costs were Rhode Island at $80 per mile and the District of Columbia at $85 per mile for combined image and sensor data. An additional variable was introduced by Arkansas and Quebec, where agency personnel collect images and sensor data, but where surface distress data reduction has been done by contract. Arkansas reported $12.50 per mi paid for that service. Quebec has done only a 2002 pilot project in which they paid $45 cdn per km (approximately $50 per mi) for the service. Cost data from those two agencies are not reflected in Table 16. Finally, very few agencies could provide any costs for data collected by agency personnel. In the few cases where an attempt was made, there were notations such as “includes man hours only,” and “no equipment depreciation.” The costs of agency-collected data are not reflected in Table 16. ADVANTAGES AND DISADVANTAGES OF AUTOMATED DATA COLLECTION AND PROCESSING Although nearly one-half the agencies responding declined to comment on these issues, it is clear that personnel safety and efficiency of data collection are the primary benefits derived from automated collection. Compared with windshield or walking surveys, there is no question that automated collec- tion is much safer, because it is generally conducted at pre- vailing traffic speeds. This is such an important issue in some states, in Texas for example, that manual data collection is used on lower-volume roads, whereas automated equipment CHAPTER SEVEN COSTS, ADVANTAGES, AND DISADVANTAGES

is used on roads with higher volume. Other noteworthy com- ments made by various agencies are summarized here. Arkansas noted, “Automated distress identification allows the Department to use existing staff in other areas.” The agency also saw advantages in owning the data collection equipment and remarked, “Owning an automated roadway analyzer (ARAN) allows flexibility in scheduling and allows its use in project level and research studies.” FHWA offices responsible for pavement evaluation provided some useful feedback. The Eastern Federal Lands Division is responsible for 8800 km (5,500 mi) of paved NPS roads. They report that NPS wants 100% sampling of every paved road and that “to ensure comparable results from an objective view, we decided that automated crack detection was the only way to complete the analysis.” Roadware Group, Inc., reports that the NPS is making spe- cial use of a new ARAN with all digital images and with WiseCrax automated distress analysis and GPS coordi- nates. In addition to conducting the usual distress surveys, park managers make good use of the digital images in reviewing conditions in remote locations, saving travel time and increasing manager efficiency (39). LTPP personnel also commented, “The biggest benefit with automated (film) distress data collection is that it pro- vides a permanent record of test section condition at a par- ticular time that can be re-evaluated at a future date if needed. Safety is also a factor.” Florida commented that “the inertial profiler system pro- vides a safer, more efficient and objective way of collecting pavement evaluation data.” Minnesota found that “we get more consistent data when using automated distress data col- lection. In the past district personnel did manual ratings with much more variability. We currently have the same two peo- ple rate the system each year. Having images allows re-checks 44 when questions arise.” Oklahoma reported that automation provides much more data at a detailed level than is possible otherwise. The agency also believes that 100% coverage is beneficial and that it would not be possible for the agency to collect on its own. Vermont and Wisconsin join Minnesota in viewing data consistency as a benefit of automated collec- tion and processing. Vermont also pointed out the advantage of having images to reprocess in the event of revisions made to protocol or processing algorithms. Virginia hopes for the elimination of the human element if automated distress processing from images is imple- mented. Washington State finds that with digital images QA and QC are straightforward and routine. The agency reported that QA and QC of previously used windshield data were dif- ficult and costly. Not all comments were positive; Florida reported that “the results from real time pavement distress analysis from images [are] far from accurate.” Wyoming would like to automate surface distress collection and processing, but sees no way to handle patching and bleeding, which are difficult to do with automated processes. No agency reported doing a cost–benefit study, although Saskatchewan expects to reduce its data collection costs by approximately 75% when it changes from manual to auto- mated distress surveys and continues to use the same data- base. Caltrans reported a higher-quality product from doing its own IRI data collection. SUMMARY Costs of automated pavement condition data collection and processing vary greatly depending on specific items addressed and on logistics. Full-featured collection and processing will average more than $30 per lane-km ($50 per lane-mi) and may Automated Sensor-Collected Data Variables Visual Surveys Surface Distress Smoothness/ Roughness Rut Depth Joint Faulting Combined Sensor Data Only Combined Costs, Images, and Sensor Data Average Range No. of Agencies 16.00 5–18 3 6.12 2.23–10.00 2 1.68 1.11–2.25 2 2.23 2.23 1 12.63 5.57–27.84 6 50.02 24–85 11 Costs per mile refer to lane-miles for automated data and roadway miles for manually collected data. TABLE 16 SUMMARY OF PAVEMENT DATA COLLECTION AND PROCESSING COSTS: CONTRACT WORK ONLY (U.S. dollars per mile)

45 reach $125 per lane-km ($200 per lane-mi) or more in urban, high-traffic areas. Distance traveled to collect data is also a significant factor in determining cost. Some agencies using automated pavement condition data collection cited increased safety as a major reason for doing so. Typically, they maintain that modern traffic volumes are too hazardous for personnel to do manual surveys, and that automated surveys are both safer and more efficient. Other agencies cited shortages of resources as the main reason for automation, especially in data collection. Not all agencies are satisfied with the results of automa- tion. Some reported that improvements are needed in the quality of images provided, as well as in the data reduced from those images.